Intensively managed grasslands have been found to be either net greenhouse gas (GHG) sources or sinks depending on management and climate, where the uptake of carbon dioxide (CO2) is balanced by respiration, crop harvest, and the emission of potent non-CO2 GHGs. This study reports eddy-covariance measurements of carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4), combined with non-gaseous imports and exports of carbon to determine the net greenhouse gas balance (NGB) of a conventionally managed forage field on a dairy farm in Agassiz, British Columbia, Canada. The forage crop (ryegrass and tall fescue) was intensively managed via 'cut and carry', where the crop was harvested and removed from the field up to 6 times a year. The field received multiple applications of dairy manure slurry and was additionally fertilized with inorganic nitrogen. A previous study determined that the field was a weak or moderate source of C in terms of the net ecosystem carbon balance (NECB); this study additionally reports that the field was a GHG source during 2020 and 2021 (NGB values of 2038 +/- 890 and 901 +/- 920 g CO2-eq m-2 y-1, respectively, where the +/- term indicates the uncertainty range). Elevated N2O emissions were observed after dairy manure slurry and N-fertilizer application, and the magnitude and duration of these post-management N2O fluxes were associated with variations in near-surface soil volumetric water content. Multiple soil freezing events were associated with elevated N2O fluxes, with the magnitude of fluxes associated with freezing intensity, and were determined to be a substantial proportion of annual N2O emissions when growing season emissions were suppressed.

Medium-resolution (10-100 m) satellite evapotranspiration (ET) products are rapidly advancing agricultural water resources research and management, but underperformance across non-agricultural land cover continues to limit broader hydrologic and ecosystem applications. These inconsistencies are often linked to model structure and representation of ET dynamics across space and time. In extensive natural ecosystems such as forests and shrublands, ET is primarily governed by equilibrium radiative energy exchange, whereas in croplands it is often enhanced by advective energy inputs. While some models represent these processes, recent intercomparison studies highlight persistent performance gaps across land covers. We hypothesize that model structure governing land-atmospheric coupling, rather than sensor limitations alone, remains a primary constraint on mediumresolution ET performance. Here, we introduce a diffusivity-independent equilibrium formulation that removes the need for explicit aerodynamic conductance parameterization and conditionally incorporates aerodynamic enhancement when advection is expected. Landsat thermal and optical observations are integrated with gridded meteorological data within the presented Radiation Advection Diffusivity-independent ET (RADET) modeling framework to predict ET. Performance is evaluated using 145 in situ flux stations across the contiguous United States and intercomparisons with OpenET and MODIS products. Results indicate that RADET achieves comparable performance to leading models in croplands while providing consistent improvements across natural ecosystems, including similar to 35% lower mean absolute error and sustained positive Nash-Sutcliffe efficiency where ensemble models often showed reduced skill. Application of satellite-based equilibrium formulations with conditional transport enhancement enables computationally efficient generation of medium-resolution ET with robust crossland cover performance, advancing research and operational applications emphasized in recent mediumresolution remote sensing initiatives.

ABSTRACT The Bale Mountains are a volcanic region in south‐central Ethiopia comprising Africa's largest alpine plateau and its adjacent montane forest. The region is recognised biologically as a centre of endemism, and hydrologically as a ‘water tower’, being the source of several rivers of critical importance for East Africa. However, little formal hydrologic data exist, and land management decisions are being made based largely on a mental model that assumes high vulnerability to changes in land use and land cover. We questioned this model using remote sensing data via Google Earth Engine to map spatial and temporal patterns of key hydrologic variables over the 20‐year period spanning 2001–2020. We combined a quantitative water balance analysis with qualitative interpretation of the region's geologic and geomorphic features. Our results show that, on average, annual evapotranspiration in the forested area exceeds annual precipitation. Evapotranspiration for the forest was seen to increase throughout the long dry season, exceeding its equilibrium value, suggesting that forest vegetation is neither water‐limited nor energy‐limited, and may be subsidised by groundwater and/or soil moisture flow derived from upslope areas and thermal vents. These results confound assumed relationships among forests, wetlands, and human activity embedded in much of the region's scientific research and conservation policies. We conclude by offering a new model and set of working hypotheses from which future scientific studies and management policies can benefit.

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15-70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under-sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups.

Physics-informed machine learning techniques have emerged to tackle challenges inherent in pure machine learning (ML) approaches. One such technique, the hybrid approach, has been introduced to estimate terrestrial evapotranspiration (ET), a crucial variable linking water, energy, and carbon cycles. A key advantage of these hybrid ET models is their improved performance, particularly under extreme conditions, compared to ET estimates relying solely on ML. However, the mechanisms driving their improved performance are not well understood. To address this gap, we developed six hybrid approaches based on different physical formulations of ET and compared them with a pure ML model. All models employed the random forest algorithm and were trained on daily-scale ET observations, in-situ meteorological data and satellite remote sensing. We found a strong correlation (r = 0.93) between the sensitivity of ET estimates to machine-learned parameters and model error (root-mean-square error; RMSE), indicating that reduced sensitivity minimizes error propagation and improves performance. Notably, the most accurate hybrid model (RMSE = 17.8 W m-2 in energy unit) utilized a novel empirical parameter, which is relatively stable due to land-atmosphere equilibrium, outperforming both the pure ML model and hybrid models requiring conventional parameters (e.g., surface conductance). These results imply that conventional parameterizations may require reevaluated to effectively integrate physical models with machine learning, as conventional choices may not be optimal for this new, hybrid, paradigm. This study underscores the critical role of domain knowledge in setting up hybrid models, potentially guiding future hybrid model developments beyond ET estimation.

Understanding the energy, water, and carbon fluxes in dryland ecosystems is essential for maintaining ecosystem functioning and biodiversity. The limited in-situ measurements in drylands pose a significant challenge to the accurate monitoring and modelling of ecosystem dynamics. Satellite remote sensing provides high potential to monitor key surface and carbon variables, such as land surface temperature (LST), evapotranspiration (ET) and gross primary productivity (GPP). Although these data provide valuable insights, their temporal resolution is limited to satellite revisit overpasses, which can limit the continuity of monitoring. To address these gaps, dynamic land surface models serve as effective tools for integrating sparse remote sensing observations with continuous simulations of energy, water, and carbon cycles. The Soil-Vegetation-atmosphere Energy, water, and CO2 traNsfer (SVEN) model exemplifies this approach, offering high temporal resolution simulations that incorporate satellite-based LST and meteorological in-situ inputs. This study focuses on calibrating and validating the model in southeastern Spain, as the only sub-desertic protected area in Europe. Calibration of SVEN was achieved using a combination of MODIS remote sensing data and in-situ LST measurements from an eddy covariance system, ensuring robust parameterization tailored to local field characteristics. Furthermore, the model was validated with in situ measurements, obtained through an eddy covariance tower. The RMSE values for the land surface temperature, latent heat flux, net radiation, sensible heat flux, gross primary productivity, and soil moisture were 1.99 ºC, 25.97 W m-2, 52.71 W m-2, 50.90 W m-2, 1.44 gCm-2s-1 and 1.19 m3m-3, respectively at half-hourly time scale. Normalized root mean square deviations of the simulated values were 7.84%, 10.81%, 5.67%, 7.81%, 13.09% and 6.59%, respectively. Otherwise, it was observed that until 8 days of revisit frequency, the calibration parameters did not affect the model accuracy considerably, increasing the RMSE of variables by 0.42 to 10.53% at the half-hourly time scale. The model’s accuracy across energy, water, and carbon fluxes highlights its potential as a reliable tool for dryland monitoring, offering insights into processes that are critical for ecological management and climate adaptation strategies. By filling the temporal gap between satellite observations, this work demonstrates the value of dynamic models like SVEN in enhancing our understanding of dryland ecosystems and promoting sustainable management practices in water-limited environments. This publication is supported by the EU COST (European Cooperation in Science and Technology) Action CA22136 “Pan-European Network of Green Deal Agriculture and Forestry Earth Observation Science” (PANGEOS).

In recent decades, relative humidity (RH) over land has declined, driving increases in droughts and wildfires. Previous explanations attribute this trend to insufficient moisture advection from the ocean to sustain RH over land, but this ignores atmospheric moisture supplied from terrestrial evapotranspiration (E). While state-of-the-art climate models underestimate this RH trend, the reason behind this discrepancy remains unclear. Here, we decipher the influence of E on near-surface humidity using observations, reanalysis, and climate simulations. Global E in reanalysis has remained fairly steady in recent decades. Consequently, changes in ocean advection can reproduce observed RH declines without considering changes in E. Conversely, climate simulations estimate significant increases in E in recent decades, leading to model-based underestimation of observed RH declines. These findings suggest E intensifications may be overestimated in current climate models, thus underestimating coupled land-atmosphere drying in model output. We also highlight an upper limit of E change under observed RH trends, which could help benchmark global E trend analyses.

Dissolved organic matter (DOM) is a key variable influencing aquatic ecosystem processes. The concentration and composition of DOM in streams depend on both the delivery of DOM from terrestrial sources and on aquatic DOM production and degradation. However, there is limited understanding of the variability of stream DOM composition at continental scales and the influence of landscape characteristics and disturbances on DOM across different regions. We assessed DOM composition in 52 streams at seven research sites across six forested ecozones in Canada in 2019-2022 using 26 indices derived from five analytical approaches: absorbance and fluorescence spectroscopy, liquid chromatography-organic carbon detection, Fourier-transform ion cyclotron resonance mass spectrometry, and asymmetric flow field-flow fractionation. Combined analyses showed clear clustering and redundancy across analytical techniques, and indicated that compositional variations were primarily related to three axes of DOM composition: (a) aromaticity, which was greater in low-relief, wetland-dominated catchments, (b) oxygenation, which was greater in colder and drier ecozones, and (c) biopolymer content, which was greater in lake-influenced catchments. Variability in DOM composition among research sites was greater than variability of streams within a site and variability over time within a stream. Forest harvesting and wildfire disturbances had no common influence on DOM composition across research sites, emphasizing the need for regional studies. Our study provides a broad understanding of the variability of stream DOM composition and its associations with landscape and catchment characteristics at a subcontinental scale, and provides key insights for the choice and interpretation of DOM indices from various analytical approaches. Dissolved organic matter (DOM) in surface waters influences water quality, aquatic organisms, and carbon cycling, but variability in its composition across different regions has not been extensively studied. We collected water samples from 52 streams across 6 different forested regions in Canada spanning from coast to coast, and analyzed them using five analytical approaches varying in complexity to characterize the composition of DOM in order to assess the differences in stream DOM among the forested regions, the environmental controls on DOM composition, and which approaches were most useful in our characterization. We found that many regions had distinct DOM composition, and climatic factors like mean annual temperature, the presence of wetlands and lakes explained most of the variations, but we were unable to detect any common effects of land disturbance. For assessing differences in DOM across regions, simple analytical approaches were as useful as the more complex approaches. Our findings are important for understanding the function of aquatic ecosystems, potential impacts of climate change and land management, and implications for drinking water treatment. We analyzed dissolved organic matter (DOM) composition in samples from 52 streams across 6 forested regions in Canada using multiple analytical techniques DOM composition varied in three dimensions: aromaticity, oxygenation and biopolymer content, linked to climate, wetlands and lakes Our subcontinental-scale assessment provides insights for data interpretation, monitoring program design and land management

Dam removal is becoming more common as aging infrastructure deteriorates and people seek to restore the environmental conditions created by dams. Despite this growth in dam removals, studies analyzing effects of dam removal on meadow ecosystems are limited. Groundwater is an important and often overlooked component of hydrologic systems, particularly in the case of surface water manipulation like dam removal. A planned dam removal project at Van Norden Meadow, in the California Sierra Nevada Range at Donner Pass, was selected to quantify the impact of dam removal on shallow groundwater and the associated meadow system. Mountain meadows are inseparable from local hydrology and are a valuable resource in California’s Sierra Nevada range, providing an important source of water, supporting natural biotic communities, and serving as climate change refugia. Using MODFLOW, an open-source finite-difference groundwater modeling program, we established a baseline groundwater model for Van Norden Meadow and used it to assess potential impacts of dam removal on the subsurface water balance in relation to hydrologic niches to support plant communities. The model predicted a slight decrease of less than 0.9 m in the water table of the lower meadow below a recessional moraine, but no impact to the water table in the upper meadow. The areas adjacent to the dam along the reservoir footprint were found to be most at risk of losing wet meadow vegetation. The model also suggested dam removal led to a 3% increase in baseflow drainage below the meadow. This increase was balanced by an 8% decrease in evapotranspiration and a slight increase (0.9%) in the mobilization of stored groundwater. While no further actions following removal may be needed in the upper meadow, restoration activities, such as vegetation planting or channel filling, may be needed to offset the decrease in the water table and promote wet meadow habitats in some regions of the lower meadow. However, newly exposed areas in the reservoir footprint will promote hydric vegetation recruitment, suggesting a potential net increase in wet meadow vegetation after dam removal in Van Norden Meadow. More broadly, we found MODFLOW was suitable for modeling dam removal despite challenges with steep slopes and thin conductive layers over bedrock.

Intensively managed grasslands have been found to be either carbon (C) sources or sinks depending on management and climate. This study reports the net ecosystem production (NEP) and latent heat fluxes (7E) from a managed forage field at a dairy farm in Agassiz, British Columbia, Canada. The forage crop (ryegrass and tall fescue) was harvested up to 6 times a year. The field received multiple applications of dairy manure slurry and was also fertilized with inorganic nitrogen. Eddy-covariance measurements of NEP were combined with C imports (manure additions) and exports (harvested biomass) to determine the net ecosystem C balance (NECB), and values of gross primary production (GPP) and 7E were used to determine water use efficiency (WUE). In terms of environmental controls on NEP, variability of daytime NEP was well described by fitting measured incoming photosynthetically active radiation with a rectangular hyperbolic light-response curve, but variability in nighttime NEP was less effectively described by soil temperature and soil moisture. After accounting for C imports and exports, the NECB of the field was -315 +/- 141 and -51 +/- 148 g C m-2 y-1 (+/- indicates the uncertainty range) during the 2020 and 2021 study years, respectively, indicating C was lost from the field and was strongly influenced by C imports and exports relative to NEP. Higher than normal soil moisture and precipitation as well as higher than normal air temperature were both found to suppress GPP and ecosystem respiration (Re), but annual NEP was more impacted by soil moisture in the first year (2020) due to its effect of lowering GPP compared to high air temperature (including the 2021 Pacific Northwest heat dome) and low soil moisture in the second year due to their greater impact on Re relative to GPP. Crop harvests were found to substantially reduce both GPP and WUE which suggests that the intensity of management in terms of harvest frequency could be modified to improve long-term C sequestration.

Abstract Although evapotranspiration (ET) from the land is a key variable in Earth system models, the accurate estimation of ET based on physical principles remains challenging. Parameters used in current ET models are largely empirically based, which could be problematic under rapidly changing climatic conditions. Here, we propose a physically based ET model that estimates ET based on the surface flux equilibrium (SFE) theory and the maximum entropy production (MEP) principle. We derive an expression for aerodynamic resistance based on the MEP principle, then propose a novel ET model that integrates the SFE model and the MEP principle. The proposed model, which is referred to as the SFE‐MEP model, becomes equivalent to the MEP state in non‐equilibrium conditions when turbulent mixing is weak and the land surface is dry. Under conditions meeting land‐atmosphere equilibrium, the SFE‐MEP model is similar to ET estimation based on the SFE model. This blended nature of the SFE‐MEP ET model allows accurate ET estimation for most inland regions by overcoming the ET overestimation issue of the SFE model in dry conditions. As a result, the SFE‐MEP model significantly improves the performance of SFE ET estimation, particularly for arid regions. The proposed model and its high accuracy of ET estimation enable novel insight into various Earth system models as it does not require any empirical parameters and only uses readily obtainable meteorological variables including reference height air temperature, relative humidity, available energy, and radiometric surface temperature.

The magnitude and extent of runoff reduction, drought intensification, and dryland expansion under climate change are unclear and contentious. A primary reason is disagreement between global circulation models and current potential evaporation (PE) models for the upper limit of evaporation under warming climatic conditions. An emerging body of research suggests that current PE models including Penman-Monteith and Priestley-Taylor may overestimate future evaporation for non-water-stressed conditions. However, they are still widely used for climatic impact analysis although the underlying physical mechanisms for PE projections remain unclear. Here, we show that current PE models diverge from observed non-water-stressed evaporation across site (> 1,500 flux tower site years), watershed (> 10,000 watershed-years), and global (25 climate models) scales. By not incorporating land-atmosphere coupling processes, current models overestimate non-water-stressed evaporation and its driving factors for warmer and drier conditions. To resolve this, we introduce a land-atmosphere coupled PE model by extending the Surface Flux Equilibrium theory. The proposed PE model accurately reproduces non-water-stressed evaporation across spatiotemporal scales. We find that terrestrial PE will increase at a similar rate to ocean evaporation but much slower than rates suggested by current PE models. This finding suggests that land-atmosphere coupling moderates continental drying trends. Budyko-based runoff projections incorporating our PE model are well aligned with those from coupled climate simulations, implying that land-atmosphere coupling is key to improving predictions of climatic impacts on water resources. Our approach provides a simple and robust way to incorporate coupled land-atmosphere processes into water management tools.

Salinity in estuaries affects numerous ecological processes, and is strongly driven by river outflow interacting with the tide. We used an exponential decay function to model the relationship between salinity and freshwater flow (i.e., river discharge) to characterize and map salinity dynamics over the tidal flats of the Fraser River delta, British Columbia, Canada. The model has three parameters: the horizontal asymptote Asym that represents the average weekly salinity at very high river flows; the theoretical intercept R0 that represents salinity conditions during very low river flows; and the natural log of the rate constant lrc that reflects the relationship between river discharge into the estuary and resultant average salinity. Using data from 47 salinity-monitoring stations deployed from 2015 to 2021, we found that Fraser River discharge had a strong and inverse relationship with average weekly salinity on the outlying tidal mudflats, which is expressed by the lrc value of −8.56. Model estimates indicate that weekly salinity (on the Practical Salinity Scale) at very low discharge rates (R0) had a value of 22.4, which decreased at high discharge rates (Asym) to a value of 3.7. This predictable decrease in salinity for increasing river discharge means that the salinity regime of the Fraser River delta tidal flats is poikilohaline, and undergoes a regular and biologically relevant transition from brackish to nearly fresh water as the river outflow increases and abates seasonally. The resulting spatial gradients in salinity dynamics were associated with distance to river outflows and existing water diversion structures (i.e., jetties and causeways), meaning the timing and extent of this transition varied across the tidal flats. As estuaries become increasingly modified by coastal development and climate change, this simple model can be used to evaluate management interventions and river discharge scenarios that affect salinity gradients over tidal flats.

Common isotherm and kinetic models cannot describe the pH-dependent sorption of heavy metal cations by biochar. In this paper, we evaluated a pH-dependent, equilibrium/kinetic model for describing the sorption of cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) by poultry litter-derived biochar (PLB). We performed sorption experiments across a range of solution pH, initial metal concentration, and reaction time. The sorption of all five metals increased with increasing pH. For Cd, Cu, and Pb, kinetics experiments demonstrated that sorption rates were greater at pH 6.5 than at pH 4.5. For each metal, all sorption data were described using single set of four adjustable parameters. Sorption edge and isotherm data were well described with R2 > 0.93 in all cases. Time-dependent sorption was well described (R2 ≥ 0.90) for all metals except Pb (R2 = 0.77). We then used the best-fit model parameters to calculate linear distribution coefficients (KD) and equilibration times as a function of pH and initial solution concentration. These calculations provide a more robust way of characterizing biochar affinity for metal cations than Freundlich distribution coefficients or Langmuir sorption capacity. Because this model can characterize metal cation sorption by biochar across a wider range of reaction conditions than traditional isotherm or kinetic models, it is better suited for estimating metal cation/biochar interactions in engineered or natural systems.

Peatland rewetting, a management effort to restore water levels in previously drained peatlands, is important for re-establishing the role of these peatlands as carbon (C) sinks. Since rewetted peatlands have a highly variable response to interannual variations in climatic conditions and functional changes, long term studies of C fluxes in these ecosystems are needed. Here, we evaluated the impact of climate variability and functional change on the interannual variability of CO2 and CH4 fluxes at Burns Bog, a rewetted temperate bog on the Pacific Coast in Canada, based on five years of eddy covariance measurements. We found that the site alternated between being an annual-scale net CO2 sink or source, ranging from-32.6 +/- 21.5 (+/- 95% CI) to 11.9 +/- 15.1 g CO2-C m-2 yr-1, respectively, while consistently being a CH4 source, ranging from 11.6 +/- 0.7 to 18.0 +/- 1.6 g CH4-C m-2 yr-1. Over the five-year period, mean annual CH4 emissions (13.7 +/- 2.5 g CH4-C m-2 yr-1; +/- SD across years) entirely offset the CO2 sink (-12.3 +/- 20.4 g CO2-C m-2 yr-1), resulting in the site being near-carbon neutral over this period (1.3 +/- 23.9 g C m-2 yr-1). This finding indicates that excluding CH4 fluxes from the net C balance results in an overestimation of the net C uptake at this site. Annual CO2 emissions from the bog were greatest in the year with a dry and warm summer, emphasizing the importance of temperature and water table depth at the bog. Regardless of the greenhouse gas (GHG) metrics (i.e., global warming potential or sustained global warming potential) used in calculating the annual CO2-eq balance, the site consistently had a positive GHG balance across the study period. Despite mainly acting as a GHG source, the rewetted site will likely have a cooling effect on the climate system over long timescales compared to drained bogs that are large CO2 sources.

Small freshwater reservoirs are ubiquitous and likely play an important role in global greenhouse gas (GHG) budgets relative to their limited water surface area. However, constraining annual GHG fluxes in small freshwater reservoirs is challenging given their footprint area and spatially and temporally variable emissions. To quantify the GHG budget of a small (0.1 km) reservoir, we deployed an eddy covariance system in a small reservoir located in southwestern Virginia, USA over two years to measure carbon dioxide (CO) and methane (CH) fluxes near-continuously. Fluxes were coupled with in situ sensors measuring multiple environmental parameters. Over both years, we found the reservoir to be a large source of CO (633-731 g CO-C m yr) and CH (1.02-1.29 g CH-C m yr) to the atmosphere, with substantial sub-daily, daily, weekly, and seasonal timescales of variability. For example, fluxes were substantially greater during the summer thermally-stratified season as compared to the winter. In addition, we observed significantly greater GHG fluxes during winter intermittent ice-on conditions as compared to continuous ice-on conditions, suggesting GHG emissions from lakes and reservoirs may increase with predicted decreases in winter ice-cover. Finally, we identified several key environmental variables that may be driving reservoir GHG fluxes at multiple timescales, including, surface water temperature and thermocline depth followed by fluorescent dissolved organic matter. Overall, our novel year-round eddy covariance data from a small reservoir indicate that these freshwater ecosystems likely contribute a substantial amount of CO and CH to global GHG budgets, relative to their surface area.

To understand patterns in CO 2 partial pressure (P CO2 ) over time in wetlands’ surface water and porewater, we examined the relationship between P CO2 and land–atmosphere flux of CO 2 at the ecosystem scale at 22 Northern Hemisphere wetland sites synthesized through an open call. Sites spanned 6 major wetland types (tidal, alpine, fen, bog, marsh, and prairie pothole/karst), 7 Köppen climates, and 16 different years. Ecosystem respiration (R eco ) and gross primary production (GPP), components of vertical CO 2 flux, were compared to P CO2 , a component of lateral CO 2 flux, to determine if photosynthetic rates and soil respiration consistently influence wetland surface and porewater CO 2 concentrations across wetlands. Similar to drivers of primary productivity at the ecosystem scale, P CO2 was strongly positively correlated with air temperature (T air ) at most sites. Monthly average P CO2 tended to peak towards the middle of the year and was more strongly related to R eco than GPP. Our results suggest R eco may be related to biologically driven P CO2 in wetlands, but the relationship is site-specific and could be an artifact of differently timed seasonal cycles or other factors. Higher levels of discharge do not consistently alter the relationship between R eco and temperature normalized P CO2 . This work synthesizes relevant data and identifies key knowledge gaps in drivers of wetland respiration.

Amending soils with biochar, a pyrolyzed organic material, is an emerging practice to potentially increase plant available water and reduce the risks associated with climatic variability in traditionally‐rainfed tropical agricultural systems. To investigate the impacts of biochar amendment on soil water storage relative to non‐amended soils, we performed an upland rice field experiment in a tropical seasonally dry region of Costa Rica consisting of plots with two different biochar amendments and a control plot. Across all plots, we collected hydrometric and isotopic data (δ 18 O and δ 2 H of rain, mobile soil, ground and rice xylem water). We observed that the soil water retention curves for biochar treated soils shifted, indicating that rice plants had 2% to 7% more water available throughout the growing season relative to the control plots and thus could withstand dry spells up to seven extra days. Furthermore, the isotopic composition of plant water in biochar and control treatments were rather similar, indicating that rice plants in different treatments likely consumed similar water. Hence, we observed that biochar amendments can stabilize water supplies for the rice plants; however, still supplemental irrigation was required to facilitate plant growth during extended dry periods. Ultimately, our findings indicate, that biochar amendments can complement, but not necessarily replace, other water management strategies to help reduce the threat of rainfall variability to rainfed agriculture in tropical regions.