Coastal salt marshes store large amounts of carbon but the magnitude and patterns of greenhouse gas (GHG; i.e., carbon dioxide (CO2) and methane (CH4)) fluxes are unclear. Information about GHG fluxes from these ecosystems comes from studies of sediments or at the ecosystem‐scale (eddy covariance) but fluxes from tidal creeks are unknown. We measured GHG concentrations in water, water quality, meteorological parameters, sediment CO2 efflux, ecosystem‐scale GHG fluxes, and plant phenology; all at half‐hour intervals over 1 year. Manual creek GHG flux measurements were used to calculate gas transfer velocity (k) and parameterize a model of water‐to‐atmosphere GHG fluxes. The creek was a source of GHGs to the atmosphere where tidal patterns controlled diel variability. Dissolved oxygen and wind speed were negatively correlated with creek CH4 efflux. Despite lacking a seasonal pattern, creek CO2 efflux was correlated with drivers such as turbidity across phenological phases. Overall, nighttime creek CO2 efflux (3.6 ± 0.63 μmol/m2/s) was at least 2 times higher than nighttime marsh sediment CO2 efflux (1.5 ± 1.23 μmol/m2/s). Creek CH4 efflux (17.5 ± 6.9 nmol/m2/s) was 4 times lower than ecosystem‐scale CH4 fluxes (68.1 ± 52.3 nmol/m2/s) across the year. These results suggest that tidal creeks are potential hotspots for CO2 emissions and could contribute to lateral transport of CH4 to the coastal ocean due to supersaturation of CH4 (>6,000 μmol/mol) in water. This study provides insights for modeling GHG efflux from tidal creeks and suggests that changes in tide stage overshadow water temperature in determining magnitudes of fluxes.

Large‐scale application of biochar has been promoted as a strategy for improving soil quality in agricultural and contaminated lands, as biochar has the potential to alter soil physical and biogeochemical properties. Biochar at different concentrations has been shown to have inconsistent effects on soil hydrological properties, yet the cause of the inconsistency is not well understood. To investigate the relative roles of biochar size and concentration, we mixed pure sand with a commercially available biochar varying its concentrations and particle sizes and measured saturated (Ksat) and unsaturated hydraulic conductivity and water‐retention characteristics. An increase in the concentration of fine biochar (<2 mm) consistently decreased Ksat and increased saturated moisture content. In contrast, an increase in the concentration of unsieved (mixture of coarse and fine) biochar up to 5% (by volume) increased Ksat, whereas any further increases in unsieved biochar concentration decreased Ksat. Increase in biochar concentration, irrespective of particle size, consistently decreased the unsaturated hydraulic conductivity. Measuring the changes in the characteristics of water‐retention curves of biochar–sand mixtures with biochar particle size, we showed that added biochar can either decrease (clog) or increase pore spaces in the mixture on the basis of the quantity of fine biochar fraction, which in turn could decrease or increase the hydraulic conductivity of the mixture. Thus, biochar concentration and particle size must be taken into consideration to maximize the intended hydrological benefits of biochar amendment.

Nature-based solutions for water-resource challenges require advances in the science of ecohydrology. Current understanding is limited by a shortage of observations and theories that can further our capability to synthesize complex processes across scales ranging from submillimetres to tens of kilometres. Recent developments in environmental sensing, data, and modelling have the potential to drive rapid improvements in ecohydrological understanding. After briefly reviewing advances in sensor technologies, this paper highlights how improved measurements and modelling can be applied to enhance understanding of the following ecohydrological examples: interception and canopy processes, root uptake and critical zone processes, and up-scaled effects of land use on streamflow. Novel and improved sensors will enable new questions and experiments, while machine learning and empirical methods provide additional opportunities to advance science. The synergy resulting from the convergence of these parallel developments will provide new insight into ecohydrological processes and thereby help identify nature-based solutions to address water-resource challenges in the 21st century.

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