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Abstract Small estuaries link uplands and nearshore marine environments but are understudied in many regions. We quantified spatial and temporal variability in CH₄, CO₂, nutrients, and organic matter in a small, deep, bar‐built tropical estuary in Fajardo, Puerto Rico. The estuary was typically impounded and strongly stratified at all three sampling locations. Across 5 sampling dates, the pycnocline was sharp, with salinity of 0 in the upper meter and up to 17 ppt at 3–5 m. Methane concentrations at the surface were extraordinarily high for a mangrove‐fringed tropical estuary, averaging from 26 to 59 μM at three stations. Surface methane flux was 17.1 mM/m²/day. Methane concentrations were high throughout the water column, declined temporarily following estuarine flushing that increased salinity, and were correlated with CO₂(r² = 0.48,p < 0.01). Small bar‐built estuaries are common in Puerto Rico and other high‐wave energy environments and should be included in assessments of greenhouse gas emissions. 

ABSTRACT Methane (CH4) accounts for about one-third of the current anthropogenic greenhouse gas-driven warming. Rivers, particularly those draining human-impacted landscapes, are important sources of CH4 to the atmosphere. Yet, effective mitigation of this important flux remains elusive due to the lack of accurate quantification of human-induced emissions and a poor understanding of its key aquatic drivers. Here, we demonstrate that CH4 emission rates from global nutrient-enriched rivers in human-impacted regions more than double (2.5-fold) those from pristine rivers. Importantly, a strong association is identified between CH4 flux and concentrations of total phosphorus and ammonium nitrogen in these systems. This relationship is mediated by increased autochthonous and exogenous supplies of labile organic substrates, enhanced methanogen abundance and diversity, and extended anoxia in nutrient-enriched rivers. Quantitative modeling incorporating nutrient effects estimates global CH4 emissions from nutrient-enriched rivers at 9.7 ± 1.1 Tg CH4 yr−1, of which 22%–49% could be mitigated by reducing current nutrient concentrations to levels constrained by sustainable development goals, half of their current levels, or pristine conditions. These findings highlight the substantial potential for effective mitigation of riverine CH4 emissions via coordinated riverine nutrient management at the global scale. 

Abstract: Water samples were collected from coastal streams and lagoons in Puerto Rico from February 2021 to March 2024 as part of ongoing coastal ecosystem monitoring. These samples were analyzed at the University of New Hampshire Water Quality Analysis Laboratory for comprehensive water chemistry including field parameters, major ions, nutrients, dissolved gases, and trace metals. Sampling sites included Quebrada Fajardo (QFJO) at multiple depths and distances upstream, Quebrada Pitahaya (Qpaya), Laguna Pitahaya (LPYHA), Luquillo streams (LUQA, LUQB), and Laguna Cartagena (LCART). Field measurements included pH, conductivity, dissolved oxygen, temperature, turbidity, and atmospheric pressure. Laboratory analyses encompassed dissolved organic carbon (DOC), total dissolved nitrogen (TDN), nutrients (NH4-N, PO4-P, NO3-N), major ions (Cl, SO4, Na, K, Mg, Ca), dissolved gases (CH4, CO2, N2O), and trace metals by Inductively Coupled Plasma (ICP) analysis. The ICP analysis provided enhanced detection capabilities for cations and metals including calcium, iron, manganese, silicon, strontium, sulfur, sodium, magnesium and potassium. Samples were collected as grab samples from the water surface. All samples were filtered through pre-combusted Whatman GF/F for nutrients and organic matter and Whatman WCN Cellulose Nitrate Membranes for metals. Values below detection limits are recorded as 1/2 the detection limit. This dataset provides comprehensive water quality data for Puerto Rican coastal watersheds, with particular focus on stratified sampling in Quebrada Fajardo to understand vertical water column structure and biogeochemical processes in coastal environments influenced by both terrestrial and marine inputs. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support was provided by the USDA Forest Service International Institute of Tropical Forestry and the University of Puerto Rico. 

Machine‐learning models have been surprisingly successful at predicting stream solute concentrations, even for solutes without dedicated sensors. It would be extremely valuable if these models could predict solute concentrations in streams beyond the one in which they were trained. We assessed the generalizability of random forest models by training them in one or more streams and testing them in another. Models were made using grab sample and sensor data from 10 New Hampshire streams and rivers. As observed in previous studies, models trained in one stream were capable of accurately predicting solute concentrations in that stream. However, models trained on one stream produced inaccurate predictions of solute concentrations in other streams, with the exception of solutes measured by dedicated sensors (i.e., nitrate and dissolved organic carbon). Using data from multiple watersheds improved model results, but model performance was still worse than using the mean of the training dataset (Nash–Sutcliffe Efficiency < 0). Our results demonstrate that machine‐learning models thus far reliably predict solute concentrations only where trained, as differences in solute concentration patterns and sensor‐solute relationships limit their broader applicability. 

The pace and trajectory of ecosystem development are governed by the availability and cycling of limiting nutrients, and anthropogenic disturbances such as acid rain and deforestation alter these trajectories by removing substantial quantities of nutrients via titration or harvest. Here, we use six decades of continuous chemical and hydrologic data from three adjacent headwater catchments in the Hubbard Brook Experimental Forest, New Hampshire—one deforested (W5), one CaSiO3-enriched (W1), and one reference (W6)—to quantify long-term nutrient and mineral fluxes. Acid deposition since 1900 drove pronounced depletion and export of base cations, particularly calcium, across all watersheds. Experimental deforestation of W5 intensified loss of biomass and nutrient cations and triggered sustained increases in streamwater pH, Ca2+, and SiO2 exports over nearly four decades, greatly exceeding the effects of direct CaSiO3 enrichment in both duration and magnitude. We detect no long-term changes in water yield or water flow paths in the experimental watersheds, and we attribute this multidecadal increase in weathering rates following deforestation to biological responses to severe nutrient limitation. Our evidence suggests that in the regrowing forest, plants are investing photosynthate into belowground processes that amplify mineral weathering to access phosphorus and micronutrients, consequently elevating the export of less limiting elements present in silicate parent material. Throughout decades of forest regrowth, enhanced biotic weathering has continued to deplete the acid buffering capacity of the terrestrial ecosystem while the export of weathering products has elevated the pH of the receiving stream. 

Abstract: The Lamprey River watershed, located in southeastern New Hampshire (USA), drains 554 km² of low elevation land before discharging into the Great Bay Estuary (Wymore and others 2021). The watershed is classified as suburban with mixed land-use that includes forests (73%), wetlands (10%), development (7%), and agriculture (NOAA Coastal Change Analysis Program 2016). We selected four distinct sites within the watershed to capture the dynamics of both tributaries and the mainstem, while accounting for variations in land use, land cover, and nutrient availability. Wednesday Hill Brook (WHB) is a 1st-order stream that drains a residential landscape and has the highest NO3 concentrations among the sites due to a high density of septic systems (Flint and McDowell 2015). Dowst Cate Forest (DCF) is a 2nd-order stream draining a headwater wetland and forested landscape and is characterized by the highest concentrations of dissolved organic carbon (DOC). The two main stem Lamprey River sites, LMP72 and LMP73, exhibit moderate concentrations of both DOC and NO3 and are located approximately 1 km apart. LMP72 is located at a low-head run-of-river dam while LMP73 is free-flowing water downstream of the reservoir. Our dataset includes weekly water chemistry and dissolved gas data collected from April 2014 through May 2023, except for DCF where data collection ended in 2021, resulting in a total of 1,179 observations across the four different sites.