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The loss of phosphorous (P) from the land to aquatic systems has polluted waters and threatened food production worldwide. Systematic trend analysis of P, a nonrenewable resource, has been challenging, primarily due to sparse and inconsistent historical data. Here, we leveraged intensive hydrometeorological data and the recent renaissance of deep learning approaches to fill data gaps and reconstruct temporal trends. We trained a multitask long short-term memory model for total P (TP) using data from 430 rivers across the contiguous United States (CONUS). Trend analysis of reconstructed daily records (1980–2019) shows widespread decline in concentrations, with declining, increasing, and insignificantly changing trends in 60%, 28%, and 12% of the rivers, respectively. Concentrations in urban rivers have declined the most despite rising urban population in the past decades; concentrations in agricultural rivers however have mostly increased, suggesting not-as-effective controls of nonpoint sources in agriculture lands compared to point sources in cities. TP loss, calculated as fluxes by multiplying concentration and discharge, however exhibited an overall increasing rate of 6.5% per decade at the CONUS scale over the past 40 y, largely due to increasing river discharge. Results highlight the challenge of reducing TP loss that is complicated by changing river discharge in a warming climate. 

Soil moisture data assimilation (SM-DA) is a valuable approach for enhancing streamflow prediction in rainfall-runoff models. However, most studies have focused on incorporating remotely sensed SM, and their results strongly depend on the quality of satellite products. Compared with remote sensing products, in situ observed SM data provide greater accuracy and more effectively capture temporal fluctuations in soil moisture levels. Therefore, the effectiveness of SM-DA in improving streamflow prediction remains site-specific and requires further validation. Here, we employed the Ensemble Kalman filter (EnKF) to integrate daily SM into lumped and distributed approaches of the Xinanjiang (XAJ) hydrological model to assess the importance of SM-DA in streamflow prediction. We observed a general improvement in streamflow prediction after conducting SM-DA. Specifically, the Nash-Sutcliffe efficiency increased from 0.61 to 0.65 for the lumped and from 0.62 to 0.70 for the distributed approaches. Moreover, the efficiency of SM-DA exhibits seasonal variation, with in situ SM proving particularly valuable for streamflow prediction during the wet-cold season compared to the dry-warm season. Notably, daily SM data from deep layers exhibit a stronger capability to improve streamflow prediction compared to surface SM. This indicates the significance of deep SM information for streamflow prediction in mountain areas. Overall, this study effectively demonstrates the efficacy of assimilating SM data to improve hydrological models in streamflow prediction. These findings contribute to our understanding of the connection between SM, streamflow, and hydrological connectivity in headwater catchments. 

Abstract Indigenous freshwater mussels (Unionidae) are integral to riverine ecosystems, playing a pivotal role in aquatic food webs and providing ecological services. With populations on the decline worldwide, freshwater mussels are of conservation concern. In this study, we explore the propensity of the invasive Round Goby (Neogobius melanostomus) fish to prey upon indigenous freshwater mussels. First, we conducted lab experiments where Round Gobies were given the opportunity to feed on juvenile unionid mussels and macroinvertebrates, revealing rates and preferences of consumption. Several Round Gobies consumed whole freshwater mussels during these experiments, as confirmed by mussel counts and x-ray images of the fishes. Next, we investigated Round Gobies collected from stream habitats of the French Creek watershed, which is renowned for its unique and rich aquatic biodiversity. We developed a novel DNA metabarcoding method to identify the specific species of mussels consumed by Round Goby and provide a new database of DNA gene sequences for 25 indigenous unionid mussel species. Several of the fishes sampled had consumed indigenous mussels, including the Elktoe (non-endangered), Creeper (non-endangered), Long Solid (state endangered), and Rayed Bean (federally endangered) species. The invasive Round Goby poses a growing threat to unionid mussels, including species of conservation concern. The introduction of the invasive Round Goby to freshwaters of North America is shaping ecosystem transitions within the aquatic critical zone having widespread implications for conservation and management. 

Abstract This study presents a unique data set from a laboratory experiment where we explored changes in the chemical composition of deionized water samples exposed to smoke. Inside a laboratory hood, water samples placed into a chamber were exposed to smoke for up to 60 min. The pattern of variations in hydrochemistry observed over time with increasing smoke exposure was similar in response to two different smoke treatments generated from burning tree litter. To estimate the smoke dosage and assess the consistency of replicate smoke treatments, we conducted additional experiments to evaluate changes in light transmission. Smoke inputs to the deionized water samples drove changes in hydrochemistry, with increases in acidity (with decreasing pH values), the content of organic matter (with increasing concentrations of dissolved organic carbon and dissolved organic nitrogen), and the content of inorganic N species (with increasing concentrations of ammonium, nitrate, and nitrite). The study was conducted on deionized water samples, and the results may not be directly transferrable to natural waters. Stream or lake waters that are low in ionic strength, poorly buffered, or low in acid‐neutralizing capacity might respond the most similar to the results of this study. In contrast, well‐buffered surface waters having higher acid‐neutralizing capacity would be more likely to neutralize acidic inputs from the smoke without significant effects on water quality. The publicly available dataset associated with this study will contribute to further consideration of the relative importance of short‐term changes in hydrochemistry driven by in‐stream inputs (e.g., changes in water chemistry from direct smoke deposition to the water surface) in contrast to terrestrial inputs (e.g., changes in water chemistry stemming from altered flow paths and source areas of the burned watershed landscape).