Groundwater supplies a significant proportion of water use in the US and is critical to the maintenance of healthy ecosystems and environmental processes, thus characterizing aquifer hydrology is important to managing and preserving these resources. While groundwater isotopes provide insight into hydrologic and ecologic processes, their application is limited to where measurements exist. To help overcome this limitation we used the random forest algorithm to develop a predictive model for shallow groundwater isotopes in the conterminous US. Our model uses environmental variables (e.g. temperature, elevation, precipitation isotopes) as predictors. We used our model to develop the first shallow groundwater isoscape of δ 2 H and δ 18 O for the conterminous US. We describe the patterns in groundwater isotopes using both observations and our modeled isoscape. We find that throughout much of the Eastern US, groundwater isotopes are close to annual amount weighted precipitation, while groundwater isotopes are significantly depleted relative precipitation across much of the High Plains and Western US. Furthermore, we compare the observations compiled for this study to isotopes of precipitation, which allows us to determine the relative recharge efficiency (i.e. ratio of groundwater recharge to precipitation) between seasons and the proportion of annual recharge that occurs in a given season. Our findings suggest that winter recharge is generally more efficient than summer recharge; however, the dominant recharge season is more varied as it is the product of both seasonal recharge efficiency and the seasonal timing of precipitation. Parts of the central US have summer dominant recharge, which is likely the result of heavy summer precipitation/nocturnal summer precipitation. Interestingly, parts of coastal California appear to have summer dominant recharge, which we suggest could be due to recharge from fog-drip. Our results summarize spatial patterns in groundwater isotopes This article is protected by copyright. All rights reserved. This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Rivers play an important role in the global carbon cycle-acting as conduits that transport and transform organic matter (OM) as it moves from the terrestrial environment to the ocean. Some of the OM carried by rivers is ultimately converted to CO 2 and thus rivers may act as a source of CO 2 to the atmosphere. Recent estimates put CO 2 emissions from rivers at 1.8 Pg C/yr-a flux equivalent to >20% of anthropogenic emissions from the burning of fossil fuels-highlighting the importance of rivers in the global carbon cycle (Raymond et al., 2013). As OM moves through aquatic systems a host of environmental (ecosystem) conditions and intrinsic (chemical) properties may influence the degree to which the OM is mineralized

River discharges are critical for understanding hydrologic and ecological systems, yet in situ data are limited in many regions of the world. While approximating river discharge using satellite-derived water surface characteristics is possible, the key challenges are unknown channel bathymetry and roughness. Here, we present an application for merging mean river-reach characteristics and time-varying altimetry measurements to estimate river discharge for sites within the Mississippi River Basin (USA). This project leverages the Surface Water and Ocean Topography (SWOT) River Database (SWORD) for approximating mean river-reach widths and slopes and altimetry data from JASON-2/3 (2008–Present) and Sentinel-3A/B (2015–Present) obtained from the Hydroweb Theia virtual stations. River discharge is calculated using Manning’s Equation, with optimized parameters for surface roughness, bottom elevation, and channel shape determined using the Kling–Gupta Efficiency (KGE). The results of this study indicate the use of optimized characteristics return 87% of sites with KGE > −0.41, which indicates that the approach provides discharges that outperform using the mean discharge. The use of precipitation to approximate missing flows not observed by satellites results in 66% of sites with KGE > −0.41, while the use of TWSA results in 65% of sites with KGE > −0.41. Future research will focus on extending this application for all available sites in the United States, as well as trying to understand how climate and landscape factors (e.g., precipitation, temperature, soil moisture, landcover) relate to river and watershed characteristics.

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