Improving nitrogen (N) status in European water bodies is a pressing issue. N levels depend not only on current but also past N inputs to the landscape, that have accumulated through time in legacy stores (e.g. soil, groundwater). Catchment-scale N models, that are commonly used to investigate in-stream N levels, rarely examine the magnitude and dynamics of legacy components. This study aims to gain a better understanding of the long-term fate of the N inputs and its uncertainties, using a legacy-driven N model (ELEMeNT) in Germany’s largest national river basin (Weser; 38,450 km2) over the period 1960-2015. We estimate the nine model parameters based on a progressive constraining strategy, to assess the value of different observational datasets. We demonstrate that beyond in-stream N loading, soil N content and in-stream N concentration allow to reduce the equifinality in model parameterizations. We find that more than 50% of the N surplus denitrifies (1480-2210 kg ha-1) and the stream export amounts to around 18% (410-640 kg ha-1), leaving behind as much as around 230-780 kg ha-1 of N in the (soil) source zone and 10-105 kg ha-1 in the subsurface. A sensitivity analysis reveals the importance of different factors affecting the residual uncertainties in simulated N legacies, namely hydrologic travel time, denitrification rates, a coefficient characterising the protection of organic N in source zone and N surplus input. Our study calls for proper consideration of uncertainties in N legacy characterization, and discusses possible avenues to further reduce the equifinality in water quality modelling.

With the availability of satellite-based precipitation products, it is pertinent to develop methods to use these data products for the purpose of design of hydraulic structures. The satellite precipitation products play a vital role in ungauged locations or when information is required a catchment scale. Prior to such applications, the accuracy and uncertainty associated with the products have to be investigated. This study compares Intensity Duration Frequency (IDF) curves using the recent precipitation product Global Precipitation Measurement (GPM-IMERG V6) with ground-based gauge data over the southeastern part of India and quantifies the uncertainty associated. Further, for comparison, a bias-corrected dataset is used in the study to understand the implication of bias correction of the satellite product in the IDF generation. The spatial correlation between the satellite IDF and the gauge-based IDF improves significantly after bias correction and the value is as high as 0.75 for 2- 10 year return period. The bias between the satellite IDF and gauge IDF is low in the north part of the study region and is high in the southeastern part, prone to extreme rainfall. Further, a significant percentage of the satellite-based IDFs (with and without bias correction) lie inside the confidence interval of the gauge-based data.

In recent decades, the application of Digital Elevation Models (DEMs) has been widely used in various aspects such as land management and flood planning since it reflects the actual topographic characteristic on the Earth’s surface. However, obtaining a high-quality DEM is often quite challenging because it is time-consuming, costly, and often confidential. This study presents an innovative approach to derive an improved vertical accuracy of CARTOSAT 10m DEM by blending it with publicly available SRTM (Shuttle Radar Topography Mission) DEM using machine learning methods such as Genetic Programming (GP) and Artificial Neural Networks (ANN). SRTM-1 DEM and CARTOSAT DEM in India are applied to GP and ANN to generate improved vertical accuracy high-quality DEM. The results revealed that the proposed approach improves the vertical accuracy by considering the reference as Ground control Points (GCPs) elevation from Differential Global Positioning System (DGPS) survey data. A significant improvement of 47 and 35% generated DEMs in RMSE compared to the SRTM-1 and CARTOSAT, respectively.

Coastal saltwater intrusion (SWI) is one key factor affecting the hydrology, nutrient transport, and biogeochemistry of coastal marsh landscapes. Future climate change, especially intensified sea level rise (SLR), is expected to trigger SWI to encroach coastal freshwater aquifers more intensively. Numerous studies have investigated decadal/century scale SWI under SLR by assuming a static coastal landscape topography. However, coastal marshes are highly dynamic systems in response to SLR, and the impact of coastal marsh evolution on SWI has received very little attention. Thus, this study investigated how coastal marsh evolution affects future SWI with a physically-based coastal hydro-eco-geomorphologic model, ATS (Advanced Terrestrial Simulator). Our synthetic modeling experiments showed that it is very likely that the marsh elevation increases with future SLR, and a depression zone is formed due to the different marsh accretion rates between the ocean boundary and the inland. We found that, compared to the cases without marsh evolution, the marsh accretion may significantly reduce the surface saltwater inflow at the ocean boundary, and the evolved topographic depression zone may prolong the residence time of surface ponding saltwater, which causes distinct subsurface salinity distributions. We also found that the marshland may become more sensitive to the upland groundwater table that controls the freshwater flux to the marshes, compared with the cases without marsh evolution. This study demonstrates the importance of marsh evolution to the freshwater-saltwater interaction under sea level rise and can help improve our predictive understanding of the vulnerability of the coastal freshwater system to sea level rise.

In an effort to reveal the subsurface hydraulic changes in fractures by seismic monitoring, aperture-related velocity changes need to be investigated. We developed a numerical approach for calculating changes in elastic wave velocity with fracture aperture opening by determining the internal energy of a digitized fracture model based on natural rough surfaces. The simulated local elastic energy revealed that the interaction energy converged within 1.5 mm of the mean fracture position, and was insignificant unless the fractures intersected. This energetic approach clarified the aperture–velocity relationship and reproduced the experimental results. Further calculations using digital fractures with various sizes and density demonstrated that the velocity can be accounted for by the superposition of a linear function of fracture density and quadratic function of aperture, and is insensitive to the fracture size. Although the relationship between fracture permeability and elastic wave velocity (i.e., the k-V relationship) depends on the fracture density, the offset-normalized k-V relationship shows clear linearity with the fracture density. The proposed k-V relationship as a function of the aperture and fracture density indicates that laboratory-scale fracture properties of a single fracture can be applied to multiple fractures on a larger scale. Our findings can be used to interpret temporal changes in seismic observations and to monitor fluid flow in fractures.

Infiltration processes in fractured-porous media remain a crucial, yet not very well understood component of recharge and vulnerability assessment. Under partially-saturated conditions flows in fractures, percolating fracture networks and fault zones contribute to the fastest spectrum of infiltration velocities via preferential pathways. Specifically, the partitioning dynamics at fracture intersections determine the magnitude of flow fragmentation into vertical and horizontal components and hence the bulk flow velocity and dispersion of fracture networks. In this work we derive an analytical solution for the partitioning processes based on smoothed particle hydrodynamics simulations and laboratory studies. The developed transfer function allows to efficiently simulate flow through arbitrary long wide aperture fracture networks with simple cubic structure via linear response theory and convolution of a given input signal. We derive a non-dimensional bulk flow velocity ($\widetilde{v}$) and dispersion coefficient ($\widetilde{D}$) to characterize the system in terms of dimensionless horizontal and vertical time scales $\tau_m$ and $\tau_0$. The dispersion coefficient is shown to strongly depend on the horizontal time scale and converges towards a constant value of $0.08$ within reasonable ranges for the fluid and geometrical parameters, while the non-dimensional velocity exhibits a characteristic $\widetilde{v} \sim \tau_m^{-1/2}$ scaling. Given that hydraulic information is often only available at limited places within (fractured-porous) aquifer system, such as boreholes or springs, our study intends to provide a rudimentary analytical concept to potentially reconstruct internal fracture network geometries from external boundary information, e.g., the dispersive properties of discharge (groundwater level fluctuations).

Soil contamination is mainly attributed to certain factors such as industrialization and increasing population with negative impact on natural resources such as petroleum. The petroleum industry affects the environment through oil spills with negative effect on human health and the surrounding ecosystem due to presence of Polycyclic Aromatic Hydrocarbons (PAHs) that can be carcinogenic to humans. The aim of this research is to compare the effectiveness of Chrypsopogon zizanioides also known as vetiver grass under the influence of biosurfactants and N.P.K. fertilizer in degrading and immobilizing persistent oil pollutants particularly the 16 Polycyclic Aromatic Hydrocarbons (PAHs) classified by United States Environmental Protection Agency (US EPA) as priority pollutants. The experiment was conducted in a glasshouse by growing the plant C. zizanioides in a freshly spiked crude oil contaminated soil and a weathered soil added with biosurfactant (ramphnolipids) produced by Pseudomonas aeruginosa. Similarly, all contaminated samples were amended with N.P.K fertilizer to promote the growth of C. zizanioides and microbial activities. Likewise, the assessment of the (bio) distribution of the petroleum hydrocarbons particularly the PAHs was carried out via Gas Chromatography Mass Spectrometry (GC MS). The result of this research has already indicated an improvement in plant growth and biomass in samples amended with N.P.K. fertilizer. It is also highly anticipated that the findings of this research will help in dissipating persistent contaminants such as PAHs in the crude oil contaminated soils under the influence of C. zizanioides and ramphnolipids and N.P.K. fertilizer as compared to the control samples.

Winter cover crops (CC) use water, which can have negative, neutral, or positive effects on soil water storage and supply for the subsequent summer crop. There is a lack of information on how tillage impacts water utilization by CC. A plot-scale Long-Term Agro-Ecosystem Research (LTAR) experiment was initiated in October 2018 at Stoneville, MS, to estimate crop water use and depletion (net loss) in two CC treatments [no-cover (NC) and CC (Austrian pea, Pisum sativum L.)] under two tillage [conventional tillage (CT) vs. no-tillage (NT)] practices in cotton (Gossypium hirsutum L.) and sorghum (Sorghum bicolor L.) cropping systems. Soil volumetric water content (VWC, %) was monitored using capacitance sensors at 12 soil depths to 115 cm. In-field measurement of VWC was recorded every 30 minutes. In the first year (Dec 2019–May 2020), the total VWC of the 0-45 cm soil profile was similar for all treatments until mid-March, when CC plots depleted more water than NC, especially for CT. No effect of CC on VWC was observed in the 45-115 cm profile. The CC did not affect VWC in NT plots. In the second year (Nov 2020–May 2021), NT (both CC and NC) quickly restored water depleted by cotton or sorghum (May to October 2020) in the whole 0-115 cm soil profile, whereas, for CT, VWC was not restored in the 45-115 cm depth until February. The CC-CT depleted more water in 35-55 cm depth in April and May, relative to NC-NT and CC-NT treatments. The business-as-usual CT-NC management system consumed soil water intermediate between NT systems and CC-CT during most of the measurement period. These results demonstrated that NT-CC can benefit row crop production systems with minimal use of water and supply water compared to NC for cotton and sorghum production.

In the present study, we demonstrate comprehensive three-dimensional breakthrough pollutant advection-dispersion curve predictions throughout the 3D block of a highly heterogeneous fractured aquifer, using a combination of 3D particle-following tracking (P-FT) outputs and channeling theory results, without substantial computations. The P-FT method neglects slow tracer pathways in groundwater, owing to pollutant recirculation and dead-end pathways, when applied to backbones extracted from discrete fracture networks (DFNs), providing a non-exhaustive advection and dispersion solution in a 3D DFN characterized by preferential flow path formation. The combination of proposed models was positively verified at a local scale using a benchmark DFN in a fractured limestone aquifer in Bari (Italy) to evaluate its suitability for use in larger scale simulations with comprehensive DFNs (up to 100,000 fractures). The modeling results were validated using tracer (chlorophyll-A) concentrations obtained from a well-to-well monitoring/injection test. The P-FT simulations to the DFN-extracted backbones helped to instantly generate suitable histograms of the pollutant concentration as a function of time, providing input for the 3D channeling model solution of the tracer advection-dispersion in the rock aquifer. Unlike other Lagrangian or stochastic models, which accommodate the tail of the expected concentration curve, the solution of the proposed model does not require tail improvements because, in groundwater, the advection-dispersion theory helps to explain the complete trend of pollutant spreading, including macro-scale channeling effects. In addition to the dispersion coefficients and Peclet number, the P-FT output provides information on actual 3D particle displacement, i.e., the 3D pollutant plume spreading through the studied aquifer.

HydroShare is a domain specific data and model repository operated by the Consortium of Universities for the Advancement of Hydrologic Science Inc. (CUAHSI) to advance hydrologic science by enabling individual researchers to more easily share products resulting from their research. The community platform supports, not just the scientific publication summarizing a study, but also the data, models and workflow scripts used to create the scientific publication and reproduce the results therein. HydroShare accepts data from anybody, and supports Findable, Accessible, Interoperable and Reusable (FAIR) principles. HydroShare is comprised of two sets of functionality: (1) a repository for users to share and publish data and models, collectively referred to as resources, in a variety of formats, and (2) tools (web apps) that can act on content in HydroShare and support web based access to compute capability. Together these serve as a platform for collaboration and computation that integrates data storage, organization, discovery, and analysis through web applications (web apps) and that allows researchers to employ services beyond the desktop to make data storage and manipulation more reliable and scalable, while improving their ability to collaborate and reproduce results. This presentation will describe the capabilities developed for HydroShare to support the full research data management life cycle. Data can be entered into HydroShare as soon as it is collected, and initially shared only with the team directly working on the data. As analysis proceeds, tools, scripts and models that act on the data to produce research results may be stored in HydroShare resources alongside the data. At the time of publication these resources may be permanently published and receive digital object identifiers and cited in research papers. Resources may themselves include citations to the research papers, thereby linking the publications to the supporting data, scripts and models. HydroShare design choices and capabilities for establishing relationships and versioning, based on simplicity, and ease of use, and some of the challenges encountered, will be discussed.

Wireless sensor networks are increasingly important in monitoring water quality changes. High frequency monitoring can be used to gather water quality information, identify existing problems and improve water quality management activities. However, missing data are unavoidable because of network communication issues, sensor maintenance or failure. Data interpolation is a process for constructing missing values based on known data points. Though traditional methods like polynomial or linear interpolation are widely used in sensor data pre-processing, there are still many limitations. Firstly, current interpolation methods give poor estimations when a continuous number of data within a period of time are missing. Secondly, many interpolation methods reconstruct missing data based on other parameters available at the same time step. When all the data are missing, these methods cannot be used. In our work, we are developing a sequence-to-sequence interpolation model (SIM) for recovering missing data sequences in wireless sensor networks. SIM uses the state-of-the-art sequence-to-sequence architecture. It consists of two parts: an encoder that reads from the source water quality time series data and a decoder that generates the missing data sequences. In our design, Bi-directional Long Short Term Memory Network is used as the encoder and decoder due to its capability in using both past and future information for a given time. The attention mechanism is applied to make the SIM focus on different parts of the input time series when interpolating missing values at different time steps. We evaluated the SIM by using time series data from Queensland government’s water quality monitoring network. Compared to Seasonal-ARIMA, the SIM reduced 23.2% MAE and 40.3% RMSE when recovering missing data in 2 adjacent time steps. The reason for the superior performance is that SIM interpolates missing data based on both the inner relationships between water quality parameters and the accumulated information through time.

Water managers face the daunting task of managing freshwater resources in the face of industrialization and population growth. As surface water resources become fully allocated, increased groundwater use can fill the void, particularly during periods of drought. Improper groundwater management can result in reduced water quality, land subsidence, increased pumping costs, and in some cases, the complete exhaustion of an aquifer and the loss of groundwater as a buffer during times of drought. Assessing the long-term impact of various groundwater management decisions can be difficult and costly, and therefore many decisions are made without sufficient analysis. Advancements in the acquisition and dissemination of Earth observations, coupled with advances in cloud computing, web apps, online mapping, and visualization provide a unique opportunity to deliver tools and actionable information to groundwater managers to assist them in addressing global and regional challenges and opportunities. We have developed a web-based tool that ingests in situ groundwater level measurements for specific aquifers and generates time series plots, maps, and raster animations showing groundwater depletion over time and short-term projections into the future. This process involves both temporal and spatial interpolation algorithms. In some aquifers, the observation wells are sparse and/or the historical observations have large gaps, leading to greater uncertainty in the interpolation and the resulting groundwater depletion estimates. To address this, we utilize Earth observations (GRACE, SMAP, etc.) and a co-kriging algorithm to enhance the interpolation process. The utility of the Earth observations in improving the estimates is evaluated using a jackknifing process. We present case studies for application of the system in the states of Utah and Texas.

The next generation of Earth System models promisesunprecedented predictive power through the application of improvedphysical representations, data collection, and high-performancecomputing. A key component to the accuracy, efficiency, and robustnessof the Earth System simulations is the time integration ofdifferential equations describing the physical processes. Manyexisting Earth System models are simulated using low-order,constant-stepsize time-integration methods with no error control,opening them up to being inaccurate, inefficient, or require aninfeasible amount of manual tweaking when run over multipleheterogeneous domains or scales. We have implemented the variable-stepize, variable-order differentialequation solver SUNDIALS as the time integrator within the Structurefor Unifying Multiple Modelling Alternatives (SUMMA) modelframework. The model equations in SUMMA were modified and augmented toexpress conservation of mass and enthalpy. Water and energy balanceerrors were tracked and kept below a strict tolerance. The resultingSUMMA-SUNDIALS software was successfully run in a fully automatedfashion to simulate hydrological processes on the North Americancontinent, sub-divided into over 500,000 catchments. We compared the performance of SUMMA-SUNDIALS with a version (calledSUMMA-BE) that used the backward Euler method with a fixed stepsize asthe time-integration method. We find that SUMMA-BE required two ordersof magnitude more CPU time to produce solutions of comparable accuracyto SUMMA-SUNDIALS. Solutions obtained with SUMMA-BE in a similar orshorter amount of CPU time than SUMMA-SUNDIALS often contained largediscrepancies. We conclude that sufficient accuracy, efficiency, and robustness ofnext-generation Earth System model simulations can realistically onlybe obtained through the use of adaptive solvers. Furthermore, wesuggest simulations produced with low-order, constant-stepsizesolvers deserve more scrutiny in terms of their accuracy.

Fresh Submarine Groundwater Discharge (FSGD) is an important pathway for the transport of water and materials from land to ocean, but changes in the transport may occur as snowfall decreases. This study was conducted on Japan’s mid-latitude western coast where FSDG is a quarter of the total riverine discharge and snowfall has decreased by ~50% since the 1990s. The altitude of the FSGD recharge area in 2018 has shifted 100–150 m higher than that in 2000, and the water residence time has decreased from 4-15 to 3-11 years. The pH of the groundwater dropped by 0.5, its CO (aq) concentration doubled, and nitrogen and phosphorus decreased by 30–40% and 70–80%, respectively. These changes in nutrients reduced primary productivity in coastal waters and doubled the excess dissolved inorganic carbon flux. Our evidence highlights the sensitivity of FSGD carbon flux to climate change and of the urgency of carbon-related FSGD research worldwide.

Providing environmental flows is challenging in the middle portion of the Rio Grande/Bravo Basin (between Elephant Butte Reservoir in New Mexico and Presidio, Texas) where water demand has continued to increase over time despite limited river water and dropping groundwater levels. Riparian ecosystems in this agriculture-dominated desert environment will likely become more vulnerable as competition over scarce water increases in the face of growing demand and dwindling supply. Little to no water is allocated to riparian ecosystems unless water or water rights are purchased or transferred to sustain those systems. Ongoing debates about providing environmental flows for riparian forest galleries in this water-scarce region have not been backed by quantitative modeling results of potential impacts on surface water and groundwater availability. We quantify water requirements to provide a menu of options for environmental flow allocation to establish cottonwood forest galleries. We apply hydrologic modeling under a projected warm-dry future to determine the frequency of river water availability for providing minimum environmental flows, and to evaluate water budget tradeoffs associated with environmental flow allocations in this region. Results inform water resources management decisions that support riparian habitats.

There is a concern that in the low latitudes of Jupiter’s moon Europa, the ice surface has developed meter-scale bladed roughness, which would pose a hazard to a lander. That concern was inspired by the presence of such structures (“penitents”) on Earth’s subtropical mountains, but their formation requires melting along with sublimation, which cannot occur on Europa. The troughs deepen rapidly by melting while the peaks remain dry and cold by sublimation, losing little mass, because of the 8.5-fold difference in latent heats of sublimation versus melting. Penitents cause a reduction of albedo by ~30% by trapping sunlight. The high albedo of Europa (~0.7 at visible wavelengths) therefore also argues against the existence of extreme surface roughness.

Large-scale measurements of the spatial distribution of water content in soils and snow are challenging for state-of-the-art hydrogeophysical methods. Cosmic-ray neutron sensing (CRNS) is a non-invasive technology that has the potential to bridge the scale gap between conventional in-situ sensors and remote-sensing products in both, horizontal and vertical domains. In this study we explore the feasibility and potential of estimating water content in soils and snow with neutron detectors in moving trains. Theoretical considerations quantify the stochastic measurement uncertainty as a function of water content, altitude, resolution, and detector efficiency. Numerical experiments demonstrate that the sensitivity of measured water content is almost unperturbed by train materials. And finally three distinct real world experiments provide a proof of concept on short and long-range tracks. With our results a trans-regional observational soil moisture product becomes a realistic vision within the next years.

Most research evaluating the potential of mangroves as a sink for atmospheric carbon has focused on carbon burial. However, the few studies that have quantified lateral exchange of carbon and alkalinity indicate that the dissolved carbon and alkalinity export may be several-fold more important than burial. This study aims to investigate rates and drivers of alkalinity, dissolved carbon and greenhouse gas fluxes of the mangrove-dominated Shark River estuary located in the Everglades National Park in Florida, USA. Time series and spatial surveys were conducted to assess total alkalinity (TAlk), organic alkalinity (OAlk), dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). Dominant metabolic processes driving dissolved carbon and greenhouse gas dynamics varied along the estuarine salinity gradient. Dissolved carbon and greenhouse gas concentrations were strongly coupled to porewater input, which was examined using radon-222. Shark River was a source of CO2 (92 mmol/m2/d), CH4 (60 μmol/m2/d) and N2O (2 μmol/m2/d) to the atmosphere. Dissolved carbon export (DIC = 142 mmol/m2/d, DOC = 39 mmol/m2/d) was several-fold higher than burial (~28 mmol/m2/d) and represents an additional carbon sink. Furthermore, the estuary was a source of TAlk (97 mmol/m2/d, normalised to mangrove area) to the coastal ocean, potentially buffering coastal acidification. Organic alkalinity was also exported to the coastal ocean (1.9 mmol/m2/d, normalised to mangrove area). By integrating our results with previous studies, we argue that alkalinity, dissolved carbon and greenhouse gas fluxes should be considered in future blue carbon budgets.

We introduce an algorithm “Watta”, which automatically calculates supraglacial lake bathymmetry along tracks of the ICESat-2 laser altimeter. Watta uses photon heights estimated by the ICESat-2 ATL03 product and extracts supraglacial lake surface, bottom, corrected depth and (sub)surface ice cover on a lake. These measurements are used to constrain empirical estimates of lake depth from satellite imagery, which were thus far dependent on sparse sets of in-situ measurements for calibration. Imagery sources include Landsat OLI, Sentinel-2 and high-resolution Planet Labs PlanetScope and SkySat data, used here for the first time to calculate supraglacial lake depths. The algorithm was developed and tested using a set of 46 lakes near Sermeq Kujalleq (Jakobshavn) glacier in Western Greenland. Our results suggest that the use of multiple imagery sources (both publicly-available and commercial) in combination with altimetry-based depths, can move towards capturing the evolution of supraglacial hydrology at improved spatial and temporal scales.

The dynamics and evolution of deltas and their channel networks involve interactions between many factors, including water and sediment discharge and cohesion from fine sediment and vegetation. These interactions are likely to affect how much vegetation influences deltas, because increasing sediment discharge increases aggradation rates on the delta and may result in sediment transport processes happening on timescales that are faster than those for vegetation growth. We explore how varying water and sediment discharge changes vegetation’s effect on delta evolution. We propose two new insights into delta evolution under different discharge conditions. First, without vegetation, we observe a regime shift in avulsion dynamics with increasing water discharge, from a few active channels supplemented by overbank flow and undergoing episodic avulsion (with low discharge) to many active channels experiencing frequent partial avulsions (with high discharge). Second, with vegetation, increased aggradation results in more frequent switching of the dominant channels with increased sediment discharge, but also prevents vegetation from establishing in non-dominant channels resulting in more frequent channel reoccupation and therefore greater stability in channel network planform. These insights have important implications for understanding the distribution of water, sediment, and nutrients on deltas in the face of future changes in climate, human modifications of fluxes of sediment and water to the coast, and especially for restored or engineered deltas with controlled water or sediment discharges.

Patterns of watershed nitrogen (N) retention and loss are shaped by how watershed biogeochemical processes retain, biogeochemically transform, and lose incoming atmospheric deposition of N. Loss patterns represented by concentration, discharge, and their associated stream exports are important indicators of watershed N retention patterns because they reveal hysteresis patterns (i.e. return to initial state) or one-way transition patterns (i.e. new steady state) that provide insight into watershed conditions driving long term stream trends. We examined the degree to which Continental U.S. (CONUS) scale deposition patterns (wet and dry atmospheric deposition), vegetation trends, and stream trends can be potential indicators of watershed N-saturation and retention conditions, and how watershed N retention and losses vary over space and time. By synthesizing changes and modalities in watershed nitrogen loss patterns based on stream data from 2200 U.S. watersheds over a 50 year record, our work characterized a new hysteresis conceptual model based on factors driving watershed N-retention and loss, including hydrology, atmospheric inputs, land-use, stream temperature, elevation, and vegetation. Our results show that atmospheric deposition and vegetation productivity groups that have strong positive or negative trends over time are associated with patterns of stream loss that uniquely indicate the stage of watershed N-saturation and reveal unique characteristics of watershed N-retention hysteresis patterns. In particular, regions with increasing atmospheric deposition and increasing vegetation health/biomass patterns have the highest N-retention capacity, become increasingly N-saturated over time, and are associated with the strongest declines in stream N exports—a pattern that is consistent across all land cover categories. In particular, the second largest factor explaining watershed N-retention was in-stream temperature and dissolved organic carbon concentration trends, while land-use explained the least amount of variability in watershed N-retention. Our CONUS scale investigation supports an updated hysteresis conceptual model of watershed N-retention and loss, providing great value to using long-term stream monitoring data as indicators of watershed N hysteresis patterns.

Thermodynamic calculations provide valuable insights into the reactions that drive the profound fluid transformations during serpentinization, where surface fluids are transformed into some of the most reduced and alkaline fluids on Earth. However, environmental observations usually deviate from thermodynamic predictions, especially those occurring at low temperatures where equilibrium is slowly reached. In this work, we sampled and analyzed >100 low-temperature (<40°C) fluids from the Samail ophiolite in Oman to test thermodynamic predictions with environmental observations. Additional simulations (e.g., fluid mixing, mineral leaching) were also conducted to account for deviations from equilibrium expectations. Type 1 circumneutral (pH 7 to 9) fluids result from fluid interactions with completely serpentinized rocks common in the shallow subsurface. Type 2 hyperalkaline (pH >11) fluids approach equilibrium with diopside, and serpentine and brucite actively forming during advanced stages of serpentinization. We also investigated fluids with pH values of 9 to 11 to test whether these fluids are indicative of intermediate stages of serpentinization or mixing between the above end-member fluids. Fluids at intermediate stages of serpentinization and fluids derived from mixing can have the same pH, but the former have considerably lower dissolved Si that can be attributed to concomitant subsurface serpentinization and mineral carbonation processes. Overall, this work demonstrates that predicted and measured compositions of serpentinization-derived fluids can be successfully reconciled using a combination of equilibrium and fluid-transport simulations. This work substantiates these calculations as useful tools in exploring serpentinization reactions in deep subsurface aquifers on Earth as well as those beyond our own planet.

Mountainous landscape evolution under tropical and alpine environments is mainly dictated by climatic forcing which influences underlying mechanisms of geomorphic transport (e.g., soil formation, river dynamics, slope stability and mass wasting). The time scale over which this influence acts ranges from seasonal to decennial time span. On the seasonal time scale, for accessible locations and when manpower is available, direct observations and field survey are the most useful and standard approaches. While very limited studies have been focused on the the decennial and century scale due to observational constrains. Here, we present an open and reproducible pipeline based on historical aerial images (up to 70yrs time span) that includes sensor calibration, dense matching and elevation reconstruction over two areas of interest that represent pristine examples for tropical and alpine environments: The Rempart Canyon in Reunion Island, and the Bossons glacier in the French Alps share a limited accessibility (in time and space) that can be overcome only from remote-sensing. We reach unprecedented resolution: the aero-triangulation falls at sub-metric scale based on ground truth, which is comparable to the initial images spatial sampling. This provides elevation time series with a better resolution to most recent satellite images such as Pleiades. In the case of the Rempart Canyon, we identified and quantified the results of 2 landslides that occurred in 1965 and 2001, and characterized the landslides dynamics. As for the alpine case, we highlight the effect of the temperature plateau occurred during 1939-1970 in Europe before the well known accelerated retreat during the post-industrial period. In both cases, we emphasize the strong effect of extreme events over multi-decennial to century time-scales.

Recent, record-breaking discharge in the Yenisei River, Siberia, is part of a larger trend of increasing river flow in the Arctic driven by Arctic amplification. These changes in magnitude and timing of discharge can lead to increased risk of extreme flood events, with implications for infrastructure, ecosystems, and climate. To better understand the changes taking place, it is useful to have records that help place recent hydrological changes in context. In addition to an existing network of river gauges, extreme flood events can be captured in the wood anatomical features of riparian trees, which help identify the most extreme flood events. Along the Yenisei River, Siberia we collected willow (Salix spp.) samples from a low terrace that occasionally floods when water levels are extremely high. Using these samples, we use an approach known as quantitative wood anatomy to measure variation in radial cell dimensions, including vessel area, wood fiber size and cell wall thickness. We then compare these measurements to observed records of flood stage. We hypothesize that (1) characteristic patterns of wood fiber size and cell wall thickness in Salix rings are present during flood years, (2) these patterns can be quantified by measuring wood fiber size and cell wall thickness, and (3) quantified variations in cell anatomical properties can be related to flood magnitude and duration. Understanding how riparian vegetation responds to extreme flood events can help us better manage riparian ecosystems and understand changes to the Arctic hydrological regime.