The tandem rise in satellite-based observations and computing power has changed the way we (can) see rivers across the Earth’s surface. Global datasets of river and river network characteristics at unprecedented resolutions are becoming common enough that the sheer amount of available information presents problems itself. Fully exploiting this new knowledge requires linking these geospatial datasets to each other within the context of a river network. In order to cope with this wealth of information, we are developing Veins of the Earth (VotE), a flexible system designed to synthesize knowledge about rivers and their networks into an adaptable and readily-usable form. VotE is not itself a dataset, but rather a database of relationships linking existing datasets that allows for rapid comparison and exports of river networks at arbitrary resolutions. VotE’s underlying river network (and drainage basins) is extracted from MERIT-Hydro. We link within VotE a newly-compiled dam dataset, streamflow gages from the GRDC, and published global river network datasets characterizing river widths, slopes, and intermittency. We highlight VotE’s utility with a demonstration of how vector-based river networks can be exported at any requested resolution, a global comparison of river widths from three independent datasets, and an example of computing watershed characteristics by coupling VotE to Google Earth Engine. Future efforts will focus on including real-time datasets such as SWOT river discharges and ReaLSAT reservoir areas.

Concurrent temperature and precipitation extremes during Indian summer monsoon generally have signicant effects on agriculture, society and ecosystems. Due to climate change, frequency and spatial extent of concurrent extremes have changed, and there is a need to advance our understanding in this domain. Quantication of individual extremes (temperature and precipitation) during the summer monsoon season and its teleconnections to climate indices have been studied comprehensively. But, less attention is devoted to the quantication of concurrent extremes and its teleconnections to climate indices. In this study, concurrent extremes (dry/hot and wet/cold) based on mean monthly temperature and total monthly precipitation during the Indian summer season from 1951 to 2019 over the Indian mainland are investigated. Next, the study uses wavelet coherence analysis to unravel the teleconnections of the spatial extent of concurrent extremes to climate indices (Nino 3.4, WEIO SST and SEEIO SST). Results show that the frequency of wet/hot concurrent extremes has increased signicantly, while the frequency of wet/cold concurrent has decreased for the time window 1985 to 2019 relative to 1951-1984. Also, a statistically signicant increase (decrease) in the spatial extent exists in concurrent dry/hot (wet/cold) extremes during the July, August and September months. The ndings of this study could advance our understanding of changes in concurrent extremes during the Indian summer monsoon due to climate change.

Despite the proliferation of computer-based research on hydrology and water resources, such research is typically poorly reproducible. Published studies have low reproducibility due to incomplete availability of data and computer code, and a lack of documentation of workflow processes. This leads to a lack of transparency and efficiency because existing code can neither be quality controlled nor re-used. Given the commonalities between existing process-based hydrological models in terms of their required input data and preprocessing steps, open sharing of code can lead to large efficiency gains for the modeling community. Here we present a model configuration workflow that provides full reproducibility of the resulting model instantiations in a way that separates the model-agnostic preprocessing of specific datasets from the model-specific requirements that models impose on their input files. We use this workflow to create large-domain (global, continental) and local configurations of the Structure for Unifying Multiple Modeling Alternatives (SUMMA) hydrologic model connected to the mizuRoute routing model. These examples show how a relatively complex model setup over a large domain can be organized in a reproducible and structured way that has the potential to accelerate advances in hydrologic modeling for the community as a whole. We provide a tentative blueprint of how community modeling initiatives can be built on top of workflows such as this. We term our workflow the “Community Workflows to Advance Reproducibility in Hydrologic Modeling’‘ (CWARHM; pronounced “swarm”).

Harsh subsurface environment limits robust workability of on-site instrumentation to be leveraged to track solid Earth’s dynamics. Distributed fiber-optic sensing technology (DFOS) allows long-period in-situ real-time detection of crustal geoenergy exploration-induced underground motions. Here, we first deployed 300-m-long fiber-optic cables behind casing of an actual injection well via single-ended, hybrid Brillouin-Rayleigh backscatterings interrogator to distributed monitor water injection test between two adjacent wells in onshore Mobara, Japan. Detailed DFOS recordings over the entire borehole visualized clear-cut spatiotemporal strain responses from one water injection. Potential injected water-transport footprint and impacted zone reasonably coincided with those of analogy-based strain fronts. Our study thus further uncovered that injection volume and injection pressure significantly dominated water injection-driven strain magnitude and coverage.

The shoreline development index – the ratio of a lake’s shore length to the circumference of a circle with the lake’s area – is a core metric of lake morphometry used in Earth and planetary sciences. In this paper, we demonstrate that the shoreline development index is scale-dependent and cannot be used to compare lakes with different areas. We show that large lakes will have higher shoreline development index measurements than smaller lakes of the same characteristic shape, even when mapped at the same scale. Specifically, the shoreline development index increases by about 14% for each doubling of lake area. These results call into question previously reported patterns of lake shape. We provide several suggestions to improve the application of this index, including a bias-corrected formulation for comparing lakes with different surface areas.

Free alternate bars are large-scale, downstream-migrating bedforms characterized by an alternating sequence of three-dimensional depositional fronts and scour holes that frequently develop in rivers as the result of an intrinsic instability of the erodible bed. Theoretical models based on two-dimensional shallow water and Exner equations have been successfully employed to capture the bar instability phenomenon, and to estimate bar properties such as height, wavelength and migration rate. However, the mathematical complexity of the problem hampered the understanding of the key physical mechanisms that sustain bar formation. To fill this gap, we considered a simplified version of the equations, based on neglecting the deformation of the free surface, which allows us to: (i) provide the first complete explanation of the bar formation mechanism as the result of a simple bond between variations of the water weight and flow acceleration; (ii) derive a simplified, physically based formula for predicting bar formation in a river reach, depending on channel width-to-depth ratio, Shields number and relative submergence. Comparison with an unprecedented large set of laboratory experiments reveals that our simplified formula appropriately predicts alternate bar formation in a wide range of conditions. Noteworthy, the hypothesis of negligible free surface effect also implies that bar formation is fully independent of the Froude number. We show that this intriguing property is intimately related to the three-dimensional nature of river bars, which allows for a gentle lateral deviation of the flow without significant deformation of the water surface.

Alkalinization of natural waters by the dissolution of natural or artificial minerals is a promising solution to sequester atmospheric CO$_2$ and counteract acidification. Here we address the alkalinization carbon capture efficiency (ACCE) by deriving an analytical factor that quantifies the increase in dissolved inorganic carbon in the water due to variations in alkalinity. We show that ACCE strongly depends on the water pH, with a sharp transition from minimum to maximum in a narrow interval of pH values. We also compare ACCE in surface freshwater and seawater and discuss potential bounds for ACCE in the soil water. Finally, we present two applications of ACCE. The first is a local application to 156 lakes in an acid-sensitive region, highlighting the great sensitivity of ACCE to the lake pH. The second is a global application to the surface ocean, revealing a latitudinal pattern of ACCE driven by differences in temperature and salinity.

Water retention curves (WRCs) and hydraulic conductivity functions (HCFs) are critical soil-specific characteristics necessary for modeling the movement of water in soils using the Richardson-Richards equation (RRE). Well-established laboratory measurement methods of WRCs and HCFs are not usually unsuitable for simulating field-scale soil moisture dynamics because of the scale mismatch. Hence, the inverse solution of the RRE is used to estimate WRCs and HCFs from field measured data. Here, we propose a physics-informed neural networks (PINNs) framework for the inverse solution of the RRE and the estimation of WRCs and HCFs from only volumetric water content (VWC) measurements. Unlike conventional inverse methods, the proposed framework does not need initial and boundary conditions. The PINNs consists of three linked feedforward neural networks, two of which were constrained to be monotonic functions to reflect the monotonicity of WRCs and HCFs. Alternatively, we also tested PINNs without monotonicity constraints. We trained the PINNs using synthetic VWC data with artificial noise, derived by a numerical solution of the RRE for three soil textures. The PINNs were able to reconstruct the true VWC dynamics. The monotonicity constraints prevented the PINNs from overfitting the training data. We demonstrated that the PINNs could recover the underlying WRCs and HCFs in non-parametric form, without a need for initial guess. However, the reconstructed WRCs at near-saturation–which was not fully represented in the training data–was unsatisfactory. We additionally showed that the trained PINNs could estimate soil water flux density with a broader range of estimation than the currently available methods.

Fracture networks are abundant in subsurface applications (e.g., geothermal energy production, CO2 sequestration). Fractured reservoirs often have a very complex structure, making modeling flow and transport in such networks slow and unstable. Consequently, this limits our ability to perform uncertainty quantification and increases development costs and environmental risks. This study provides an advanced methodology for simulation based on Discrete Fracture Model (DFM) approach. The preprocessing framework results in a fully conformal, uniformly distributed grid for realistic 2D fracture networks at a required level of precision. The simplified geometry and topology of the resulting network are compared with input (i.e., unchanged) data to evaluate the preprocessing influence. The resulting mesh-related parameters, such as volume distributions and orthogonality of control volume connections, are analyzed. Furthermore, changes in fluid-flow response related to preprocessing are evaluated using a high-enthalpy two-phase flow geothermal simulator. The simplified topology directly improves meshing results and, consequently, the accuracy and efficiency of numerical simulation. The main novelty of this work is the introduction of an automatic preprocessing framework allowing us to simplify the fracture network down to required level of complexity and addition of a fracture aperture correction capable of handling heterogeneous aperture distributions, low connectivity fracture networks, and sealing fractures. The graph-based framework is fully open-source and explicitly resolves small-angle intersections within the fracture network. A rigorous analysis of changes in the static and dynamic impact of the preprocessing algorithm demonstrates that explicit fracture representation can be computationally efficient, enabling their use in large-scale uncertainty quantification studies.

Changes in the magnitude and frequency of river flows have potential to alter sediment dynamics and morphology of rivers globally, but the direction of these changes remains uncertain. A lack of data across spatial and temporal scales limits understanding of river flow regimes and how changes in these regimes interact with river bank erosion and floodplain deposition. Linking characteristics of the flow regime to changes in bank erosion and floodplain deposition is necessary to understand how rivers will adjust to changes in hydrology from societal pressures and climatic change, particularly in snowmelt-dominated systems. We present a lidar dataset, intensive field surveys, aerial imagery and hydrologic analysis spanning 60 years, and spatial analysis to quantify bank erosion, lateral accretion, floodplain overbank deposition, and a floodplain fine sediment budget in an 11-km long study segment of the meandering gravel bed East River, Colorado, USA. Stepwise regression analysis of channel morphometry in nine study reaches and snowmelt-dominated annual hydrologic indices in this mountainous system suggest that sinuosity, channel width, recession slope, and flow duration are linked to lateral erosion and accretion. The duration of flow exceeding baseflow and the slope of the annual recession limb explain 59% and 91% of the variability in lateral accretion and erosion, respectively. This strong correlation between the rate of change in river flows, which occurs over days to weeks, and erosion suggests a high sensitivity of sedimentation along rivers in response to a shifting climate in snowmelt-dominated systems, which constitute the majority of rivers above 40° latitude.

Deep meteoric waters comprise a key component of the hydrologic cycle, transferring water, energy, and life between the earth's surface and deeper crustal environments, yet little is known about the nature and extent of meteoric water circulation. Using water stable isotopes, we show that maximum circulation depths of meteoric waters across North America vary considerably from 1 to 5 km, with the deepest circulation in western North America in areas of greater topographic relief. Shallower circulation occurs in sedimentary and shield-type environments with subdued topography. The amount of topographic relief available to drive regional groundwater flow and flush saline fluids is an important control on the extent of meteoric water circulation, in addition to permeability. The presence of an active flow system in the upper few kilometers of the Earth's crust and stagnant brines trapped by negative buoyancy offers a new framework for understanding deep groundwater systems.

Tropical rivers constitute a major portion of the global aquatic C flux entering the ocean, and the Rio Negro is one of the largest single C exporters with a particularly high export of terrestrial C. We investigated the role of whitesand ecosystems (WSEs) in blackwater formation in the Rio Negro basin to develop novel constraints for the terrestrial carbon export from land to the aquatic continuum. To this end, we used ultrahigh resolution mass spectrometry (FT-MS, Orbitrap) to identify markers in dissolved organic carbon (DOC) from ground- and surface waters of two contrasting WSEs feeding Rio Negro tributaries, and compared them with known Rio Negro marker from two openly available FT-MS datasets. Tributaries were fed by a whitesand riparian valley connected to terra firme plateau, and a typical upland whitesand Campina. WSE-DOC molecular composition differed by 80% from plateau DOC, which was characterized by reworked, highly unsaturated N- and S-containing molecules. WSE-DOC contained mainly condensed aromatics and polyphenols. WSE samples differed by 10% in molecular DOC composition and also by their isotopic content (14C, 18O, 2H). Upland WSE-DOC was exported by fresh precipitation and had maximum age of 13 years, being five years older than riparian valley WSE-DOC. Unexpectedly, only markers from the upland WSE, which cover a small proportion of the landscape, were identical to Negro markers. Markers of the riparian valley WSE, which are widespread and known for high DOC export, surprisingly showed lower coverage with Negro markers. Analysis of robust matching WSE markers between FT-MS datasets by Pubchem suggested well-known plant metabolites (chromenes and benzofurans) as promising candidates for targeted approaches and calibration. Our results suggest that terrestrial DOC from upland WSEs is a main source of specific blackwater molecules missing in the regional ecosystem C balance, whereas C export from the riparian valley and especially from terra firme plateaus represents mainly recycled and transformed carbon not directly affecting the ecosystem C balance. Our study highlights the potential of high-resolution techniques to constrain carbon balances of ecosystems and landscapes. Comparisons of FT-MS datasets and complementary isotopic information shows high potential to identify robust molecular markers that link forests, soils, aquifers and aquatic systems, and are needed for a deeper understanding of the regional C cycle in tropical blackwater catchments.

Large boulders with a diameter of up to several tens of meters are globally observed in mountainous bedrock channel environments. Recent theories suggest that high concentrations of boulders are associated with changes in channel morphology. However, data are scarce and ambiguous, and process-related studies are limited. Here we present data from the Liwu River, Taiwan, showing that channel width and slope increase with boulder concentration. We apply two mass balance principles of bedrock erosion and sediment transport and develop a theory to explain the steepening and widening trends. Five mechanisms are considered and compared to the field data. The cover effect by immobile boulders is found to have no influence on channel width. Channel width can partially be explained by boulder control on the tools effect and on the partitioning of the flow shear stress. However, none of the mechanisms we explored can adequately explain the scattered width data, potentially indicating a long-timescale adjustment of channel width to boulder input. Steepening can be best described by assuming a reduction of sediment transport efficiency with boulder concentration. We find that boulders represent a significant perturbation to the fluvial landscape. Channels tend to adjust to this perturbation leading to a new morphology that differs from boulder-free channels. The general approach presented here can be further expanded to explore the role of other boulder-related processes.

High quality citizen science data can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the errors in submitted observations vary, and their occurrence may depend on CS-specific characteristics. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that: (1) each CS observation is subject to a 5 specific error type, each with its own bias and noise; and (2) an observation’s error type depends on the error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and error communities, (ii) bias and noise for each error type, (iii) error distribution of each error community, and (iv) the error community to which each CS belongs. The model, applied to Nepal CS rainfall observations, 10 identifies five error types and sorts CSs into four model-inferred communities. In the case study, 73% of CSs submitted data with errors in fewer than 5% of their observations. The remaining CSs submitted data with unit, meniscus, unknown, and outlier errors. A CS’s assigned community, coupled with model-inferred error probabilities, can identify observations that require verification. With such a system, the onus of validating CS data is partially transferred from human effort to machine-learned algorithms.

Geothermal activity in the Chilean Southern Volcanic Zone is strongly controlled by the regional Liquiñe-Ofqui Fault System (LOFS) and the Andean Transverse Faults (ATF). We analyzed fifteen thermal springs in the Liquiñe area to assess the origin and the main physicochemical processes related to the LOFS and ATF. Major, minor and trace elements identify two defined clusters spatially related to the regional fault systems. In both clusters, ionic relationships suggest that the principal hydrogeochemical processes are mainly dominated by water-rock interactions. Factorial analysis provided two factors: i) F1 (65.1%), saturated by Cl, HCO, Na, SiO, Li, B and Cs, represents water-rock interaction processes driven by temperature in presence of CO; ii) F2 (28.5%) represented by SO and Mo, represents a minor water-rock interaction enhanced by the presence of HS. Samples associated to the LOFS have high scores of both factors, while those from the ATF have only high factor 1 scores. Ionic ratios compared with literature data, clearly identify the samples spatially associated to the LOFS from the ones associated to the ATF with a fuzzy pattern. Water stable isotopes values suggest a meteoric origin with small deviations from local meteoric isotopic line. CO exchange with slightly high and low temperature water rocks interaction is present in most of the samples. Our results indicate that groundwater circulation along faults is a complex process where different constraints influence the final hydeogeochemistry and reaction intensity. Finally, the established processes at Liquiñe area are not upscalable at the whole Southern Volcanic Zone.

Machine learning (ML) based models have demonstrated very strong predictive capabilities for hydrologic modeling, but are often criticized for being black-boxes. In this paper we use a technique from the field of explainable AI (XAI), called layerwise relevance propagation (LRP) to “open the black box”. Specifically we train a deep neural network on data from a set of hydroclimatically diverse FluxNet sites to predict turbulent heat fluxes, and then use the LRP technique to analyze what it learned. We show that the neural network learns physically plausible relationships, including different ways of partitioning the turbulent heat fluxes according to moisture or energy limiting characteristics of the sites. That is, the neural network learns different behaviors at arid and non-arid sites. We also develop and demonstrate a novel technique that uses the output of the LRP analysis to explore how the neural network learned to regionalize between sites. We find that the neural network primarily learned behaviors that differed between evergreen forested sites and all other vegetation classes. Our analysis shows that even simple neural networks can extract physically-plausible relationships and that by using XAI methods we can learn new information from the ML-based methods.

Hydroperiod, or the amount of time a lentic waterbody contains water, shapes communities of aquatic organisms. Precise measurement of hydroperiod features such as inundation timing and duration can help predict community dynamics and ecosystem stability. In areas defined by high spatial and temporal variability, fine-scale temporal variation in inundation timing and duration may drive community structure, but that variation may not be captured using common approaches including remote sensing technology. Here, we provide methods to accurately capture inundation timing by fitting hidden Markov models to measurements of daily temperature standard deviation collected from temperature loggers. We describe a rugged housing design to protect loggers from physical damage and apply our methods to a group of intermittent ponds in southeastern Arizona, showing that initial pond inundation timing is highly variable across a small geographic scale (~50km2). We also compare a 1-logger (pond only) and 2-logger (pond + control) design and show that, although a single logger may be sufficient to capture inundation timing in most cases, a 2-logger design can increase confidence in results. These methods are cost-effective and show promise in capturing variation in intraregional inundation timing that may have profound effects on aquatic communities, with implications for how these communities may respond to hydroperiod alteration from a changing climate.

Open source in-situ environmental sensor hardware continues to expand across the geosphere to a variety of applications. These systems typically perform three fundamental tasks: sample sensors at a specified time or period, save data onto retrievable media, switch power to components on and off in between sample cycles to conserve battery energy and increase field operation time. This is commonly accomplished through integrating separate off-the-shelf components into the desired system such as: power relays, SD card hardware, Real-Time Clocks (RTCs), and coin cell batteries. To enable faster prototyping, the Openly Published Environmental Sensing Lab abstracted all of these requirements into a single PCB that can be dropped into any project to achieve these commonly-required capabilities. The hardware is laid out in a “Feather” form factor, a popular configuration in the open-source hardware community, to easily mate with other industry standard products. The onboard RTC acts as an alarm clock that wakes a user-attached micro controller from low-power sleep modes in between sample cycles. By integrating all these components into a single PCB, we save cost while significantly reducing physical system size. The design as well as a suite of code functions that enable the user to configure all the Hypnos board features are detailed. For more information, please visit open-sensing.org/projects.

Time-lapse gravimetry (repeat microgravity measurement) is a powerful tool for monitoring temporal mass distribution variations, including seasonal and long-term groundwater storage changes (GWSC). This geophysical method for measuring changes in gravity (Δg) is potentially applicable to any groundwater system. Here, I present Gravi4GW, a Python tool for the site-adapted calculation of β, the conversion factor between Δg and GWSC (also known as “topographic admittance”). Alpine catchments, in particular, are ideal target sites as they are highly sensitive to climate variations and can experience significant GWSC, while often lacking groundwater monitoring infrastructure. Therefore, to illustrate the usage of Gravi4GW, I investigate a detailed example of an alpine catchment and examine spatial variations and the effects of depth assumptions. This novel and accessible tool is designed to be useful in both the planning and data processing stages of time-lapse gravimetric field studies.

In its three-dimensional (3-D) characterization, drought is approached as an event whose spatial extent changes over time. Each drought event has an onset and end time, a location, a magnitude, and a spatial trajectory. These characteristics help to analyze and describe how drought develops in space and time, i.e., drought dynamics. Methodologies for 3-D characterization of drought include a 3-D clustering technique to extract the drought events from the hydrometeorological data. The application of the clustering method yields small ‘artifact’ droughts. These small clusters are removed from the analysis with the use of a cluster size filter. However, according to the literature, the filter parameters are usually set arbitrarily, so this study concentrated on a method to calculate the optimal cluster size filter for the 3-D characterization of drought. The effect of different drought indicator thresholds to calculate drought is also analyzed. The approach was tested in South America with data from the Latin American Flood and Drought Monitor (LAFDM) for 1950–2017. Analysis of the spatial trajectories and characteristics of the most extreme droughts is also included. Calculated droughts are compared with information reported at a country scale and a reasonably good match is found.

Conceptual hydrological models imply a simplification of the complexity of the hydrological system; however, it lacks the flexibility in reproducing a wide range of the catchment responses. Usually, a trade-off is done to sacrifice the accuracy of a specific aspect of the system behavior in favor of the accuracy of other aspects. This study evaluates the benefit of using a modular approach, “The fuzzy committee model” of building specialized models (same structure associated with different parameter realization) to reproduce specific responses (high and low flow response) of the catchment. The study also assesses the applicability of using predicted runoff from both specialized models with certain weights based on a fuzzy membership function to form a fuzzy committee model. This research continues to explore the fuzzy committee models first presented by [Solomatine, 2006] and further developed by [Fenicia et al., 2007; Kayastha et al., 2013]. In this paper, weighting schemes with power parameter values are investigated. A thorough study is conducted on the relation between the fuzzy committee variables (the membership functions and the weighting schemes), and their effect on the model performance. Furthermore, the Fuzzy committee concept is applied on a conceptual distributed model with two cases, the first with lumped catchment parameters and the latter with distributed parameters. A comparison between different combinations of the fuzzy committee variables showed the superiority of all Fuzzy Committee models over single models. Fuzzy committee of distributed models performed well, especially in capturing the highest peak in the calibration data set; however, it needs further study of the effect of model parameterization on the model performance and uncertainty.

Geologic carbon storage is required for achieving negative CO2 emissions to deal with the climate crisis. The classical concept of CO2 storage consists in injecting CO2 in geological formations at depths greater than 800 m, where CO2 becomes a dense fluid, minimizing storage volume. Yet, CO2 has a density lower than the resident brine and tends to float, challenging the widespread deployment of geologic carbon storage. Here, we propose for the first time to store CO2 in supercritical reservoirs to reduce the buoyancy-driven leakage risk. Supercritical reservoirs are found at drilling-reachable depth in volcanic areas, where high pressure (p>21.8 MPa) and temperature (T>374 ºC) imply CO2 is denser than water. We estimate that a CO2 storage capacity in the range of 50-500 Mt yr-1 could be achieved for every 100 injection wells. Carbon storage in supercritical reservoirs is an appealing alternative to the traditional approach.

Rock texture has a critical influence on the way rocks weather. The most important textural factors affecting weathering are grain size and the presence of cracks and stylolites. These discontinuities operate as planes of mechanical weakness at which chemical weathering is enhanced. However, it is unclear how different rock textures impact weathering rates and the size of weathered grains. Here, we use a numerical model to simulate weathering of rocks possessing grain boundaries, cracks, and stylolites. We ran simulations with either synthetic or natural patterns of discontinuities. We found that for all patterns, weathering rates increase with discontinuity density. When the density was <~25%, the weathering rate of synthetic patterns followed the order: grid >honeycomb >Voronoi >brick-wall. For higher values, all weathering rates were similar. We also found that weathering rates decreased as the tortuosity of the pattern increased. Moreover, we show that textural patterns strongly impact the size distributions of detached grains. Rocks with an initial monomodal grain size distribution produce weathered fragments that are normally distributed. In contrast, rocks with an initial log-normal size distribution produce weathered grains that are log-normally distributed. For the natural patterns, weathering produced lower modality distributions.

Cold temperatures limit nitrate-N load reductions of woodchip bioreactors in higher-latitude climates. This two-year, on-farm (Willmar, Minnesota, USA) study was conducted to determine whether field-scale nitrate-N removal of woodchip bioreactors can be improved by the addition of cold-adapted, locally isolated bacterial denitrifying strains (bioaugmentation) or dosing with a carbon (C) source (biostimulation). In Spring 2017, biostimulation removed 66% of the nitrate-N load, compared to 21% and 18% for bioaugmentation and control, respectively. The biostimulation nitrate-N removal rate (NRR) was also significantly greater, 15.0 g N m-1 d-1, versus 5.8 and 4.4 g N m-1 d-1, for bioaugmentation and control, respectively. Bioclogging of the biostimulation beds limited dosing for the remainder of the experiment; NRR was greater for biostimulation in Fall 2017, but in Spring 2018 there were no differences among treatments. Carbon dosing did not increase outflow dissolved organic C concentration. The abundance of one of the inoculated strains, Cellulomonas sp. strain WB94, increased over time, while another, Microvirgula aerodenitrificans strain BE2.4, increased briefly, returning to background levels after 42 days. Eleven days after inoculation in Spring 2017, outflow nitrate-N concentrations of bioaugmentation were sporadically reduced compared to the control for two weeks but were insignificant over the study period. The study suggests that biostimulation and bioaugmentation are promising technologies to enhance nitrate removal during cold conditions. A means of controlling bioclogging is needed for biostimulation, and improved means of inoculation and maintaining abundance of introduced strains is needed for bioaugmentation. In conclusion, biostimulation showed greater potential than bioaugmentation for increasing nitrate removal in a woodchip bioreactor, whereas both methods need improvement before implementation at the field scale.