The Smart Rock is a submersible sensor suite that monitors temperature, pressure (water depth), turbidity, and electrical conductivity. The sensor suite can be deployed in streams for 3-6 months at a time taking data every 20 minutes. The frequency of data collection can be changed which will affect battery life. Smart Rock assembly has been streamlined to make assembly and programming as accessible as possible. We want water sensing to be affordable and accessible to water scientists around the world. Our goal with the Smart Rock remains to develop an affordable, user friendly, low cost, and accessible for our different users needs. For those familiar with previous versions of the Smart Rock let me cover what has changed with this latest revision. We have designed our own electrical conductivity sensor with increased control for the user to change the range and resolution of the sensor. The enclosure has shrunk with a new selection of batteries doubling the capacity of the previous version’s battery. We worked on stabilizing the code and simplifying the operation. You can now control the unit’s settings via a config file on the SD card. No more Arduino reprogramming to change settings.

Krypton-81 dating provides new insights into the timing, mechanisms, and extent of meteoric flushing versus retention of saline fluids in the subsurface in response to changes in geologic and/or climatic forcings over 50 ka to 1.2 Ma year timescales. Remnant Paleozoic seawater-derived brines (2-2.5 km depth) associated with evaporites in the Paradox Basin, Colorado Plateau, are beyond the 81Kr dating range (>1.2 Ma) and have likely been preserved due to negative fluid buoyancy and low permeability. 81Kr dating of formation waters above the evaporites indicates topographically-driven meteoric recharge (0.03-0.8 Ma) and salt dissolution since the Late Pleistocene. Formation waters below the evaporites, in basal aquifers, contain relatively young meteoric water components (0.4-1.1 Ma based on 81Kr) that partially flushed remnant brines and dissolved evaporites. We demonstrate that recent, rapid denudation of the Colorado Plateau (<4-10 Ma) activated deep, basinal-scale flow systems as recorded in 81Kr groundwater age distributions.

Emerging parametric insurance products targeted at regional governments consider an index of flooding as the instrument for payoff and rate setting. Inundation extent from satellite remote sensing may provide a more direct measure of flood risk in this context than hydraulic modeling of flow and inundation. Here, we examine satellite-based fractional inundated area as a proxy for flood impact that can be used for index insurance payment at a regional scale. Typical methods for estimating return periods from unbounded distributions such as the GEV (generalized extreme value distribution) are not appropriate for fractional flooded area, which is bounded by 0 and 1. Here we examine alternative bounded distributions (2 parameter and a 4 parameter Beta) to estimate return periods and quantify uncertainty using a bootstrap sampling procedure for the short duration satellite record of fractional flooded area. We consider two examples with distinct flood dynamics i) a country (Bangladesh) where a flood can cover the majority of the land surface, and ii) a river basin (the Rio Salado basin in Argentina) where the worst flood covered only a modest fraction of the watershed. We explore how a parametric insurance policy based on fractional flooded area could be priced based on a typical approach used in the industry, that accounts for uncertainty for small sample estimation. Our exploratory approach to model selection illustrates how estimating the uncertainty price influences insurance contract pricing and is important to consider the choice of distribution beyond just the traditional measures of goodness of fit.

Notwithstanding the large number of studies on bedforms such as dunes and antidunes, performing quantitative predictions of bedform type and geometry remains an open problem. Here we present the results of laboratory experiments specifically designed to study how sediment supply and caliber may impact equilibrium bedform type and geometry in the upper regime. Experiments were performed in a sediment feed flume with flow rates varying between 5 l/s and 30 l/s, sand supply rates varying between 0.6 kg/min and 20 kg/min, uniform and non-uniform sediment grain sizes with geometric mean diameter varying between 0.22 mm and 0.87 mm. The experimental data and the comparison with datasets available in the literature revealed that the ratio of the volume transport of sediment to the volume transport of water Qs/Qw plays a prime control on the equilibrium bed configuration. The equilibrium bed configuration transitions from washed out dunes (lower regime), to downstream migrating antidunes (upper regime) for Qs/Qw between 0.0003 and 0.0007. For values of Qs/Qw greater than those typical of downstream migrating antidunes, the bedform wavelength increases with Qs/Qw. At these high values of Qs/Qw equilibrium bed configurations with fine sand are characterized by upstream migrating antidunes or cyclic steps, and significant suspended load. In experiments with coarse sand, equilibrium is characterized by plane bed with bedload transport in sheet flow mode. Standing waves form at the transition between downstream migrating antidunes and bed configurations with upstream migrating bedforms.

The usefulness of satellite multi-sensor precipitation (SMP) and other satellite-informed precipitation products in water resources modeling can be hindered by substantial errors which vary considerably with spatiotemporal scale. One approach to cope with these errors is by combining SMPs with ensemble generation methods, such that each ensemble member reflects one plausible realization of the true—but unknown—precipitation. This requires replicating the spatiotemporal autocorrelation structure of SMP errors. The climatology of this structure is unknown for most locations due to a lack of ground reference observations, while the unique anisotropy and nonstationarity within any particular precipitation system limit the relevance of this climataology to the depiction of error in individual storm systems. Characterizing and simulating this autocorrelation across spatiotemporal scales has thus been called a grand challenge within the precipitation community. We introduce the Space-Time Rainfall Error and Autocorrelation Model (STREAM), which combines anisotropic and nonstationary SMP spatiotemporal correlation structures with a pixel-scale precipitation error model to stochastically generate ensemble precipitation fields that resemble “ground truth” precipitation. We generate STREAM precipitation ensembles at high resolution (1-hour, 0.1˚) with minimal reliance on ground-reference data, and evaluate these ensembles at multiple scales. STREAM ensembles consistently “bracket” ground-truth observations and replicate the autocorrelation structure of ground-truth precipitation fields. STREAM is compatible with pixel-scale error/uncertainty formulations beyond those presented here, and could be applied globally to other precipitation sources such as numerical weather predictions or “blended” products. In combination with recent work in SMP uncertainty characterization, STREAM could be run without any ground data.

Flooding is one of the most devastating natural hazards to which our society worldwide must adapt, especially as its severity and occurrence tend to increase with climate changes. This research work focuses on the assimilation of 2D flood observations derived from remote-sensing images acquired during overflowing events. To do so, the resulting binary wet/dry maps are expressed in terms of wet surface ratios (WSR) over a number of floodplain subdomains. This ratio is assimilated jointly with in-situ water-level gauge observations to improve the flow dynamics within the floodplain. An Ensemble Kalman Filter with a dual state-parameter analysis approach is implemented on top of a TELEMAC-2D hydrodynamic model. The EnKF control vector is composed of spatially-distributed friction coefficients and a corrective parameter of the inflow discharge. It is extended with the hydraulic states within the floodplain subdomains. This data assimilation strategy was validated and evaluated over a reach of the Garonne river. The observation operator associated with the WSR observations, as well as the dual state-parameter sequential correction, was first validated in the context of Observing System Simulation Experiments. It was then applied to two real flood events that occurred in 2019 and 2021. The merits of assimilating SAR-derived WSR observations, in complement to the in-situ water-level observations, are shown in the parameter and observation spaces with assessment metrics computed over the entire flood events. It is also shown that the hydraulic state correction within the dual state-parameter analysis approach significantly improves the flood dynamics, especially during the flood recession.

Using web standards including Schema.org and JSON-LD, the GeoCODES project extends Schema.org with Project 418's geoscience specific vocabulary. By embedding properly formatted and populated JSON-LD files in web sites serving geolocated datasets, search engines such as Google and Bing are able to parse and index these data sets and then to provide information concerning these datasets via standard web search tools. Due to the difficult nature of properly formatting and populating these JSON-LD structures, the GeoCODES User Interface was created to guide data providers through the process of describing the data and validating the descriptions against standard vocabularies. The result is user friendly and easily extensible web based, mobile device ready tool for automatically generating JSON-LD metadata for organizations and datasets. This ultimately allows the original data to be found and used by both scientists and the public.

Many of the world’s major aquifers are under severe stress as a result of intensive pumping in support of irrigated agriculture. The question of what the future holds for these aquifers and the agricultural production they support is of paramount importance in a world of burgeoning populations, dietary shifts, and climate change. Addressing that question requires a better understanding of the how and why of a particular aquifer’s response to pumping. One important, but largely underutilized, source of information is the data from monitoring well networks that provide near-continuous records of water levels through time. Although many regions have such networks operated by local, state, or Federal entities, the vast majority of efforts are, by fiscal necessity, focused on keeping the networks up and running. Little, if any, time is spent on interpreting the acquired hydrographs. The index well network in the High Plains aquifer (HPA) in central and western Kansas is an exception, as hydrograph interpretation is an important program emphasis. Examination of multiyear hydrographs has resulted in the development of profound insights concerning, for example, the frequency of episodic recharge, the magnitude and variability of net inflow, characteristics of the monitored aquifer (continuity, hydraulic regime, etc.), and the impact of extreme meteorological events. These insights have allowed us to develop a significantly better understanding of how the aquifer will respond to proposed management actions; such an understanding is critical for charting more sustainable paths for aquifers across the globe. We will demonstrate these points through an examination of two multiyear hydrographs from the HPA in western Kansas with an emphasis on the insights that shed light on the prospects for the sustainability of this heavily stressed system and the agricultural production that it supports.

Recent advancement of computational linguistics, machine learning, including a variety of toolboxes for Natural Language Processing (NLP), help facilitate analysis of vast electronic corpuses for a multitude of objectives. Research papers published as electronic text files in different journals offer windows into trending topics and developments, and NLP allows us to extract information and insight about these trends. This project applies Latent Dirichlet Allocation (LDA) Topic Modeling for bibliometric analyses of all abstracts in selected high-impact (Impact Factor > 0.9) journals in hydrology. Topic modeling uses statistical algorithms to extract semantic information from a collection of texts and has become an emerging quantitative method to assess substantial textual data. The resulting generated topics are interpretable based on our prior knowledge of hydrology and related sub-disciplines. Comparative topic trend, term, and document level cluster analyses based on different time periods was performed. These analyses revealed topics such as climate change research gaining popularity in Hydrology over the last decade. An inter-topic correlation analysis also revealed the nature of information exchange and absorption between various communities within the hydrology domain. The primary objective of this work is to allow researchers to explore new branches and connections in the Hydrology literature, and to facilitate comprehensive and inclusive literature reviews. We aim to use these results combined with probability distribution between topics, journals and authors to create an ontology that is useful for scientists and environmental consultants for exploring relevant literature based on topics and topic relationships.

A physically based model is formulated for the active layer depth of permafrost under changing boundary condition instead of constant boundary condition considered in the traditional Stefan problem. Time varying ground heat flux is obtained from net radiation and surface temperature using the Maximum Entropy Production (MEP) model as the driver of the active layer melting process. Conductive heat flux at the melting front is approximated in terms of an analytical function of ground heat flux. The simulated active layer depth is in good agreement with the field observations.

The Calhoun Critical Zone Observatory (CCZO) has been collecting above and below canopy water, energy and carbon fluxes and other hydro-meteorological processes since 2016. The observations provide a unique opportunity to investigate the coupling between aboveground environmental conditions and belowground fluxes of mass and energy in general and the fate of fixed carbon in forested ecosystems in particular. The simultaneous measurements of soil-canopy-atmosphere states and fluxes reveal the role of pine forest in diurnal and seasonal dynamics of the CCZ ecosystem.

The increase in world population, added to socioeconomic development and climate change, have highlighted one of the biggest problems worldwide: the depletion of water resources. The La Ligua and Petorca river basins, in central Chile, are an example of this problem, as rainfall has decreased in recent years, while socio-economic activities, mainly agriculture have increase. This situation has led to a severe water stress, and the need for integrated and sustainable river basin management, aimed at understanding the behavior of basins, aquifers, and the exchange of flows between them. Therefore, the main objective of this research is to quantify the impacts of climate change, in terms of groundwater scarcity, in semi-arid basins using integrated modeling of water resources. For this purpose, groundwater/surface waters integrated models of La Ligua and Petorca basins were developed using WEAP and MODFLOW. Both basins present different hydrological, social, and geographical characteristics. Different scenarios were evaluated to quantify groundwater depletion. These scenarios depend on climatic forcings, such as precipitation and temperature, which were obtained from the Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Results forecast that annual precipitation will decrease, whereas average annual temperature will increase in these semi-arid regions. As a consequence, the aquifer’s recovery rate will reduce, decreasing the number of wells that provide drinking water in rural and agricultural areas. In conclusion, the coupling of hydrological and hydrogeological models is a tool that allows researchers and stakeholders to make opportune and appropriate decisions on the management of basins and aquifers, which is even more important in basins that are expected to be or are already under severe water stress.

The use of modeling tools for integrated water resources management is a complex task due to the large number of processes involved in a basin. Moreover, these modeling tools commonly require information that is not readily available, such as illegal water withdrawals, or other data difficult to obtain, which results in groundwater models that fail to capture the aquifer dynamics. In recent years, machine learning algorithms have shown outstanding performance as prediction tools. Despite being questioned for not having a physical basis, they have been used in areas such as hydrology and hydrogeology (e.g., for flow prediction, rain forecast). Thus, the objective of this research is to estimate groundwater withdrawals using machine learning algorithms and integrated water management models. To achieve this objective, ensembles of groundwater levels were generated with a previously calibrated groundwater/surface water integrated model. Then, these ensembles were used as input parameters for Gaussian process regression (GPR) and artificial neural network (ANN) models to construct time series of water withdrawals throughout a basin. This method was applied in the Petorca and La Ligua basins, in central Chile, as they exhibit a contrasting reality in terms of water availability even when they have geographical proximity. Also, these basins are within an effective extraction monitoring program lead by the Chilean water authority that can be used to validate the users’ water withdrawal. Our results show that the GPR model, compared to ANNs, adequately estimates the spatiotemporal distribution of groundwater withdrawals in the pilot basins. Thus, the use of machine learning algorithms improves the performance of integrated water resources management models.

The rapid acceleration of Greenland Ice Sheet mass loss over, particularly the last two decades, is well documented.However, limits in early remote sensing restricted the details with which we could examine local changes on an ice-sheet-wide scale, particularly in areas of slow motion, along shear margins and complex coastal terrain. We explore the localcharacter of rapid contemporary change marine-terminating glaciers using satellite-derived ice sheet surface velocities,glacier terminus advance/retreat history, and surface elevation-change data from the 1980s to the present. Widespread glacierterminus retreat is a strong and more consistent climate response indicator than velocity change, but local changes in velocityare critical indicators of rapid ice sheet reconfiguration. Ice thickness changes related to changing ice dynamics often providethe first evidence of processes that initiate outlet glacier retreats and mass loss, such as the development of sub-ice shelfcavities and subglacial hydrology changes. Reconfiguration is observed locally as narrowing zones of fast-flow, ice flowrerouting, shear margin migration, and likely glacier outlet abandonment. These patterns are apparent in all ice sheet sectorsand observable from space-borne instruments. The rapid reconfiguration now well underway in Greenland has wide-rangingimplications, including expected changes in subglacial hydrology, ice discharge, freshwater flux to the ocean, and transport ofnutrients and sediment. Lacking detailed observations of earlier deglaciations and current limits on ice-sheet modelcapabilities, the expanding details of these combined observational records may provide a valuable analog for studying pastice sheet dynamics and projecting future ice loss.

Light detection and ranging (LiDAR) technologies are changing the ways in which scientists research the Arctic. Unmanned aerial vehicle (UAV)-based LiDAR collects detailed structural landscape data by returning high density point clouds. LiDAR systems are improving the quality and accuracy of data collection compared to field surveys and help to remove some of the logistical barriers of research in remote and complicated terrain. Our study mapped thermokarst depressions in a 3 km2 watershed on the Seward Peninsula near Nome, Alaska in 2017 and 2018. The watershed is characterized as tussock permafrost landscape consisting of grasses and mosses interspersed with patches of dense shrubs. By configuring the UAV with a 32 laser swath and flying slowly at low altitude, we collected high density point clouds of about 4,000 points m2, including high density terrain surface points underneath dense shrubby vegetation. We then modeled the sub-vegetation terrain surface at very fine detail to detect thermokarst depressions. Combining these high resolution data with vegetation surveys and topographic properties, we tested the relationship between permafrost subsidence, thermokarst depressions and vegetation type, specifically the relationships in shrub-associated thermokarst features. By coupling our LiDAR data and analysis with hydrologic models, climate variables (e.g., snow depth, soil moisture), and vegetation surveys, we can infer geospatial relationships between thermokarst development, vegetation, and landscape position throughout the watershed. The technologies used in our study have implications for predicting the development of future thermokarst features and permafrost thaw sites across the Arctic.

This paper presents some pre-earthquake and coseismic water-level and stream-flow changes observed in Japan and Taiwan. The results suggest that: (1) Hydrological precursors do occur; (2) they can be observed at a relatively few sensitive sites; (3) these “sensitive” sites consistently show coseismic changes; (4) the mechanisms of these precursors can be understood, if crustal heterogeneity and pre-earthquake slow-slip events are included in their mechanism consideration. In Japan, the monitored well was sensitive, because it tapped a permeable aquifer connected to a nearby fault, which was under a hydraulic –pressure gradient caused by pumping activity in an underground gallery on the other side of the fault. Also, the fault was in a near-critical condition, such that leakage could be caused by a small crustal disturbance, such as seismic shaking, in the case of coseismic changes, or a stress increment, in the case of precursory changes. In Taiwan, both the sensitive well and the stream gauge were located on the hanging wall of the seismogenic fault of the magnitude-7.6 Chi-Chi earthquake in 1999. The hanging wall probably bulged before the earthquake, causing opening up of fractures along some secondary faults, and allowing stored groundwater to flow down to the monitored stream and caused the observed pre-earthquake stream-flow increase. The continued fracture-opening process toward greater depth then caused down-flow of water into greater depth of the crust and triggered the occurrence of slow-slip events, which propagated down-dip to where these faults met the seismogenic fault and caused slow-slip events to propagate up-dip the seismogenic fault toward the hypocenter, triggering the earthquake and the observed water-level precursor at a well located near the tip of the wall.

We quantify how changes in natural flood discharge control bedload rest time distributions and may influence particle diffusion through mountain river networks. We embedded accelerometers and gyroscopes into artificial cobbles deployed in Halfmoon Creek, Colorado, USA, and measured bedload transport during 28 daily snowmelt flood hydrographs in 2015. From the motion sensor data we calculate motion and rest distributions over ~6 orders of temporal magnitude, from ~2 seconds to ~1 month. Motion durations follow a thin-tailed exponential distribution. Rests >12 hours can be well fit by both truncated Pareto distributions and exponentially-tempered Pareto distributions, suggesting ambiguity in whether rests remain heavy-tailed or transition to thin tails at even longer timescales. Rest time scaling varies not only with timescale but also with flow intensity, becoming less heavy-tailed as shear stress increases. A rest time scaling break at ~12 hours may be caused by daily discharge cyclicity.

Organic matter is an important constituent in organic-rich shale, which influences the hydrocarbon generation, as well as the mechanical behavior, of shale reservoirs. The physical, chemical, and mechanical properties of organic matter depend on the source material and the thermal evolution process. Previous works attempted to investigate the impact of thermal maturation on the mechanical properties of organic matter. However, owing to the lack of maceral classification and the limitation of data volume during the mechanical measurement, no consistent trend has been identified. In this work, vitrinite reflectance test, scanning electron microscope observation, nanoindentation, and micro-Raman analysis were combined for geochemical and mechanical characterization. A total of 114 test areas were selected for testing, enhancing reliability of the test results. The Young’s moduli of organic matter are from 3.57 GPa to 8.32 GPa. With the same thermal maturity, inertinite has the highest Young’s modulus, while the modulus of bitumen is the lowest. The Young’s moduli of different organic types all increase with vitrinite reflectance. When vitrinite reflectance increases from 0.62% to 1.13%, the modulus of inertinite and vitrinite is increased by 57% and 78%, respectively. In addition, with the increase of thermal maturity, the micro-Raman test results show a decrease of intensity ratio of D peak to G peak, indicating an increase of the ordered structure in organic matter. Organic type and thermal maturity reflect the diversity of the source material and chemical structure change during the thermal evolution process, and together they influence the mechanical properties of organic matter.

Environmental controls on methane (CH) emission from lakes are poorly understood at sub-daily time scales due to a lack of continuous data, especially for ebullition. We used a novel technique to partition eddy covariance CH flux observed in the littoral zone of a mid-latitude shallow lake in Japan and examined the environmental controls on diffusion and ebullitive CH flux separately at a sub-daily time scale during different seasons. Both diffusive and ebullitive flux were significantly higher in summer than winter. The contribution of ebullitive flux to total flux was 56% on average. Diffusive flux increased with increasing wind speed due to increased subsurface turbulence. For a given wind speed, diffusive flux was higher in summer than in winter due to the higher concentration of dissolved CH in the surface water during summer. The transfer of accumulated dissolved CH from the bottom layer to the surface in summer and the accumulation of dissolved CH under surface ice in winter were important for explaining the variability of diffusive flux. In summer, ebullition tended to occur following triggers such as a decrease in hydrostatic pressure or an increase in wind speed. In winter, on the other hand, the impact of triggers was not obvious, and ebullition tended to occur in the morning when the wind speed began to increase. The low CH production rate in winter slowed the replenishment of bubbles in the sediment, negating the effect of triggers on ebullition.

Flow separation has been observed and studied in sinuous laboratory channels and natural meanders, but the effects of flow separation on along-channel drag are not well understood. Motivated by observations of large drag coefficients from a shallow, sinuous estuary, we found in idealized numerical models representative of that system that flow separation in tidal channels with curvature can create form drag that increases the total drag to more than twice that from bottom friction alone. In the momentum budget, the pressure gradient is balanced by the combined effects of bottom friction and form drag, which is calculated directly. The effective increase in total drag coefficient depends on two geometric parameters: dimensionless water depth and bend sharpness, or the bend radius of curvature to channel width ratio. We introduce a theoretical boundary layer separation model to explain this parameter dependence and to predict flow separation and the increased drag. The drag coefficient can increase by a factor of 2 - 7 in “sharp” and “deep” sinuous channels where flow separation is most likely. Flow separation also enhances energy dissipation due to increased velocities, resulting in greater loss of tidal energy and weakened stratification.Flow separation and the associated drag increase are expected to be more common in meanders of tidal channels than rivers, where point bars that inhibit flow separation are more commonly found. The increased drag due to flow separation affects the tidal amplitude and phasing along the estuary and creates potential morphological feedbacks.

This study proposes the utilization of municipal well records as an alternative dataset for large-scale heterogeneity characterization of hydraulic conductivity () and specific storage () using hydraulic tomography (HT). To investigate the performance of HT and the feasibility of utilizing municipal well records, a three-dimensional aquifer/aquitard system is constructed and synthetic groundwater flow and solute transport experiments are conducted to generate data for inverse modeling and validation of results. In particular, we simultaneously calibrate four groundwater models with varying parameterization complexity using five datasets consisting of different time durations and periods. Calibration and validation results are qualitatively and quantitatively assessed to evaluate the performance of investigated models. The estimated and tomograms from different model cases are also validated through the simulation of independently conducted pumping tests and conservative solute transport. Our study reveals that: 1) the HT analysis of municipal well records is feasible and yields reliable heterogeneous and distributions where drawdown records are available; 2) accurate geological information is of critical importance when data density is low and should be incorporated for geostatistical inversions; 3) the estimated and tomograms from the geostatistical model with geological information are capable in providing robust predictions of both groundwater flow and solute transport. Overall, this synthetic study provides a general framework for large-scale heterogeneity characterization using HT through the interpretation of municipal well records, and provides guidance for applying this concept to field problems.

Nutrient enrichment is a major issue to many inland and coastal waterbodies worldwide, including Chesapeake Bay. River water quality integrates the spatial and temporal changes of watersheds and forms the foundation for disentangling the effects of anthropogenic inputs. However, many water-quality studies are focused on limited portions of the watershed or a subset of potential drivers. We demonstrate with the Chesapeake Bay Nontidal Monitoring Network (84 stations) that advanced machine learning approaches – i.e., hierarchical clustering and random forest – can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks. Cluster analysis revealed the regional patterns of short-term TN trends (2007-2018) and categorized the stations to three distinct clusters, namely, V-shape (n = 25), monotonic decline (n = 35), and monotonic increase (n = 26). Random forest models were developed to predict the clusters using watershed characteristics and major N sources, which provided information on regional drivers of TN trends. We show encouraging evidence that improved nutrient management has resulted in declines in agricultural nonpoint sources, which in turn contributed to water quality improvement. Additionally, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. However, TN trends are degrading in forested watersheds, suggesting new sources of N in forests. Finally, TN trends were predicted for the entire Chesapeake Bay watershed at the scale of 979 river segments, providing fine-level information that can facilitate targeted watershed management, especially in unmonitored areas. More generally, this combined use of clustering and classification approaches can be applied to other monitoring networks to address similar questions.

Links between hydrology and sliding of the Greenland Ice Sheet (GrIS) are poorly understood. Here, we monitored meltwater’s propagation through the entire glacial hydrologic system for catchments at different elevations by quantifying the lag cascade as daily meltwater pulses traveled through the supraglacial, englacial, and subglacial drainage systems. We found that meltwater’s residence time within supraglacial catchments-depending upon area, snow cover, and degree of channelization-controls the timing of peak moulin head, resulting in the two hour later peak observed at higher-elevations. Unlike at lower elevations where peak moulin head and sliding coincided, at higher elevations peak sliding lagged moulin head by ~2.8 hours. This delay was likely caused by the area’s lower moulin density, which required diurnal pressure oscillations to migrate further away from subglacial conduits to elicit the observed velocity response. These observations highlight the supraglacial drainage system’s control on coupling GrIS hydrology and sliding.

Temporal redox fluctuations alter the pools of reducible FeIII and greenhouse gas emissions in humid upland soils. However, it is less clear how the characteristics of these fluctuations (length, frequency, amplitude) impact biogeochemical rates. We hypothesized that anaerobic rates of FeIII reduction and CH4 emissions are sensitive to the length of soil oxygen deprivation. To test this hypothesis, we exposed a surface soil from the Luquillo Experimental Forest to three lengths of O2 perturbation during repeated redox oscillations: an anoxic interval of 6 d with oxic intervals of 8, 24, or 72 h. We found that shorter oxic intervals resulted in more anaerobic FeIII reduction, while longer oxic intervals stimulated higher anaerobic CH4 emissions (CO2 fluxes did not change). We propose that short O2 pulses stimulate Fe reduction by resupplying the FeIII electron acceptor, but do not last long enough to inhibit microbial Fe reducers; conversely long O2 pulses suppress microbial iron reducers to a greater extent than methanogens leading to enhanced CH4 emissions. Thus, the length of periodic oxidant exposure selectively enhances less thermodynamically favorable anaerobic processes by modulating the competitiveness of dominate anaerobic bacteria, which is important for regulating greenhouse gas emissions in redox dynamic soils.