Hydrological Community Research Articles

Reimagining the Surface Water and Ocean Topography Mission as the “Landsat” of Surface Water [Perspective

The Surface Water Ocean Topography (SWOT) mission, jointly developed by NASA and French Space Agency (CNES) with contributions from the Canadian and U.K. space agencies, and planned for launch in 2022, is designed to provide a spatially distributed and high-frequency measurement of water elevation data for the hydrology and oceanography communities for the first time <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> . By virtue of its novel observational capability and stated scientific goals, SWOT satellite data are expected to have a profound impact on our understanding of global surface water. Although there have been many satellite missions that can either map water extent or water elevation, SWOT is the first mission that will measure extent and elevation concurrently regardless of cloud cover conditions and with a higher degree of precision.

GW/SW-MST: A Groundwater/Surface-Water Method Selection Tool.

Groundwater/surface-water (GW/SW) exchange and hyporheic processes are topics receiving increasing attention from the hydrologic community. Hydraulic, chemical, temperature, geophysical, and remote sensing methods are used to achieve various goals (e.g., inference of GW/SW exchange, mapping of bed materials, etc.), but the application of these methods is constrained by site conditions such as water depth, specific conductance, bed material, and other factors. Researchers and environmental professionals working on GW/SW problems come from diverse fields and rarely have expertise in all available field methods; hence there is a need for guidance to design field campaigns and select methods that both contribute to study goals and are likely to work under site-specific conditions. Here, we present the spreadsheet-based GW/SW-Method Selection Tool (GW/SW-MST) to help practitioners identify methods for use in GW/SW and hyporheic studies. The GW/SW-MST is a Microsoft Excel-based decision support tool in which the user selects answers to questions about GW/SW-related study goals and site parameters and characteristics. Based on user input, the tool indicates which methods from a toolbox of 32 methods could potentially contribute to achieving the specified goals at the site described.

Reconceptualizing the hyporheic zone for nonperennial rivers and streams.

Nonperennial streams dominate global river networks and are increasing in occurrence across space and time. When surface flow ceases or the surface water dries, flow or moisture can be retained in the subsurface sediments of the hyporheic zone, supporting aquatic communities and ecosystem processes. However, hydrological and ecological definitions of the hyporheic zone have been developed in perennial rivers and emphasize the mixing of water and organisms, respectively, from both the surface stream and groundwater. The adaptation of such definitions to include both humid and dry unsaturated conditions could promote characterization of how hydrological and biogeochemical variability shape ecological communities within nonperennial hyporheic zones, advancing our understanding of both ecosystem structure and function in these habitats. To conceptualize hyporheic zones for nonperennial streams, we review how water sources and surface and subsurface structure influence hydrological and physicochemical conditions. We consider the extent of this zone and how biogeochemistry and ecology might vary with surface states. We then link these components to the composition of nonperennial stream communities. Next, we examine literature to identify priorities for hydrological and ecological research exploring nonperennial hyporheic zones. Lastly, by integrating hydrology, biogeochemistry, and ecology, we recommend a multidisciplinary conceptualization of the nonperennial hyporheic zone as the porous subsurface streambed sediments that shift between lotic, lentic, humid, and dry conditions in space and time to support aquatic-terrestrial biodiversity. As river drying increases in extent because of global change, we call for holistic, interdisciplinary research across the terrestrial and aquatic sciences to apply this conceptualization to characterize hyporheic zone structure and function across the full spectrum of hydrological states.

Nordic hydrological frontier in the 21st century

AbstractThe 21st century has brought new challenges and opportunities and has also increased demands on the Nordic hydrological community. Our hydrological science focus and approaches need rethinking and adaptation to the changing requirements of society in response to climate change and human interventions, in search of more comprehensive and cross-disciplinary solutions. This commentary highlights new possibilities and suggests vital steps forward for the scientific discipline within Nordic hydrological research. By providing a common direction, we hope to increase awareness, accelerate progress in the hydrological community, and emphasize the importance of hydrological knowledge for serving other fields of science and society at large. We hope that our vision and the opportunities we identify will raise awareness of the scientific discipline and assist in the long-term development of the Nordic hydrological frontier in the 21st century.

Calibration and Uncertainty Analysis of Hussainsagar Watershed using SWAT-CUP, Sufi -2 Approach

Simulation of process-based hydrological models is a significant process and can be better interpreted by calibration and validation of these models. In the present study, SWAT model was chosen to simulate the hydrologic processes of Hussainsagar watershed followed by calibration and validation for monthly time steps using the SUFI-2 approach. Several trials were initiated to achieve a good correlation between the observed and simulated values as each objective function is sensitive to certain parameters. The goodness of fit was further assessed using the coefficient of determination (R2) and Nash–Sutcliffe Efficiency (NS) between the observed and final simulated values. The results have shown a good correlation between observed and simulated discharge values with R2 =0.97 and NS = 0.96, for calibration period and with R2 =0.99 and NS = 0.97 for the validation period for monthly time steps. After performing calibration followed by validation, the influence of each parameter considered was ranked with the help of global sensitivity analysis present in the SWAT-CUP. The overall study revealed that the ArcSWAT can be used efficiently to study the behavior of an urban watershed having varied hydrology, also determine the trend in the rainfall pattern for daily and monthly periods. The results obtained have shown a good correlation and would be helpful to the hydrological community for adopting better water management practices and in designing better infrastructure.

Socioeconomic drought analysis by standardized water supply and demand index under changing environment

Along with climate change, drought has been known to be a catastrophic and complicated natural calamity that severely influences society and individual lives. Among various types of droughts, a socioeconomic drought is defined when a watershed is not capable of meeting water demands, resulting in water shortage. Although meteorological, hydrological, and agricultural communities have defined and assessed other types of drought over the last decades, there are however few studies that have defined and evaluated the socioeconomic drought. This study develops a new socioeconomic drought index, named Standardized Water Supply and Demand Index (SWSDI), to systematically identify and evaluate socioeconomic drought. First, water demand and water supply for a watershed (Dongjiang River basin in this study) were evaluated by the System Dynamics (SD) model and the Soil and Water Assessment Tool (SWAT). With water balance between water supply and water converted to the normal distribution function, the SWSDI was obtained by a normalization process. Finally, a threshold corresponding to a percentile value was determined to identify the socioeconomic drought events and further assess the characteristics of the socioeconomic drought, such as drought duration and severity. Results indicated that socioeconomic drought events occurring in the Dongjiang River basin under climate change were frequent but moderate and their frequency and severity varied with climate scenarios employed in the study. In particular, the socioeconomic drought events were frequently detected during a transition period from high flow period to low flow period and vice versa. An increase in radiation intensity induced not only the latency of more severe socioeconomic drought events but also an inter-annual distribution of socioeconomic drought events that were increasingly concentrated. This study showed that SWSDI was effective in identifying and evaluating socioeconomic drought, and can be applied to other watersheds. This approach can be employed for socioeconomic drought monitoring and management.

A taxonomy of hydrological processes and watershed function

AbstractThis paper presents a taxonomy (hierarchical organization) of hydrological processes; specifically, runoff generation processes in natural watersheds. Over 130 process names were extracted from a literature review of papers describing experimental watersheds, perceptual models, and runoff processes in a range of hydro‐climatic environments. Processes were arranged into a hierarchical structure, and presented as a spreadsheet and interactive diagram. For each process, additional information was provided: a list of alternative names for the same process, a classification into hydrological function (e.g., flux, storage, release) and a unique identifier similar to a hashtag. We hope that the proposed hierarchy will prompt collaboration and debate in the hydrologic community into naming and organizing processes, towards a comprehensive taxonomy. The taxonomy provides a method to label and search hydrological knowledge, thereby facilitating synthesis and comparison of processes across watersheds.

A hydrologist's guide to open science

Abstract. Open, accessible, reusable, and reproducible hydrologic research can have a significant positive impact on the scientific community and broader society. While more individuals and organizations within the hydrology community are embracing open science practices, technical (e.g., limited coding experience), resource (e.g., open access fees), and social (e.g., fear of weaknesses being exposed or ideas being scooped) challenges remain. Furthermore, there are a growing number of constantly evolving open science tools, resources, and initiatives that can be overwhelming. These challenges and the ever-evolving nature of the open science landscape may seem insurmountable for hydrologists interested in pursuing open science. Therefore, we propose the general “Open Hydrology Principles” to guide individual and community progress toward open science for research and education and the “Open Hydrology Practical Guide” to improve the accessibility of currently available tools and approaches. We aim to inform and empower hydrologists as they transition to open, accessible, reusable, and reproducible research. We discuss the benefits as well as common open science challenges and how hydrologists can overcome them. The Open Hydrology Principles and Open Hydrology Practical Guide reflect our knowledge of the current state of open hydrology; we recognize that recommendations and suggestions will evolve and expand with emerging open science infrastructures, workflows, and research experiences. Therefore, we encourage hydrologists all over the globe to join in and help advance open science by contributing to the living version of this document and by sharing open hydrology resources in the community-supported repository (https://open-hydrology.github.io, last access: 1 February 2022).

COVID‐19 Impacts Highlight the Need for Holistic Evaluation of Research in the Hydrologic Sciences

AbstractAs we reckon with the effect of COVID‐19 on the research enterprise in hydrologic science, it is important to acknowledge that disruptions will be persistent and that institutional‐level adjustments, while helpful, are not sufficient to mitigate all impacts on hydrologic scientists. Here, we describe the breadth of research contributions in the hydrologic sciences, consider how the pandemic has impacted this portfolio of contributions, document one impact that is already being realized in publication of research, and suggest guidance to the hydrologic science community, institutions, review panels, and funding organizations in considering these impacts at various stages of hiring and promotion in our community. Acknowledging the diversity of contributions to research is particularly valuable because it provides a more objective, transparent, and holistic basis for evaluating individuals within the context of norms of the hydrologic science community. With clearly established values, it is easier to identify impacts of life events, such as those related to the COVID‐19 pandemic, as they are manifested in individuals under a diversity of circumstances.

Vegetation, hydrology, and sand movement interactions on the Slate Canyon alluvial fan-Keeler Dunes Complex, Owens Valley, California

Relationships among hydrology, sand movement, and vegetation community dynamics within the Slate Canyon alluvial fan and Keeler Dunes Complex, Owens (dry) Lake, California, were characterized. Construction of flood control berms (1954, 1967) cut off surface overflow events on the alluvial fan, resulting in subsequent very low plant cover and significant sand movement. Hydrologic modeling and analysis of plant cover and sand movement from aerial photos were combined to document and explain changes in plant cover in the study area for upland vegetation and groundwater-dependent vegetation. Where hydrologic factors dominated, upland vegetation was observed to have much lower cover (median 6%) than groundwater-dependent vegetation (median 41%) and much higher variability throughout the 1944 to 2012 study period. For upland vegetation, simulated potential plant cover was 9.2% and this declined to 4.4% when channel flow inputs were eliminated due to berm construction. Areas with minimal sand movement over the study period expressed actual plant cover similar to simulated levels. Areas with significant sand movement, however, had much lower plant cover than simulated potential values and areas with flood flow inputs had much higher cover. The decline in plant cover from 9.2 to 4.4% resulted in an estimated 4.4-fold greater sand movement (normalized sand flux increased from 14% to 62% relative to 100% for bare soil). The sensitivity of surface stability at low vegetation cover and the potential for hydrologic changes to reduce vegetation cover in this range illustrates how hydrology and sand movement interactions can destabilize sensitive sand dunes.

Monitoring of Inland Water Levels by Satellite Altimetry and Deep Learning

Deep convolutional neural networks (NNs) have proven their efficiency for image processing and are routinely used for image classification. In this article, we use them to convert radar measurements into water distance and ultimately into water levels of inland waterbodies. The measurements used are the successive echoes of the spaceborne radar altimeter signal on a waterbody, the radargram. We show that by using forward modeling with an accurate altimetry simulator, we can generate a sufficient amount of radargrams and train a deep NN accurately enough to obtain water level series from radargrams in a hydrology context. The method is validated at selected waterbodies by comparing these water level time series with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements on rivers whose width varied between 50 m and 4 km. The correlation of these time series with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data was over 0.95 with root mean square error (RMSE) between 26 and 43 cm. The results were also more robust than the Offset Center of Gravity (OCOG)/Ice-1 retracker time series of the same data. The validation shows that this automatic method performs generally as well as a carefully tuned manual method for removing outliers from the ranges provided by the state of the art classical retrackers used by the spatial hydrology community. This new tool is a big step toward a generic, global, and automated method to retrieve inland water levels from altimetry measurements. This goal is especially important in the context of continuously declining number of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements and of utmost importance for adequate water resources management at the global scale.

Why do we have so many different hydrological models? A review based on the case of Switzerland

AbstractHydrology plays a central role in applied and fundamental environmental sciences, but it is well known to suffer from an overwhelming diversity of models, particularly to simulate streamflow. We discuss here in detail how such diversity did arise based on the example of Switzerland. The case study's relevance stems from the fact that Switzerland, despite being a small country, shows a variety of hydro‐climatological regimes, of water resources management challenges, and of hydrological research institutes that led to a model diversification that stands exemplary for the diversification that arose also at larger scales. Our analysis, based on literature review, personal inquiry, and an author survey, summarizes the main driving forces behind model diversification. We anticipate that this review not only helps researchers from other fields but in particular also the international hydrology community to understand why we have so many different streamflow models.This article is categorized under: Science of Water &gt; Science of Water Science of Water &gt; Hydrological Processes Science of Water &gt; Methods

Vegetation and hydrology stratification as proxies to estimate methane emission from tidal marshes

Direct measurement of methane emissions is cost-prohibitive for greenhouse gas offset projects, necessitating the development of alternative accounting methods such as proxies. Salinity is a useful proxy for tidal marsh CH4 emissions when comparing across a wide range of salinity regimes but does not adequately explain variation in brackish and freshwater regimes, where variation in emissions is large. We sought to improve upon the salinity proxy in a marsh complex on Deal Island Peninsula, Maryland, USA by comparing emissions from four strata differing in hydrology and plant community composition. Mean CH4 chamber-collected emissions measured as mg CH4 m−2 h−1 ranked as S. alterniflora (1.2 ± 0.3) ≫ High-elevation J. roemerianus (0.4 ± 0.06) > Low-elevation J. roemerianus (0.3 ± 0.07) = S. patens (0.1 ± 0.01). Sulfate depletion generally reflected the same pattern with significantly greater depletion in the S. alterniflora stratum (61 ± 4%) than in the S. patens stratum (1 ± 9%) with the J. roemerianus strata falling in between. We attribute the high CH4 emissions in the S. alterniflora stratum to sulfate depletion likely driven by limited connectivity to tidal waters. Low CH4 emissions in the S. patens stratum are attributed to lower water levels, higher levels of ferric iron, and shallow rooting depth. Moderate CH4 emissions from the J. roemerianus strata were likely due to plant traits that favor CH4 oxidation over CH4 production. Hydrology and plant community composition have significant potential as proxies to estimate CH4 emissions at the site scale.

Exploring the Application of Flood Scaling Property in Hydrological Model Calibration

AbstractModel calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash–Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among subbasins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments.Significance StatementThis paper proposes a new hydrological model calibration strategy that explores the potential of applying flood scaling properties as constraints. The proposed method effectively captures flood peaks with fewer requirements of observed streamflow time series data, providing a new alternative method in hydrological model calibration for ungauged watersheds. For gauged watersheds, adopting flood scaling properties as model constraints could make the hydrological model calibration more physically based and improve the performance at catchment interior points. We encourage this novel method to be adopted in model calibration for both gauged and data-scarce watersheds.

Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States

Abstract. Recent advancements in computational efficiency and Earth system modeling have awarded hydrologists with increasingly high-resolution models of terrestrial hydrology, which are paramount to understanding and predicting complex fluxes of moisture and energy. Continental-scale hydrologic simulations are, in particular, of interest to the hydrologic community for numerous societal, scientific, and operational benefits. The coupled hydrology–land surface model ParFlow–CLM configured over the continental United States (PFCONUS) has been employed in previous literature to study scale-dependent connections between water table depth, topography, recharge, and evapotranspiration, as well as to explore impacts of anthropogenic aquifer depletion to the water and energy balance. These studies have allowed for an unprecedented process-based understanding of the continental water cycle at high resolution. Here, we provide the most comprehensive evaluation of PFCONUS version 1.0 (PFCONUSv1) performance to date by comparing numerous modeled water balance components with thousands of in situ observations and several remote sensing products and using a range of statistical performance metrics for evaluation. PFCONUSv1 comparisons with these datasets are a promising indicator of model fidelity and ability to reproduce the continental-scale water balance at high resolution. Areas for improvement are identified, such as a positive streamflow bias at gauges in the eastern Great Plains, a shallow water table bias over many areas of the model domain, and low bias in seasonal total water storage amplitude, especially for the Ohio, Missouri, and Arkansas River basins. We discuss several potential sources for model bias and suggest that minimizing error in topographic processing and meteorological forcing would considerably improve model performance. Results here provide a benchmark and guidance for further PFCONUS model development, and they highlight the importance of concurrently evaluating all hydrologic components and fluxes to provide a multivariate, holistic validation of the complete modeled water balance.

SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models

Abstract. Catchment-scale hydrological models are widely used to represent and improve our understanding of hydrological processes and to support operational water resource management. Conceptual models, which approximate catchment dynamics using relatively simple storage and routing elements, offer an attractive compromise in terms of predictive accuracy, computational demands, and amenability to interpretation. This paper introduces SuperflexPy, an open-source Python framework implementing the SUPERFLEX principles (Fenicia et al., 2011) for building conceptual hydrological models from generic components, with a high degree of control over all aspects of model specification. SuperflexPy can be used to build models of a wide range of spatial complexity, ranging from simple lumped models (e.g., a reservoir) to spatially distributed configurations (e.g., nested sub-catchments), with the ability to customize all individual model components. SuperflexPy is a Python package, enabling modelers to exploit the full potential of the framework without the need for separate software installations and making it easier to use and interface with existing Python code for model deployment. This paper presents the general architecture of SuperflexPy, discusses the software design and implementation choices, and illustrates its usage to build conceptual models of varying degrees of complexity. The illustration includes the usage of existing SuperflexPy model elements, as well as their extension to implement new functionality. Comprehensive documentation is available online and provided as a Supplement to this paper. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations for model comparison and for operational work.

Stemflow infiltration areas into forest soils around American beech (Fagus grandifolia Ehrh.) trees

AbstractThe size of stemflow infiltration areas around the boles of trees is currently a topic of interest and debate within the hydrologic community. There is a gap in our knowledge of stemflow infiltration areas in many wooded ecosystems and a need for more than the few studies that have examined stemflow infiltration areas directly. Hence, this field study was specifically undertaken to mitigate the existing data gap by providing direct measurements of stemflow infiltration areas from high stemflow‐producing American beech (Fagus grandifolia Ehrh.) trees. Different stemflow rates (290, 72 and 31 L h−1) were simulated using dye‐infused stemflow and the areas of stemflow infiltration around four trees determined by measuring the areal extent of dye on the soil surface. Our results revealed that stemflow infiltration areas ranged from 0.0035 to 0.0951 m2 tree−1. The mean basal area funnelling ratio was 46.5 ± 1.8, whereas the funnelling ratios per unit infiltration areas, , were between 32.0 and 258.4. Despite intentionally high stemflow rates, chosen to compensate for the high infiltration capacities of forest soils, these results reinforce the fact that stemflow is an extremely localized input in natural forests. Thus, these results, even if specific to F. grandifolia within a particular forest and soil type, support a growing body of work indicating that stemflow infiltration areas are usually &lt;1 m2, and often much smaller, in natural forests. Moreover, the high values of provide further evidence indicating that stemflow inputs are important for the development of hot spots in near‐trunk soils.

An Open‐Source Python Library for Varying Model Parameters and Automating Concurrent Simulations of the National Water Model

AbstractThe National Water Model (NWM), a configuration of the Weather Research and Forecasting Hydrological model, operates as the United States’ hydrological model. The NWM predicts streamflow at more than 2.7 million river reaches; and is a subject of growing attention in the hydrological modeling community. Large‐scale computationally distributed models such as the NWM, often require technical knowledge of, and access to, cluster‐based computing environments for model compilation and simulation. User‐friendly tools capable of setting up and running such models to adjust and explore their parameter space generally do not exist. Here we present the Dockerized Job Scheduler (DJS) a Python library that takes a service approach to modeling. The library is capable of (1) generating varied parameter sets and (2) orchestrating concurrent NWM simulations via Docker. DJS is designed to automate the deployment of varied parameter simulations and lower the model usage entrance barrier. In this paper, we use a case study to demonstrate its installation and usage.

Achieving Breakthroughs in Global Hydrologic Science by Unlocking the Power of Multisensor, Multidisciplinary Earth Observations

AbstractOver the last half century, remote sensing has transformed hydrologic science. Whereas early efforts were devoted to observation of discrete variables, we now consider spaceborne missions dedicated to interlinked global hydrologic processes. Furthermore, cloud computing and computational techniques are accelerating analyses of these data. How will the hydrologic community use these new resources to better understand the world's water and related challenges facing society? In this commentary, we suggest that optimizing the benefits of remote sensing for advancing hydrologic research will happen by integrating multidisciplinary and multisensor data, leveraging commercial satellite measurements, and employing data assimilation, cloud computing, and machine learning. We provide several recommendations to these ends.

Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA

Biogenic methane is estimated to account for one-fifth of the natural gas worldwide and there is great interest in controlling methane from different sources. Biogenic coalbed methane (CBM) production relies on syntrophic associations between fermentative bacteria and methanogenic archaea to anaerobically degrade recalcitrant coal and produce methanogenic substrates. However, very little is known about how differences in geochemistry, hydrology, and microbial community composition influence subsurface carbon utilization and CBM production. The addition of an amendment consisting of microalgal biomass has previously been shown to increase CBM production while providing the possibility of a closed-loop fossil system where waste (production water) is used to grow algae to ultimately produce energy (methane). However, the efficiency of enhancing CBM production under different redox conditions remains unresolved. In this study, we focused on the U.S. Geological Survey's Birney test site (Montana, USA) that has nine wells vertically accessing four coal seams with varying geochemistry (low and high sulfate (SO42−)) and methane production rates. We used organic matter (OM) in the form of algal biomass to discern the effect of this amendment on OM degradation and microbially enhanced CBM production potential under different geochemical constraints. We tracked changes in community composition, OM composition, organic carbon (OC) concentration, methane production, and nutrients in batch systems over six months. Methane production was detected only in microcosms from low SO42− wells (168 to 800 μg methane per gram of coal). The OC consumption varied across time for all wells and the variation was greatest for the low SO42− wells. Different groups of syntrophic bacteria were associated with net‑carbon consuming microcosms, and specifically Syntrophorhabdus was identified with several different statistical methods as a potentially important coal degrader. Results from this study provide insight into potential coal-degraders, the compositional changes in some of the different OM fractions, and trends in carbon consumption related to methane production across coal seams along the vertical SO42− gradient.