Production of hydrogen peroxide in an intra-meander hyporheic zone at East River, Colorado

The traditionally held assumption that photo-dependent processes are the predominant source of H2O2 in natural waters has been recently questioned by an increrasing body of evidence showing the ubiquitiousness of H2O2 in dark water bodies and in groundwater. In this study, we conducted field measurement of H2O2 in an intra-meander hyporheic zone and in surface water at East River, CO. On-site detection using a sensitive chemiluminescence method suggests H2O2 concentrations in groundwater ranging from 6 nM (at the most reduced region) to ~ 80 nM (in a locally oxygen-rich area) along the intra-meander transect with a maxima of 186 nM detected in the surface water in an early afternoon, lagging the maximum solar irradiance by ∼ 1.5 h. Our results suggest that the dark profile of H2O2 in the hyporheic zone is closely correlated to local redox gradients, indicating that interactions between various redox sensitive elements could play an essential role. Due to its transient nature, the widespread presence of H2O2 in the hyporheic zone indicates the existence of a sustained balance between H2O2 production and consumption, which potentially involves a relatively rapid succession of various biogeochemically important processes (such as organic matter turnover, metal cycling and contaminant mobilization). More importantly, this study confirmed the occurrence of reactive oxygen species at a subsurface redox transition zone and further support our understanding of redox boundaries on reactive oxygen species generation and as key locations of biogeochemical activity.

A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (Reco). However, accurate estimation of ET and Reco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and Reco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and Reco variables, as well as providing reliable estimation of ET and Reco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and Reco and enhances process understanding at sparsely monitored watersheds.

Spatial Distributions and Model Selections of Commercial Estuarine Fish (Sciaenidae) Populations Related to Water Quality, Chl-a, and AML in Musi River mouth, South Sumatra

Estuary and river mouth are essential habitats for many commercial estuarine fishes, including the Sciaenidae family. While recently, estuaries have been threatened by anthropogenic marine litter (AML) transported from nearby land and river. An important type of AML is plastic litter since it takes a long degradation time. In the South Sumatra Province, Indonesia, one of the vital estuaries is the Musi estuary. This paper aims to map the spatial distributions of two Sciaenids, including Panna microdon and Otolithoides pama, and Sciaenid’s environmental covariates, including water quality, chlorophyll a, and plastic litters in Musi estuary and model the correlations of Sciaenids with their covariates. The maps were developed using GIS, and the model was validated using AIC methods. The data were collected from 3 river mouths in the west, central, and east of the Musi estuary. The data showed that the populations of both Sciaenids were higher in the east river mouth rather than in the west. Sciaenid populations were positively correlated with high salinity, DO, chlorophyll a, moderate transparency, and low temperature. A high load of AML’s frequency (7.54 items/m2) and weights (36.8 gram/m2) has reduced both Sciaenid populations in the central river mouth of the estuary. In contrast, low AML loads in the east have correlated with high Sciaenid populations. Model selection based on AIC values shows the best model for P.microdon retained an effect of AML weight with AIC values of 22.591 and 28.321 for O. pama. This concludes that the weight of plastic litter in estuary water was the main limiting factor for Sciaenid populations in Musi.

Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

A ‘world of method and intrigue’: Muriel Spark's Literary Intelligence

In 1944, Muriel Spark was recruited by the Foreign Office to work as a Duty Secretary in the Political Warfare Executive at Milton Bryan. ‘I played a very small part,’ Spark wrote in her autobiography, ‘but as a fly on the wall I took in a whole world of method and intrigue in the dark field of Black Propaganda or Psychological Warfare, and the successful and purposeful deceit of the enemy.’ Drawing on research in Spark's personal and literary archives at the McFarlin Library, Tulsa, and the National Library of Scotland, this essay explores the ways in which this ‘world of method and intrigue’ is taken in and reformulated in Spark's writing. Political espionage takes centre-stage in several of Spark's fictions, and a preoccupation with secrecy and spying runs through her work. But the methods of black propaganda can also be read as a secret sharer of some of Spark's most characteristic aesthetic strategies. Focusing in particular on Spark's most direct treatment of her secret war work – The Hothouse by the East River – critical tension centres on reading Spark's literary intelligence less as a re-enactment than as a subversion of the logics of disinformation.

On the similarity of hillslope hydrologic function: a process-based approach

Hillslope similarity is an active topic in hydrology because of its importance to improve our understanding of hydrologic processes and enable comparisons and paired studies. In this study, we propose a holistic bottom-up hillslope similarity classification based on a region’s integrative hydrodynamic response quantified by the seasonal changes in groundwater levels. The main advantage of the proposed classification is its ability to describe recharge and discharge processes. We test the performance of the proposed classification by comparing it to seven other common hillslope similarity classifications. These include simple classifications based on the aridity index, topographic wetness index, elevation, land cover, and more sophisticated machine-learning classifications that jointly integrate all these data. We assess the ability of these classifications to identify and categorize hillslopes with similar static characteristics, hydroclimatic behaviors, land surface processes, and subsurface dynamics in a mountainous watershed, the East River, located in the headwaters of the Upper Colorado River Basin. The proposed classification is robust as it reasonably identifies and categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics (and hence hillslopes with similar hydrologic function). In general, the other approaches are good in identifying similarity in a single characteristic, which is usually close to the selected variable. We further demonstrate the robustness of the proposed classification by testing its ability to predict hillslope responses to wet and dry hydrologic conditions, of which it performs well when based on average conditions.

A hybrid data–model approach to map soil thickness in mountain hillslopes

Soil thickness plays a central role in the interactions between vegetation, soils, and topography, where it controls the retention and release of water, carbon, nitrogen, and metals. However, mapping soil thickness, here defined as the mobile regolith layer, at high spatial resolution remains challenging. Here, we develop a hybrid model that combines a process-based model and empirical relationships to estimate the spatial heterogeneity of soil thickness with fine spatial resolution (0.5 m). We apply this model to two aspects of hillslopes (southwest- and northeast-facing, respectively) in the East River watershed in Colorado. Two independent measurement methods – auger and cone penetrometer – are used to sample soil thickness at 78 locations to calibrate the local value of unconstrained parameters within the hybrid model. Sensitivity analysis using the hybrid model reveals that the diffusion coefficient used in hillslope diffusion modeling has the largest sensitivity among all input parameters. In addition, our results from both sampling and modeling show that, in general, the northeast-facing hillslope has a deeper soil layer than the southwest-facing hillslope. By comparing the soil thickness estimated between a machine-learning approach and this hybrid model, the hybrid model provides higher accuracy and requires less sampling data. Modeling results further reveal that the southwest-facing hillslope has a slightly faster surface soil erosion rate and soil production rate than the northeast-facing hillslope, which suggests that the relatively less dense vegetation cover and drier surface soils on the southwest-facing slopes influence soil properties. With seven parameters in total for calibration, this hybrid model can provide a realistic soil thickness map with a relatively small amount of sampling dataset comparing to machine-learning approach. Integrating process-based modeling and statistical analysis not only provides a thorough understanding of the fundamental mechanisms for soil thickness prediction but also integrates the strengths of both statistical approaches and process-based modeling approaches.

Implications of sample treatment on characterization of the riverine environmental metabolome

High-resolution mass spectrometry techniques are widely used in the environmental sciences to characterize natural organic matter and, when utilizing these instruments, researchers must make multiple decisions regarding sample pre-treatment and the instrument ionization mode. To identify how these choices alter organic matter characterization and resulting conclusions, we analyzed a collection of 17 riverine samples from East River, CO (USA) under four PPL-based Solid Phase Extraction (SPE) treatment and electrospray ionization polarity (e.g., positive and negative) combinations: SPE (+), SPE (-), non-SPE (-), and non-SPE (+). The greatest number of formula assignments were achieved with SPE-treated samples due to the removal of compounds that could interfere with ionization. Furthermore, the SPE (-) treatment captured the most formulas across the widest chemical compound diversity. In addition to a reduced number of assigned formulas, the non-SPE datasets resulted in altered thermodynamic interpretations that could cascade into incomplete assumptions about the availability of organic matter pools for microbial heterotrophic respiration. Thus, we infer that the SPE (-) treatment is the best single method for characterizing environmental organic matter pools unless the focus is on lipid-like compounds, in which case we recommend a combination of SPE (-) and SPE (+) to adequately characterize these molecules.

Hidden Processes During Seasonal Isolation of a High-Altitude Watershed

Biogeochemical processes capable of altering global carbon systems occur frequently in Earth’s Critical Zone–the area spanning from vegetation canopy to saturated bedrock–yet many of these phenomena are difficult to detect. Observation of these processes is limited by the seasonal inaccessibility of remote ecosystems, such as those in mountainous, snow- and ice-dominated areas. This isolation leads to a distinct gap in biogeochemical knowledge that ultimately affects the accuracy and confidence with which these ecosystems can be computationally modeled for the purpose of projecting change under different climate scenarios. To examine a high-altitude, headwater ecosystem’s role in methanogenesis, sulfate reduction, and groundwater-surface water exchange, water samples were continuously collected from the river and hyporheic zones (HZ) during winter isolation in the East River (ER), CO watershed. Measurements of continuously collected ER surface water revealed up to 50 μM levels of dissolved methane in July through September, while samples from 12 cm deep in the hyporheic zone at the same location showed a spring to early summer peak in methane with a strong biogenic signature (<65 μM, δ13C-CH4, −60.76‰) before declining. Continuously collected δ18O-H2O and δ2H-H2O isotopes from the water column exhibited similar patterns to discrete measurements, while samples 12 cm deep in the hyporheic zone experienced distinct fluctuations in δ18O-H2O, alluding to significant groundwater interactions. Continuously collected microbial communities in the river in the late fall and early winter revealed diverse populations that reflect the taxonomic composition of ecologically similar river systems, including taxa indicative of methane cycling in this system. These measurements captured several biogeochemical components of the high-altitude watershed in response to seasonality, strengthening our understanding of these systems during the winter months.