Climate change is leading to river ice thinning and shorter ice cover durations, posing significant risks to travel safety and ecosystem health. Due to limited in-situ observations in Alaska, models and remote sensing are employed to understand changing river conditions. This study conducts a comparative evaluation of statistical, machine learning, and remote sensing techniques to assess river ice presence and thickness across Alaska and the Yukon River basin. Sentinel-1 synthetic aperture radar data, climate model outputs, and in-situ river ice observations throughout Alaska are used to evaluate the regional applications of these techniques for determining river ice phenology and thickness. Our analysis reveals that ice presence can be accurately identified using Sentinel-1 images and climate data processed through machine learning models, achieving high accuracy across Alaska. Predicting ice break-up and freeze-up with these methods also yields high accuracy, with a root mean square error (RMSE) of 5.3 and 15.0 days, respectively, for machine learning at out-of-sample locations. Statistical, machine learning, and remote sensing techniques each demonstrated similar performance in determining ice thickness, with RMSEs ranging from 18 to 23 cm for out-of-sample years or locations. However, an ensemble of these methods significantly reduced the RMSE to 13 cm. Using the best-performing models, we generated high-resolution estimates of river ice phenology and thickness for major rivers in Alaska. The ensemble river ice thickness methods and the machine learning ice presence model show promise for widespread application in diverse regions, facilitating environmental monitoring and enhancing river ice safety.

Record high temperatures were documented in the McMurdo Dry Valleys, Antarctica, on March 18, 2022, exceeding average temperatures for that day by nearly 30°C. Satellite imagery and stream gage measurements indicate that surface wetting coincided with this warming more than two months after peak summer thaw and likely exceeded thresholds for rehydration and activation of resident organisms that typically survive the cold and dry conditions of the polar fall in a freeze-dried state. Such events may be a harbinger of future climate conditions characterized by warmer temperatures and greater thaw in this region of Antarctica, which could influence the distribution, activity, and abundance of sentinel taxa. Here we describe the ecological responses to this weather anomaly reporting on meteorological and hydrological measurements across the region and on biological observations from Canada Stream, one of the most diverse and productive ecosystems within the McMurdo Dry Valleys.

To estimate whole-stream metabolism, the open-channel oxygen method has traditionally provided underlying assumptions for modeled estimates of gross primary productivity (GPP) and ecosystem respiration (ER). The open-channel oxygen method employs the diel dissolved oxygen (DO) curve, which attributes stream metabolism to four processes: photosynthesis by primary producers, oxidative respiration, reaeration, and groundwater flux. Of these processes, groundwater flux is often assumed to be negligible when modeling whole-stream metabolism, which may introduce bias in estimates of GPP and ER. For example, if net groundwater flux is into the main channel, we may expect an overestimation of modeled ER due to dissolved oxygen dilution effects from influent groundwater. Although this error is recognized, there is a lack of continuous and spatial data that quantifies the extent of bias that is introduced by not including groundwater flux in model parameters. To investigate this bias, we measured whole-stream metabolism and groundwater flux in Como Creek, a headwater catchment 26 km west of Boulder, CO. DO sensors were deployed in the stream and groundwater wells in June 2018 at 3 sites along 500 m of the reach. BASE (Bayesian Single-station Estimation), a package available through R, was used for modeling whole-stream metabolism between peak streamflow and baseflow. BASE also optimizes the reaeration coefficient, which was estimated both including and neglecting groundwater discharge and DO concentration. Preliminary results indicate that Como Creek has a net groundwater flux out of the stream, resulting in higher rates of GPP in the groundwater-corrected model output, and indicating the potential for bias in uncorrected models.

The relationship between dissolved solute concentration (C) and discharge (q) in streams, i.e., the C-q relationship, is a useful diagnostic tool for understanding biogeochemical processes in watersheds. In the ephemeral glacial meltwater streams of the McMurdo Dry Valleys [MDVs], Antarctica, studies show significant chemostatic relationships for weathering solutes and NO3-. Dissolved organic carbon (DOC) concentrations here are low compared to temperate streams, in the range of 0.1 to 2 mg C L-1, and their chemical signal clearly indicates derivation from microbial biomass. Many MDV streams support abundant microbial mats, which are also a source of organic matter to underlying hyporheic sediments. We investigated whether the DOC generation rate from these autochthonous organic matter pools was sufficient to maintain chemostasis for DOC despite these streams’ large diel and interannual fluctuations in discharge. To evaluate the DOC-q relationship, we fit the long-term DOC-q data to two models: a power law and an advection-reaction model. By using model outputs and other common metrics to characterize the DOC-q relationship, we found that this relationship is chemostatic in several MDV streams. We propose a conceptual model in which hyporheic carbon storage, hyporheic exchange rates, and net DOC generation rates are key interacting components that enable chemostatic DOC-q behavior in MDV streams. This model clarifies the role of autochthonous carbon stores in maintaining DOC-q chemostasis and may be useful for examining these relationships in temperate systems, where autochthonous organic carbon is readily bioavailable but where its signal is masked by a larger allochthonous signal.

Climate projections suggest that snowfall-dominated areas will decline substantially in the coming decades. Such climate impacts are already being observed in Colorado where the dominant source of annual peak discharge is shifting from snowmelt to rainfall, altering the paths by which water flows through a landscape and is ultimately delivered to streams. Observed climate driven shifts in stream flow dynamics and permanence highlight the increasing importance of understanding the hydrologic connectivity of uplands to streams in lower elevation, montane ecoregions. We collected geochemical and hydrometric data over three years to quantify hydrologic connectivity of uplands to a montane headwater stream at the Manitou Experimental Forest in central Colorado. We use a combined approach of concentration-discharge relationships and end-member mixing analysis, paired with high resolution measurements of soil moisture, precipitation, and groundwater levels to characterize source areas to the stream in 3-dimensions: longitudinal, lateral, and vertical. Samples were collected and measurements were recorded along the stream profile (longitudinal), from groundwater wells and soil lysimeters installed with increasing distance from the stream (lateral), and from shallow versus deep groundwater wells and soil moisture measured at different depths (vertical). Results indicate distinct differences in stream chemistry along the longitudinal stream profile, with highest concentrations at the most upstream sites and lowest concentrations at the most downstream sites. Stream solute concentrations increased with decreasing stream discharge values from spring to late summer. However, the stream remained chemostatic during all recorded rain storms, suggesting a difference in flow pathways during individual summer storm pulses. End member mixing analysis suggests spatiotemporal differences in shallow and deep vertical source areas, and between riparian and upland sources to the stream. These results provide a promising step towards quantifying the expansion and contraction of runoff source areas to a montane headwater stream.

Concentration-discharge (C-Q) relationships can provide insight into how catchments store and transport solutes, but analysis is often limited to long-term behavior assessed from infrequent grab samples. Increasing availability of high-frequency sensor data has shown that C-Q relationships can vary substantially across temporal scales, and in response to different hydrologic drivers. Here, we present four years of dissolved organic carbon (DOC) and nitrate-nitrogen (NO3-N) sensor data from a snowmelt- dominated catchment in the Rocky Mountains of Colorado. We assessed both the direction (enrichment vs. dilution) and hysteresis in C-Q relationships across a range of time scales, from interannual to sub-daily. Both solutes exhibited a seasonal flushing response, with concentrations initially increasing as solute stores are mobilized by the melt pulse, but then declining as these stores are depleted. The high-frequency data revealed that the seasonal melt pulse was composed of numerous individual daily melt pulses. The solute response to daily melt pulses was relatively chemostatic, suggesting mobilization and depletion to be progressive rather than episodic processes. In contrast, rainfall-induced pulses produced short-lived but substantial enrichment responses, suggesting they may activate alternative solute sources or transport pathways. The results clearly demonstrate that solute responses to individual events may differ significantly from the longer-term behavior these events combine to generate, something which only becomes apparent when high-frequency data are used.