Hyporheic exchange has the potential to significantly influence river temperatures in regions of continuous permafrost under low‐flow conditions given the strong thermal gradients that exist in river ...bed sediments. However, there is limited understanding of the impacts of hyporheic exchange on Arctic river temperatures. To address this knowledge gap, heat fluxes associated with hyporheic exchange were estimated in a fourth‐order Arctic river using field observations coupled with a river temperature model that accounts for hyporheic exchange influences. Temperature time series and tracer study solute breakthrough curves were measured in the main channel and river bed at multiple locations and depths to characterize hyporheic exchange and provide parameter bounds for model calibration. Model results for low‐flow periods from 3 years indicated that hyporheic exchange contributed up to 27% of the total river energy balance, reduced the main channel diel temperature range by up to 1.7 °C, and reduced mean daily temperatures by up to 0.21 °C over a 13.1‐km study reach. These influences are due to main channel heat loss during the day and gain at night via hyporheic exchange and heat loss from the hyporheic zone to the ground below via conduction. Main channel temperatures were found to be sensitive to simulated changes in ground temperatures due to changes in hyporheic exchange heat flux and deeper ground conduction. These results suggest that the moderating influence of hyporheic exchange could be reduced if ground temperatures warm in response to projected increases in permafrost thaw below rivers.
Key Points
Hyporheic exchange heat fluxes significantly affect low‐order Arctic river temperature under low‐flow conditions
Hyporheic exchange is a net heat sink and reduces the amplitude of diel river temperature variations
Model results suggest that warming of deep sediments would reduce heat sink effects of hyporheic exchange
Groundwater flow regimes in the seasonally thawed soils in areas of continuous permafrost are relatively unknown despite their potential role in delivering water, carbon, and nutrients to streams. ...Using numerical groundwater flow models informed by observations from a headwater catchment in arctic Alaska, United States, we identify several mechanisms that result in substantial surface‐subsurface water exchanges across the land surface during downslope transport and create a primary control on dissolved organic carbon loading to streams and rivers. The models indicate that surface water flowing downslope has a substantial groundwater component due to rapid surface‐subsurface exchanges across a range of hydrologic states, from unsaturated to flooded. Field‐based measurements corroborate the high groundwater contributions, and river dissolved organic carbon concentrations are similar to that of groundwater across large discharge ranges. The persistence of these groundwater contributions in arctic watersheds will influence carbon export to rivers as thaw depth increases in a warmer climate.
Plain Language Summary
This paper shows that groundwater processes have a dominant role in controlling carbon export from the land to streams in permafrost terrain. We use hydrologic models to show that microtopography on the land surface drives the rapid exchange of overland flow with shallow groundwater. In other words, the water (porpoises) from just above to just below the land surface and back again as it moves downslope. Combined with the rapid leaching of organic carbon from soils, these findings provide a mechanistic explanation for two decades of measurements showing high concentrations of carbon in soils and streams during high flow conditions for both spring snowmelt and summer storms. During drier time periods, groundwater contributions from the thin thawed layer make up the flow in streams and keep dissolved organic carbon concentrations high. The persistence of these groundwater contributions in arctic watersheds will influence carbon export to rivers as thaw depth increases in a warmer climate.
Key Points
Surface‐subsurface water exchanges across the landscape control solute export from watersheds
Overland flow water contains substantial amounts of groundwater
Constant stream DOC over large flow ranges and varied active layer thaw corroborate the role of surface‐subsurface exchanges in DOC export
Remote sensing of river discharge promises to augment in situ gauging stations, but the majority of research in this field focuses on large rivers (>50 m wide). We present a method for estimating ...volumetric river discharge in low‐order (<50 m wide) rivers from remotely sensed data by coupling high‐resolution imagery with one‐dimensional hydraulic modeling at so‐called virtual gauging stations. These locations were identified as locations where the river contracted under low flows, exposing a substantial portion of the river bed. Topography of the exposed river bed was photogrammetrically extracted from high‐resolution aerial imagery while the geometry of the remaining inundated portion of the channel was approximated based on adjacent bank topography and maximum depth assumptions. Full channel bathymetry was used to create hydraulic models that encompassed virtual gauging stations. Discharge for each aerial survey was estimated with the hydraulic model by matching modeled and remotely sensed wetted widths. Based on these results, synthetic width‐discharge rating curves were produced for each virtual gauging station. In situ observations were used to determine the accuracy of wetted widths extracted from imagery (mean error 0.36 m), extracted bathymetry (mean vertical RMSE 0.23 m), and discharge (mean percent error 7% with a standard deviation of 6%). Sensitivity analyses were conducted to determine the influence of inundated channel bathymetry and roughness parameters on estimated discharge. Comparison of synthetic rating curves produced through sensitivity analyses show that reasonable ranges of parameter values result in mean percent errors in predicted discharges of 12%–27%.
Key Points
Channel bathymetry was photogrammetrically derived from aerial imagery
River discharge was estimated by coupling aerial imagery with a 1‐D hydraulic model
Minimal knowledge of the system was required to remotely sense river discharge
Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with ...harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.
Display omitted
•Hyperspectral imaging system with microscope used to measure algal endmember spectra•Multiple endmember spectral mixture analysis (MESMA) used to distinguish among genera•Pairwise spectral separability of 12 genera evaluated using simulated mixtures•Fractional abundance of each endmember from MESMA of hyperspectral satellite images•Cyanobacteria genera detected in images match field data, including toxigenic strains
Lateral inflows control the spatial distribution of river discharge, and understanding their patterns is fundamental for accurately modelling instream flows and travel time distributions necessary ...for evaluating impacts of climate change on aquatic habitat suitability, river energy budgets, and fate of dissolved organic carbon. Yet, little is known about the spatial distribution of lateral inflows in Arctic rivers given the lack of gauging stations. With a network of stream gauging and meteorological stations within the Kuparuk River watershed in northern Alaska, we estimated precipitation and lateral inflows for nine subcatchments from 1 July to 4 August,2013, 2014, and 2015. Total precipitation, lateral inflows, runoff ratios (area‐normalized lateral inflow divided by precipitation), percent contribution to total basin discharge, and lateral inflow per river kilometre were estimated for each watershed for relatively dry, moderate, or wet summers. The results show substantial variability between years and subcatchments. Total basin lateral inflow depths ranged 24‐fold in response to a threefold change in rainfall between dry and wet years, whereas within‐basin lateral inflows varied fivefold from the coastal plain to the foothills. General spatial trends in lateral inflows were consistent with previous studies and mean summer precipitation patterns. However, the spatially distributed nature of these estimates revealed that reaches in the vicinity of a spring‐fed surficial ice feature do not follow general spatial trends and that the coastal plain, which is typically considered to produce minimal runoff, showed potential to contribute to total river discharge. These findings are used to provide a spatially distributed understanding of lateral inflows and identify watershed characteristics that influence hydrologic responses.
Lateral inflow was estimated for three summers in the Kuparuk River watershed (A), located in a region of continuous permafrost (B), which was divided into nine subcatchments including two tributaries, the Toolik River (TR)and Imnavait Creek (IC)(Aand C). Lateral inflows increased with distance from the ocean and showed strong intrabasin variability in the vicinity of the Kuparuk River aufeis (Dand E). These estimates of lateral inflows can improve hydrologic, habitat, and carbon process modelling.
Water temperature controls in low arctic rivers King, Tyler V.; Neilson, Bethany T.; Overbeck, Levi D. ...
Water resources research,
June 2016, 2016-06-00, 20160601, Letnik:
52, Številka:
6
Journal Article
Recenzirano
Understanding the dynamics of heat transfer mechanisms is critical for forecasting the effects of climate change on arctic river temperatures. Climate influences on arctic river temperatures can be ...particularly important due to corresponding effects on nutrient dynamics and ecological responses. It was hypothesized that the same heat and mass fluxes affect arctic and temperate rivers, but that relative importance and variability over time and space differ. Through data collection and application of a river temperature model that accounts for the primary heat fluxes relevant in temperate climates, heat fluxes were estimated for a large arctic basin over wide ranges of hydrologic conditions. Heat flux influences similar to temperate systems included dominant shortwave radiation, shifts from positive to negative sensible heat flux with distance downstream, and greater influences of lateral inflows in the headwater region. Heat fluxes that differed from many temperate systems included consistently negative net longwave radiation and small average latent heat fluxes. Radiative heat fluxes comprised 88% of total absolute heat flux while all other heat fluxes contributed less than 5% on average. Periodic significance was seen for lateral inflows (up to 26%) and latent heat flux (up to 18%) in the lower and higher stream order portions of the watershed, respectively. Evenly distributed lateral inflows from large scale flow differencing and temperatures from representative tributaries provided a data efficient method for estimating the associated heat loads. Poor model performance under low flows demonstrated need for further testing and data collection to support the inclusion of additional heat fluxes.
Key Points:
Dominant heat fluxes in a low arctic river vary with streamflow
Lateral inflows are more critical in lower order headwater portions of the basin
Accurate low flow predictions require additional information and/or heat fluxes
Lake trophic state is a key ecosystem property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, ...standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.
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