The influence of hyporheic fluxes on regional groundwater discharge zones

The importance of hyporheic water fluxes induced by hydromorphologic processes at the streambed scale and their consequential effects on stream ecohydrology have recently received much attention. However, the role of hyporheic water fluxes in regional groundwater discharge is still not entirely understood. Streambed-induced flows not only affect mass and heat transport in streams but are also important for the retention of solute contamination originating from deep in the subsurface, such as naturally occurring solutes as well as leakage from the future geological disposal of nuclear waste. Here, we applied a multiscale modeling approach to investigate the effect of hyporheic fluxes on regional groundwater discharge in the Krycklan catchment, located in a boreal landscape in Sweden. Regional groundwater modeling was conducted using COMSOL Multiphysics constrained by observed or modeled representations of the catchment infiltration and geological properties, reflecting heterogeneities within the subsurface domain. Furthermore, streambed-scale modeling was performed using an exact spectral solution of the hydraulic head applicable to streaming water over a fluctuating streambed topography. By comparing the flow fields of watershed-scale groundwater discharge with and without consideration of streambed-induced hyporheic flows, we found that the flow trajectories and the distribution of the travel times of groundwater were substantially influenced by the presence of hyporheic fluxes near the streambed surface. One implication of hyporheic flows is that the groundwater flow paths contract near the streambed interface, thus fragmenting the coherent areas of groundwater upwelling and resulting in narrow “pinholes” of groundwater discharge points.

Using machine learning to generate high-resolution soil wetness maps for planning forest management

<p>Open peatlands are a recognizable feature in the boreal landscape that are commonly mapped from aerial photographs. However, wet soils also occur on tree covered peatlands and in the riparian zones of forest streams and surrounding lakes. Comparisons between field data and available maps show that only 36 % of wet soils in the boreal landscape are marked on maps, making them difficult to manage. Wet soils have lower bearing capacity than dry soils and are more susceptible to soil disturbance from land-use management with heavy machinery. Topographical modelling of wet area indices has been suggested as a solution to this problem and high-resolution digital elevation models (DEM) derived from airborne LiDAR are becoming accessible in many countries. However, most of these topographical methods relies on the user to define appropriate threshold values in order to define wet areas. Soil textures, topography and climatic differences make any application difficult on a large scale. This complex landscape variability can be captured by utilizing machine learners that uses automated data mining methods to discover patterns in large data sets. By using soil moisture data from 20 000 field plots from the National Forest Inventory of Sweden, we combined information from 24 indices and ancillary environmental features using a machine learning known as extreme gradient boosting. Extreme gradient boosting used the field data to learn how to classify soil moisture and delivered high performance compared to many traditional single algorithm methods. With this method we mapped soil moisture at 2 m spatial resolution across the Swedish forest landscape in five days using a workstation with 32 cores. This new map captured 79 % (kappa 0.69) of all wet soils compared to only 36 % (kappa 0.39) captured by current maps. In addition to capture open wetlands this new map also capture riparian zones and previously unmapped cryptic wetlands underneath the forest canopy. The new maps can, for example, be used to plan hydrologically adapted buffer zones, suggest machine free zones near streams and lakes in order to prevent rutting from forestry machines to reduce sediment, mercury and nutrient loads to downstream streams, lakes and sea.</p>

A multisite dataset of near-surface soil temperature, active-layer thickness, and soil and vegetation conditions measured in northwestern Canada, 2016-2017

Quantifying and understanding spatial variation in permafrost conditions at the landscape-scale is important for land use planning and assessing the impacts of permafrost thaw. This report documents detailed field data observed at 110 sites in two areas in northwestern Canada from 2016 to 2017. One area is a northern boreal landscape near Inuvik and the other is a tundra landscape near Tuktoyaktuk. The observations include near-surface soil temperatures (Tnss) at 107 sites, and active-layer thickness, soil and vegetation conditions at 110 sites. The data set includes the original Tnss records, the calculated daily, monthly, and annual averages of Tnss, soil and vegetation conditions at these sites, and photographs taken in the field. This data set will be useful for understanding the spatial heterogeneity of permafrost and validating modelling and mapping products.

Laboratory, field, mast-borne and airborne spectral reflectance measurements of boreal landscape during spring

We publish and describe a surface spectral reflectance data record of seasonal snow (dry, wet, shadowed), forest ground (lichen, moss) and forest canopy (spruce and pine, branches) constituting the main elements of the boreal landscape. The reflectances are measured with spectro(radio)meters covering the wavelengths from visible (VIS) to short-wave infrared (SWIR) (350 to 2500 nm). In this paper, we describe the instruments used and how the spectral observations at different scales along with the concurrent in situ reference data have been collected, processed and archived. Information on the quality of the data and factors causing uncertainty are discussed. The main experimental site is located in the Sodankylä Arctic Space Centre in northern Finland (67.37∘ N, 26.63∘ E; 179 m a.s.l) and the surrounding region. The collection includes highly controlled snow and conifer branch laboratory spectral measurements, portable field spectroradiometer observations of snow and snow-free ground at different locations, and continuous mast-borne reflectance time series data of a pine forest and forest opening. In addition to the surface level spectral reflectance, data from airborne imaging spectrometer campaigns over the Sodankylä boreal forest and Saariselkä fell region at selected spectral bands are included in the collection. All measurements of the data record correspond to a typical polar-orbiting satellite observation event in the high-latitude spring season regarding their Sun or illumination source (calibrated lamp) zenith angle and close-to-nadir instrument viewing angle. For all measurement geometries, observations are given in surface reflectance quantity corresponding to the typical representation of a satellite observation quantity to facilitate their comparison with other data sources. The openly accessible spectral reflectance data at multiple scales are suitable to climate and hydrological research and remote sensing model validation and development. To facilitate easy access to the data record the four datasets described here are deposited in a permanent data repository (http://www.zenodo.org/communities/boreal_reflectances/) (Hannula et al., 2019). Each dataset of a distinct scale has its own unique DOI – laboratory: https://doi.org/10.5281/zenodo.3580078 (Hannula and Heinilä, 2018a); field: https://doi.org/10.5281/zenodo.3580825 (Heinilä et al., 2019a); mast-borne: https://doi.org/10.5281/zenodo.3580096 (Hannula and Heinilä, 2018b); and airborne: https://doi.org/10.5281/zenodo.3580451 (Heinilä, 2019a) and https://doi.org/10.5281/zenodo.3580419 (Heinilä, 2019b).