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Forests worldwide contain unique cultural traces of past human land use. Increased pressure on forest ecosystems and intensive modern forest management methods threaten these ancient monuments and cultural remains. In northern Europe, older forests often contain very old traces, such as millennia-old hunting pits and indigenous Sami hearths. Investigations have repeatedly found that forest owners often fail to protect these cultural remains and that many are damaged by forestry operations. Current maps of hunting pits are incomplete, and the locations of known pits have poor spatial accuracy. This study investigated whether hunting pits can be automatically mapped using national airborne laser data and deep learning. The best model correctly mapped 70% of all the hunting pits in the test data with an F1 score of 0.76. This model can be implemented across northern Scandinavia and could have an immediate effect on the protection of cultural remains. [ABSTRACT FROM AUTHOR]

A longstanding challenge in stream ecology is to understand how landscape configuration organizes spatial patterns of ecosystem function via lateral groundwater connections. We combined laboratory bioassays and field additions of a metabolic tracer (resazurin) to test how groundwater‐stream confluences, or "discrete riparian inflow points" (DRIPs), regulate heterotrophic microbial activity along a boreal stream. We hypothesized that DRIPs shape spatial patterns and rates of aquatic heterotrophic microbial activity by supplying labile dissolved organic matter (DOM) to streams. Laboratory bioassays showed that the potential influence of DRIPs on heterotrophic activity varied spatially and temporally, and was related to their DOM content and composition. At the reach scale, DRIP‐stream confluences elevated the spatial heterogeneity and whole‐reach rates of heterotrophic activity, especially during periods of high land–water hydrological connectivity. Collectively, our results show how the arrangement of lateral groundwater connections influence heterotrophic activity in streams with implications for watershed biogeochemical cycles. [ABSTRACT FROM AUTHOR]

Extensive use of drainage ditches in European boreal forests and in some parts of North America has resulted in a major change in wetland and soil hydrology and impacted the overall ecosystem functions of these regions. An increasing understanding of the environmental risks associated with forest ditches makes mapping these ditches a priority for sustainable forest and land use management. Here, we present the first rigorous deep learning–based methodology to map forest ditches at regional scale. A deep neural network was trained on airborne laser scanning data (ALS) and 1,607 km of manually digitized ditch channels from 10 regions spread across Sweden. The model correctly mapped 86% of all ditch channels in the test data, with a Matthews correlation coefficient of 0.78. Further, the model proved to be accurate when evaluated on ALS data from other heavily ditched countries in the Baltic Sea Region. This study leads the way in using deep learning and airborne laser scanning for mapping fine-resolution drainage ditches over large areas. This technique requires only one topographical index, which makes it possible to implement on national scales with limited computational resources. It thus provides a significant contribution to the assessment of regional hydrology and ecosystem dynamics in forested landscapes. [ABSTRACT FROM AUTHOR]

Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. When accurately scaled, terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluated seven different terrain indices at varying digital elevation model (DEM) resolutions and user-defined thresholds as well as two available soil moisture maps, using an extensive field dataset (398 plots) of soil moisture conditions registered in five classes from a survey covering a (68 km 2) boreal landscape. We found that the variation in soil moisture conditions could be explained by terrain indices, and the best predictors within the studied landscape were the depth to water index (DTW) and a machine-learning-generated map. Furthermore, this study showed a large difference between terrain indices in the effects of changing DEM resolution and user-defined thresholds, which severely affected the performance of the predictions. For example, the commonly used topographic wetness index (TWI) performed best on a resolution of 16 m, while TWI calculated on DEM resolutions higher than 4 m gave inaccurate results. In contrast, depth to water (DTW) and elevation above stream (EAS) were more stable and performed best on 1–2 m DEM resolution. None of the terrain indices performed best on the highest DEM resolution of 0.5 m. In addition, this study highlights the challenges caused by heterogeneous soil types within the study area and shows the need of local knowledge when interpreting the modelled results. The results from this study clearly demonstrate that when using terrain indices to represent soil moisture conditions, modelled results need to be validated, as selecting an unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results. [ABSTRACT FROM AUTHOR]

The digitized vector lines have no width so we utilized average ditch width from a field inventory where 2188 ditch channels were surveyed across Sweden. Should we follow the forest industry's recommendation to increase ditch-cleaning to maintain high forest biomass production, restore them by blocking the artificial channels, use alternative continuous cover forestry techniques to manage water tables, or leave ditches to develop freely? Water is the primary medium through which society will experience the effects of climate change. [Extracted from the article]

Soil moisture has important implications for drought and flooding forecasting, forest fire prediction and water supply management. However, mapping soil moisture has remained a scientific challenge due to forest canopy cover and small-scale variations in soil moisture conditions. Digital terrain analysis has been suggested as a way forward to model soil moisture variability across landscapes, and multiple digital terrain indices have been developed. However, the performance of these terrain indices depends on the resolution of the digital elevation models used and, in many cases, user-defined index-specific thresholds. In this study, we compared soil moisture predictions using nine different terrain indices and available soil wetness maps, at varying resolutions and user-defined thresholds, with a field dataset of soil moisture registered in five classes from a forest survey covering a boreal landscape. We found that topography could explain the spatial variation in soil moisture conditions but the effects of changing DEM resolution and user-defined thresholds severely affected the performance of the soil moisture modelling. These results demonstrate that modelled soil moisture conditions need to be validated, as selecting unsuitable DEM resolution or user-defined threshold can give ambiguous and even incorrect results. Challenges caused by heterogeneous soil types within the study area highlight the need for local knowledge when interpreting the modelled results. [ABSTRACT FROM AUTHOR]

Drainage networks delineated from Digital Elevation Models (DEMs), are the basis for the modelling of geomorphological and hydrological processes, biogeochemical cycling, and water resources management. Besides providing effective models of water flows, automatically extracted drainage networks based on topography can diverge from reality to varying degrees. The variability of such disagreement within catchments has rarely been examined as a function of the heterogeneity of land cover, soil type, and slope in the catchment of interest. This research gap might not only substantially limit our knowledge of the uncertainty of hydrological prediction, but can also cause problems for users attempting to use the data at a local scale. Using 1:100000 scale land cover maps, Quaternary deposits maps, and 2 m resolution DEMs, it is found that the accuracy of delineated drainage networks tends to be lower in areas with denser vegetation, lower hydraulic conductivity, and higher erodibility. The findings of this study could serve as a guide for the more thoughtful usage of delineated drainage networks in environmental planning, and in the uncertainty analysis of hydrological and biochemical predictions. Therefore, this study makes a first attempt at filling the knowledge gap described above. [ABSTRACT FROM AUTHOR]

Streams are important emitters of CO2 but extreme spatial variability in their physical properties can make upscaling very uncertain. Here, we determined critical drivers of stream CO2 evasion at scales from 30 to 400 m across a 52.5 km2 catchment in northern Sweden. We found that turbulent reaches never have elevated CO2 concentrations, while less turbulent locations can potentially support a broad range of CO2 concentrations, consistent with global observations. The predictability of stream pCO2 is greatly improved when we include a proxy for soil‐stream connectivity. Catchment topography shapes network patterns of evasion by creating hydrologically linked "domains" characterized by high water‐atmosphere exchange and/or strong soil‐stream connection. This template generates spatial variability in the drivers of CO2 evasion that can strongly bias regional and global estimates. To overcome this complexity, we provide the foundations of a mechanistic framework of CO2 evasion by considering how landscape process domains regulate transfer and supply. [ABSTRACT FROM AUTHOR]

The history of mining and smelting and the associated pollution have been documented using lake sediments for decades, but the broader ecological implications are not well studied. We analyzed sediment profiles covering the past ~10,000 years from three lakes associated with an iron blast furnace in central Sweden, as an example of the many small-scale furnaces with historical roots in the medieval period. With a focus on long-term lake-water quality, we analyzed multiple proxies including geochemistry, pollen and charcoal, diatom composition and inferred pH, biogenic silica (bSi), visible near-infrared spectroscopy (VNIRS)-inferred lake-water total organic carbon (LW-TOC), and VNIRS-inferred sediment chlorophyll (sed-Chl). All three lakes had stable conditions during the middle Holocene (~5000 BCE to 1110 CE) typical of oligo-dystrophic lakes: pH 5.4–5.6, LW-TOC 15–18 mg L−1. The most important diatom taxa include, for example, Aulacoseira scalaris, Brachysira neoexilis, and Frustulia saxonica. From ~1150 CE, decreases in LW-TOC, bSi, and sed-Chl in all three lakes coincide with a suite of proxies indicating disturbance associated with local, small-scale agriculture, and the more widespread use of the landscape in the past (e.g. forest grazing, charcoal production). Most important was a decline in LW-TOC by 30–50% in the three lakes prior to the 20th century. In addition, the one lake (Fickeln) downstream of the smelter and main areas of cultivation experienced a shift in diatom composition (mainly increasing Asterionella formosa) and a 0.6 pH increase coinciding with increasing cereal pollen and signs of blast furnace activity. The pH did not change in the other two lakes in response to disturbance; however, these lakes show a slight increase (0.3–0.5 pH units) because of modern liming. LW-TOC has returned to background levels in the downstream lake and remains lower in the other two. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) concentrations in lakes vary strongly over time. This variability is rarely captured by environmental monitoring but is crucial for accurately assessing the magnitude of lake CO2 emissions. However, it is unknown to what extent temporal variability needs to be captured to understand important drivers of lake carbon cycling such as climate and land management. We used environmental monitoring data of Swedish forest lakes collected in autumn (n = 439) and throughout the whole open water season (n = 22) from a wet and a dry year to assess temporal variability in effects of climate and forestry on CO2 concentrations across lakes. Effects differed depending on the season and year sampled. According to cross‐lake comparisons based on autumn data, CO2 concentrations increased with annual mean air temperature (dry year) or catchment forest productivity (wet year) but were not related to colored dissolved organic matter concentrations. In contrast, open water‐season averaged CO2 concentrations were similar across temperature and productivity gradients but increased with colored dissolved organic matter. These contradictions resulted from scale mismatches in input data, lead to weak explanatory power (R2 = 9–32%), and were consistent across published data from 79 temperate, boreal, and arctic lakes. In a global survey of 144 published studies, we identified a trade‐off between temporal and spatial coverage of CO2 sampling. This trade‐off clearly determines which conclusions are drawn from landscape‐scale CO2 assessments. Accurate evaluations of the effects of climate and land management require spatially and temporally representative data that can be provided by emerging sensor technologies and forms of collaborative sampling. Plain Language Summary: Lakes interact with the atmosphere by exchanging greenhouse gases such as carbon dioxide that have implications for the global climate. In order to predict future carbon dioxide exchanges, the effects of climate and land management need to be determined. Here we show that the apparent effects of climate and forestry on carbon dioxide concentrations in temperate, boreal, and arctic lakes are inconsistent and depend on whether lakes are sampled during autumn or throughout the year and whether it was a wet or dry year. To accurately quantify effects, hundreds of lakes would need to be sampled at least six times per year. Published literature suggests that this is unrealistic. This implies a need for new automated measurement technologies and collaborative teamwork to provide representative data and allow accurate predictions of the effects of climate and land management. Key Points: Snapshot sampling is commonly used despite strong temporal variability in lake CO2 concentrationsThe sampling scheme strongly influences inferred patterns in carbon dioxide concentrations along environmental gradientsPredictions of the effects of climate and land management on lake carbon cycling should be based on temporally representative data [ABSTRACT FROM AUTHOR]

With the introduction of high-resolution digital elevation models, it is possible to use digital terrain analysis to extract small streams. In order to map streams correctly, it is necessary to remove errors and artificial sinks in the digital elevation models. This step is known as preprocessing and will allow water to move across a digital landscape. However, new challenges are introduced with increasing resolution because the effect of anthropogenic artefacts such as road embankments and bridges increases with increased resolution. These are problematic during the preprocessing step because they are elevated above the surrounding landscape and act as artificial dams. The aims of this study were to evaluate the effect of different preprocessing methods such as breaching and filling on digital elevation models with different resolutions (2, 4, 8, and 16 m) and to evaluate which preprocessing methods most accurately route water across road impoundments at actual culvert locations. A unique dataset with over 30,000 field-mapped road culverts was used to assess the accuracy of stream networks derived from digital elevation models using different preprocessing methods. Our results showed that the accuracy of stream networks increases with increasing resolution. Breaching created the most accurate stream networks on all resolutions, whereas filling was the least accurate. Burning streams from the topographic map across roads from the topographic map increased the accuracy for all methods and resolutions. In addition, the impact in terms of change in area and absolute volume between original and preprocessed digital elevation models was smaller for breaching than for filling. With the appropriate methods, it is possible to extract accurate stream networks from high-resolution digital elevation models with extensive road networks, thus providing forest managers with stream networks that can be used when planning operations in wet areas or areas near streams to prevent rutting, sediment transport, and mercury export. [ABSTRACT FROM AUTHOR]

This study focuses on avoiding negative effects on surface waters using new techniques for identifying wet areas near surface waters. This would aid planning and designing of forest buffer zones and off-road forestry traffic. The temporal variability in the geographical distribution of the stream network renders this type of planning difficult. A field study was performed in the 68 km2 Krycklan Catchment to illustrate the variability of a boreal stream network. The perennial stream length was 140 km while the stream length during high-flow conditions was 630 km. Comparing the field-measured stream network to the network presented on current maps showed that 58% of the perennial and 76% of the fully expanded network was missing on current maps. Similarly, cartographic depth-to-water maps showed that associated wet soils constituted 5% of the productive forest land during baseflow and 25% during high flow. Using a new technique, maps can be generated that indicate full stream networks, as well as seasonally active streams and associated wet soils, thus, forestry planning can be performed more efficiently and impacts on surface waters can be reduced. [ABSTRACT FROM AUTHOR]