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The snow cover has a major influence on the wintertime surface energy budget. Accurate simulation of the snowpack energy fluxes is difficult due to limitations in the parameterization of turbulent fluxes under stable conditions and landscape properties (e.g. canopy and topography) that complicate the radiation budget. In fact, description of turbulent fluxes is subject to major uncertainties in snow modelling, and simulating snow in forests is critical for hydrological and climate modelling. Yet, detailed studies that evaluate the models with surface energy flux observations at high latitudes are rare. In this study, we evaluate components of the SURFEX land surface model on four eddy covariance sites in Finland. These sites cover two different climate and snow conditions, the southern and northern subarctic zone, and two different boreal landscape types, peatland and forest. On the peatland sites, we evaluate the sensitivity of simulated surface energy fluxes and snow conditions to different process parameterizations (e.g. snow processes and turbulent exchange) implemented in the detailed snowpack model Crocus. On the forest sites, we examine alternative approaches to represent the energy and mass budgets of the soil and vegetation with the ISBA and MEB models, and assess their performance in simulating energy fluxes, snow conditions and soil thermal regimes. We show that the turbulent fluxes under stable conditions simulated by the default stability correction function do not match the observed values, and thus, it is necessary to increase the simulated turbulent exchange under stable conditions. Moreover, we demonstrate that explicit vegetation is required to concurrently simulate accurate surface heat fluxes and snow/soil conditions in forests. Our results have larger implications for choosing suitable model parameterizations and structures depending on the use case of interest.

Digital twins are part of ongoing digital transformation to test, monitor, and maintain physical environments virtually. The collaboration of smart measurement sensors, advanced communication networks, cloud data storage capacity, and cutting-edge computing techniques has the potential to create a digital twin of a river basin with greater physical, spatial, and temporal scalability. The digital twin is defined as a realistic virtual representation of the physical river basin that aids in improved decision-making through real-time data connectivity, association, and relationship. Because of the continuous bidirectional interactions between virtual and physical entities, the digital twin is unique to the physical river basin. The digital twin has the advantage of adapting to changing real-time river basin characteristics, resulting in increased operational efficiency, better uncertainty quantification, early warning detection, and identification of emergency management. By imagining smart river basin management via the digital twin concept, we are venturing into uncharted territory, with the goal of improving the ecological status of a river basin by balancing environmental and socioeconomic interdependence while minimizing natural resource depletion. This poster provides an overview of the concept, framework, methodology, and challenges involved in developing a digital twin of a river basin. The framework’s six dimensions are river basin, data, modeling, infrastructure, service, and connectivity. The methodology emphasizes the digital twin’s purpose identification, maturity spectrum, workflow architecture, technical core, data layers, model simulations, knowledge creation, and effective application. We discuss the key services provided by the digital twin for the river basin, as well as its future prospects for autonomous control in the physical river basin.

The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy budget is challenging due to e.g. vegetation and topography that complicate the radiation budget, and limitations in theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models (LSMs) against detailed observations of all surface energy components at high latitudes are scarce. In this study, we compared different configurations of SURFEX LSM model against surface energy flux, snow depth and soil temperature observations from four eddy covariance stations in Finland. The sites cover two different climate and snow conditions, representing the southern and northern subarctic zones, and the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested the sensitivity of surface energy fluxes to different process parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and assess their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that using a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible and latent heat fluxes over snow. For accurate simulations of surface heat fluxes and snow/soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we found the peat soil temperature profile simulations to be greatly improved with realistic soil texture (soil organic carbon) parameterization. Although we focused on models within the SURFEX LSM platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases.

Detailed information on seasonal snow cover and depth is essential to the understanding of snow processes, operational forecasting, and as input for hydrological models. Recent advances in unmanned aircraft systems (UASs) and structure from motion (SfM) techniques have enabled low-cost monitoring of spatial snow depth distribution in resolutions up to a few centimeters. Here, we study the spatiotemporal variability of snow depth and interactions between snow and vegetation in different subarctic landscapes consisting of a mosaic of conifer forest, mixed forest, transitional woodland/shrub, and peatland areas. To determine the spatiotemporal variability of snow depth, we used high-resolution (50 cm) snow depth maps generated from repeated UAS-SfM surveys in the winter of 2018/2019 and a snow-free bare ground survey after snowmelt. Due to poor sub-canopy penetration with the UAS-SfM method, tree masks were utilized to remove canopy areas and the area (36 cm) immediately next to the canopy before analysis. Snow depth maps were compared to the in-situ snow course and a single-point continuous ultrasonic snow depth measurement. Based on the results, the difference between the UAS-SfM survey median snow depth and single-point measurement increased for all land cover types during the snow season, from +5 cm at the beginning of the accumulation to -16 cm in coniferous forests and -32 cm in peatland during the melt period. This highlights the poor representation of point measurements even on the sub-catchment scale. The high-resolution snow depth maps agreed well with the snow course measurement, but the spatial extent and resolution of maps were substantially higher. The snow depth variability (5–95 percentiles) within different land cover types increased from 17 cm to 42 cm in peatlands and from 33 cm to 49 cm in the coniferous forest from the beginning of the snow accumulation to the melt period. Both the median snow depth and its variability were found to increase with canopy density; this increase was greatest in the conifer forest area, followed by mixed forest, transitional woodland/shrub, and open peatlands. Using the high spatial resolution data, we found a systematic increase (2–20 cm), then a decline of snow depth near the canopy with increasing distance (from 1 m to 2.5 m) of the peak value through the snow season. This study highlights the potential of the UAS-SfM in high-resolution monitoring of snow depth in multiple land cover types and snow-vegetation interactions in subarctic and remote areas where field data is not available.

Snow conditions in the northern hemisphere are rapidly changing, and information on snow depth is critical for decision-making and other societal needs. Unmanned aircraft systems (UASs) can offer data resolutions of a few centimeters at a catchment-scale, and thus provide a low-cost solution to bridge the gap between sparse manual probing and low-resolution satellite data. In this study, we present a series of snow depth measurements using different UAS platforms throughout the winter in the Finnish subarctic site Pallas, which has a heterogeneous landscape. We discuss the different platforms, the methods utilized, difficulties working in the harsh northern environment, and the results and their accuracy compared to in situ measurements. Generally, all UASs produced spatially representative estimates of snow depth in open areas after reliable georeferencing by using the Structure from Motion (SfM) photogrammetry technique. However, significant differences were observed in the accuracies produced by the different UASs compared to manual snow depth measurements, with overall RMSEs varying between 13.0 to 25.2 cm, depending on the UAS. Additionally, a reduction in accuracy was observed when moving from an open mire area to forest covered areas. We demonstrate the potential of low-cost UASs to efficiently map snow surface conditions, and we give some recommendations on UAS platform selection and operation in a harsh subarctic environment with variable canopy cover.

As far as I can tell, no abstract is necessary for HPEye submissions

A soil moisture estimation method was developed for Sentinel-1 synthetic aperture radar (SAR) ground range detected high resolution (GRDH) data to analyze moisture conditions in a gently undulating and heterogeneous subarctic area containing forests, wetlands, and open orographic tundra. In order to preserve the original 10-m pixel spacing, PIMSAR (pixel-based multitemporal nonlocal averaging) nonlocal mean filtering was applied. It was guided by multitemporal statistics of SAR images in the area. The gradient boosted trees (GBT) machine learning method was used for the soil moisture algorithm development. Discrete and continuous in situ soil moisture values were used for training and validation of the algorithm. For surface soil moisture, the root mean square error (RMSE) of the method was 6.5% and 8.8% for morning and evening images, respectively. The corresponding maximum errors were 34.1% and 33.8%. The pixelwise sensitivity to the training set and method choice was estimated as the variance of the soil moisture values derived using the algorithms for the three best methods with respect to the criteria: the smallest maximum error, the smallest RMSE value, and the highest coefficient of determination (R-2) value. It was, on average, 6.3% with a standard deviation of 5.7%. Our approach successfully produced instantaneous high-resolution soil moisture estimates on daily basis for the subarctic landscape and can further be applied to various hydrological, biogeochemical, and management purposes.

Subarctic ecohydrological processes are changing rapidly, but detailed and integrated ecohydrological investigations are not as widespread as necessary. We introduce an integrated research catchment site (Pallas) for atmosphere, ecosystems, and ecohydrology studies in subarctic conditions in Finland that can be used for a new set of comparative catchment investigations. The Pallas site provides unique observational data and high-intensity field measurement datasets over long periods. The infrastructure for atmosphere- to landscape-scale research in ecosystem processes in a subarctic landscape has recently been complemented with detailed ecohydrological measurements. We identify three dominant processes in subarctic ecohydrology: (a) strong seasonality drives ecohydrological regimes, (b) limited dynamic storage causes rapid stream response to water inputs (snowmelt and intensive storms), and (c) hydrological state of the system regulates catchment-scale dissolved carbon dynamics and greenhouse (GHG) fluxes. Surface water and groundwater interactions play an important role in regulating catchment-scale carbon balances and ecosystem respiration within subarctic peatlands, particularly their spatial variability in the landscape. Based on our observations from Pallas, we highlight key research gaps in subarctic ecohydrology and propose several ways forward. We also demonstrate that the Pallas catchment meets the need for sustaining and pushing the boundaries of critical long-term integrated ecohydrological research in high-latitude environments.

Catchment storage sustains ecologically important low flows in headwater systems. Understanding the factors controlling storage is essential in analysis of catchment vulnerability to global change. We calculated catchment storage and storage sensitivity of streamflow for 61 boreal headwater catchments in Finland. We also explored the connection between computed storage indices and low flow conditions. The relationships between selected climate, snow, and catchment characteristics and calculated storage properties and low flows were investigated, in order to assess the importance of different factors that render catchments vulnerable to climate and environmental change. We found that the most sensitive areas to climate change were located in the southern boreal coastal zone, with fine‐grained soils and agricultural areas. In contrast, catchments in the middle and northern boreal zone, with till and peatland soils and higher snow water equivalent values, were less sensitive under current conditions. In addition, we found a threshold at a snow to precipitation ratio of 0.35. Above that threshold, summer low flows were generally sensitive to changes in snow conditions, whereas below that threshold catchment characteristics gained importance and the sensitivity was more directly related to changes in temperature and timing of rainfall. These findings suggest that a warming climate will have pronounced impacts on hydrology and catchment sensitivity related to snow quantity and snow cover duration in certain snow to precipitation ratio zones. Moreover, land use activities had an impact on storage properties in agricultural and drained peatland areas, resulting in a negative effect on low flows.

Due to the rise in global temperature, changes in precipitation patterns are predicted particularly in Arctic regions. Such changes in patterns and modifications in typical snow to precipitation ratios will affect the snowpack thickness and the timing of snow accumulation and snow melting. Stable water isotopes (δ2H, δ18O) are one of the latest tools in exploring and tracing such changes, however, snow isotope and particularly snowmelt isotope datasets are rarely available which hamper the high-resolution isotope based hydrological investigations in Arctic regions. In this study, we perform an investigation for evaluating spatiotemporal variations in stable isotopes of snow and snowmelt water. Our Pallas research catchment is located in a subarctic setting in northern Finland. The measurements were made at 11 locations along a 2 km snow survey, which is established on the transect of the catchment, comprising of different landscape features (i) forested hillslope, (ii) mixed forest and (iii) open mires. We sampled depth-integrated bulk snowpack and fixed 5 cm incremental snow stratigraphy profile in snowpits. For snowmelt sampling, we used a system of snowmelt lysimeter, deployed at 11 locations. The bulk snowpack samples were collected biweekly, fixed 5 cm incremental stratigraphic snowpit samples during the period of maximum snowcover thickness and during the start of peak melting, during the peak melting and after the peak melting. Snowmelt samples were collected daily during the spring season until the complete disappearance of snow with complementary measurements of snowmelt flux, snow density and snow water equivalent. Our results indicate the higher mean values of snowmelt isotopes relative to the bulk snowpack and surface snow isotopes. The snow isotope profiles in snowpack reveal that the isotopes at the snow-air and snow-ground interfaces are enriched in heavier isotopes as compared to the middle of the snowpack. The snowmelt isotopes show that the isotopes are initially depleted in heavier isotopes but with the progress of melting, they start to become enriched. A well defined depleted to enriched pattern is observed at different locations in the forested hillslope area, while a relatively dispersed depleted to enriched pattern is observed at different locations in the mixed forested area. Our unique high-resolution dataset of snow and snowmelt isotopes will be useful in many applications; such as for evaluating post-depositional isotope modification in the seasonal snowpack, developing tracer-aided mass and energy based snow models. The establishment of snowmelt isotope dataset, showing spatiotemporal variability of snowmelt isotopes, is an important step forward in isotope based plant-water uptake studies and hydrological analyses in snow-influenced catchments.

Research on ecohydrological separation and plant water use have been increasing in the last few years, and various studies indicate that trees can use winter precipitation as a dominant water source during the growing season. Such studies are of great importance to northern regions, where soil water recharge timing is predicted to be significantly altered due to climate change. In order to assess plant water use in sub-arctic environment, it is necessary to understand how soil water pools under different land covers evolve throughout the year and how cryogenic processes alter the isotope input signal. This field study was conducted from May 2019 to June 2020 in Pallas catchment, located in sub-arctic conditions in Finnish Lapland. Soil cores up to 1 meter depth with 5 cm increments and xylem water of dominant tree species were collected in 4 locations, ranging from forest to shrubland/forest transitional area, and to forested peatland. All locations are positioned on a snow survey, in the vicinity of previously installed groundwater wells and snow lysimeters, and within 2 kilometers of rain gauge. Additional spatial samples of topsoil and xylem water were collected throughout the catchment during 2019 growing season. Relative proportions of tree source water were calculated by Bayesian mixing model MixSIAR. We produce new data set that displays plot and catchment scale soil water heterogeneities in a snow dominated environment, and examine: i) How soil properties affect isotopic composition of soil water?; ii) What is the effect of rising groundwater level on soil water isotope composition?; and iii) How snowpack thickness and melt timing modify soil water isotope patterns? We analyze if these varying pools of water are reflected in tree xylem water. Soil water isotope dynamics under deep snowpack, during and after snowmelt reveal how snow accumulation and melt timing and magnitude influence plant available water for growing season.

Snowmelt spring floods dominate the annual carbon flux in Arctic streams. However, climate change is altering their timing and magnitude due to changes in snow conditions, further altering the processes controlling the carbon cycle at the catchment scale. Current knowledge is limited by a lack of high-resolution data from Arctic areas. In this study we combine high-resolution biogeochemical-hydro-climatological variables with spectral wavelet analysis for new insights into carbon processes.This study was conducted during the snowmelt spring flood period in a sub-arctic headwater catchment in Pallas-Ylläs national park, Finland (68°02′N, 24°16′W). We collected in-stream dissolved organic carbon (DOC), carbon dioxide (C02), and terrestrial C02 flux alongside a suite of hydro-climatological variables measured at 30-minute intervals. Continuous wavelet transformations and wavelet coherence were produced to assess the relationship between hydro-climatological variables and carbon variables at different periodicities.Wavelet transforms indicated that the onset of snowmelt caused the development of significant diel periodicity for in-stream DOC, CO2 and terrestrial CO2 flux, while substantial periods of significant periodicity were observed at multiple day periodicities. Wavelet coherence analysis identified that DOC was consistently lead by flow and conductivity across daily and multiple daily scales suggesting that transport of carbon from the surface and shallow sub-surface pathways to the stream were the predominant processes controlling in-stream DOC. Interestingly for in-stream CO2, groundwater level showed periodic rather than consistent spectral coherence suggesting it is not a consistent control on CO2 in the spring flood. The strongest coherence for in-stream CO2 was with in-stream O2, which may suggest the importance of in-stream metabolism as a control on in-stream CO2 dynamics. Terrestrial CO2 fluxwas controlled by notably different processes than in-stream Carbon and linked strongest to climatological variables. Photosynthetically active radiation (PAR) showed the strongest relationship with CO2 terrestrial flux dynamics. Our study highlights the unique processes controlling different parts of the carbon cycle in a headwater arctic catchment during the snowmelt spring flood. We highlight in-stream DOC as particularly vulnerable to changes in spring flood magnitude and timing given the importance of snowmelt dominated transport processes to DOC flux. To identify future changes in the Arctic carbon cycle, wavelet analysis shows potential as tool to analyse changes in processes in high-resolution datasets.

Fully integrated physically based hydrological modeling is an essential method for increasing hydrological understanding of groundwater‐surface water (GW‐SW) interactions in peatlands and for predicting anthropogenic impacts on these unique ecosystems. Modeling studies represent peat soil in a simplistic manner, as a homogeneous layer of uniform thickness, but field measurements consistently show pronounced spatial variability in peatlands. This study evaluated uncertainty in groundwater levels and exfiltration fluxes associated with the simplified representation of the peat soil layer. For transferability of the results, impacts of selected topographical and hydrogeological conceptual models on GW‐SW exchange fluxes were simulated in a hypothetical hillslope representing a typical aquifer‐mire transect. The results showed that peat soil layer geometry defined the simulated spatial GW‐SW exchange patterns and groundwater flow paths, whereas total groundwater exfiltration flux to the hillslope and groundwater level in the peatland were only subtly altered by different conceptual peat soil geometry models. GW‐SW interactions were further explored using different scenarios and dimensionless parameters for peat hydraulic conductivity and hillslope‐peatland system slope. The results indicated that accurate representation of physical peat soil properties and landscape topography is important when the main objective is to model spatial GW‐SW exchange. Groundwater level in virtual peatland was not greatly affected by groundwater drawdown in an adjacent aquifer, but the magnitude and spatial distribution of GW‐SW interactions was significantly altered. This means that commonly used groundwater depth observations near peat‐mineral soil interfaces and within peatlands may not be a suitable indicator for monitoring the hydrological state of groundwater‐dependent peatland ecosystems.

Seasonal snow accumulation and melt dominates the hydrology in high latitude areas, providing water storages for both ecological and human needs. However, until recent years there has been a lack of cost-efficient way to measure the spatiotemporal variability of the snow depth and cover in high resolution. Unmanned aircraft systems (UAS) can offer spatial resolutions up to few centimeters, depending on the weather and light conditions, camera quality and drone specification. We used multiple different quadcopters and a fixed wing UAS to determine and analyze the spatiotemporal variability of snow depth and cover in three test plots with different land-cover types (forested slope, open peatland, and peatland-forest) in subarctic northern Finland, where weather and light conditions are challenging. Five measurement campaigns were conducted during winter 2018/2019 and a snow-free bare ground survey after snowmelt. Snow depth maps were constructed using Structure from Motion (SfM) photogrammetry technique and by differentiating the acquired models from snow-covered and snow-free surveys. Due to poor sub-canopy penetration with UAS-SfM method, tree masks were utilized to remove canopy effects prior to analysis. The snow depth maps produced with different UAS were compared to in situ snow course and an automatic ultrasonic measurement data. We highlight the difficulties of working in subarctic winter conditions and discuss the accuracy of UAS-derived snow depth maps. We show that the UAS-derived snow depth measurements agree well with manual snow survey measurements and UAS are suitable method for extending the spatial snow data coverage, whereas a continuous point snow depth measurement is unable to accurately present sub-catchment scale snow depth variability. Furthermore, the spatiotemporal variability of snow accumulation and melt between and within different land cover types is presented.

Seasonally frozen ground (SFG) occurs on ~25% of the Northern Hemisphere’s land surface, and the influence of SFG on water, energy, and solute fluxes is important in cold climate regions. The hydrological role of permafrost is now being actively researched, but the influence of SFG has been receiving less attention. Intuitively, water movement in frozen ground is blocked by ice forming in soil pores that were open to water flow prior to freezing. However, it has been shown that the hydrological influence of SFG is insignificant in some cases, with soil remaining permeable to water even when frozen. There is a clear knowledge gap concerning (1) how intensively and (2) under what physiographical and climate conditions SFG influences hydrological fluxes. We conducted a systematic literature review examining the hydrological importance of SFG we found reported in 143 publications. We found a clear hydrological influence of frozen ground in small-scale laboratory measurements, but a more ambiguous effect when the spatial scale under study increased to hillslopes, catchments, or watersheds. We also found that SFG may be hydrologically less important in forested areas or in regions with deep snow cover. Our systematic review suggests that hydrological influence of SFG may become more important in a future warmer climate with less snow and intensified land use in high-latitude areas.

Due to changes in the snowmelt timing and the potential shift towards less snowfall and more rainfall, infiltration patterns into the soil will increasingly be altered in a warming climate. Mixing and transport processes of water in the unsaturated topsoil layer regulate the subsurface transport and retention of solutes and contaminants, as well as the distribution of plant available water. Recent advances in soil isotope ecohydrology indicate that in some ecosystems, water in macropores largely bypasses soil matrix and rapidly percolates into the groundwater. Here we combine tracer experiments and geophysical surveys to explore soil water mixing in non-stratified till soil in the Pallas catchment located in sub-arctic conditions in Finnish Lapland. A 5x20 m plot at the Kenttärova hilltop was sprinkled with deuterated water (d2H 88‰) for two days (totally 200 mm at average intensity of 6.7 mm/h), until surface water ponding was observed on substantial share of the plot. Soil moisture dynamic were monitored by a network of soil moisture sensors and manual soil probe measurements. Soil water was sampled hourly with suction cup lysimeters at three (5 cm, 30 cm, 60 cm) depths and pan lysimeter at 35 cm depth in two soil profiles on the irrigation plot. Groundwater was sampled hourly, while xylem samples from spruce and birch trees in the plot were collected on each day of the experiment and on a weekly basis during the following month. Ground penetrating radar (GPR) survey and soil coring with window sampler down to 1 m depth were completed four times over the course of the experiment, and additional set of soil cores were taken two weeks after the experiment to inspect how natural precipitation events have infiltrated into the deuterium enriched zone. We investigate the mechanisms of soil matrix water replenishment by answering the following questions: i) Can all soil matrix water be displaced during high volume events and when does newly introduced soil matrix water become available to the plants?; ii) What is the extent of soil water mixing at different depths?; and iii) What is the effect of increased moisture content and groundwater table rise on soil water mixing? Due to paucity of field data sets and inability of most hydrological models to accurately simulate soil freezing and thawing effects, ecohydrologic partitioning has been barely studied in Northern regions with seasonal snow cover. We present a novel field data set that focuses on soil matrix water replenishment in glaciated till soil at sub-arctic conditions. Results support our understanding of ecohydrological processes in northern environments where hydrological cycle is dominated by intense infiltration events as it occurs during snowmelt.

The fate of the vast stocks of organic carbon stored in permafrost of the Western Siberian Lowland, the world's largest peat-land, is uncertain. Specifically, the amount of greenhouse gas emissions ...

Continuous data on spatial and temporal patterns of snowmelt rates are essential for hydrological studies, but are commonly not available, especially in the subarctic, mainly due to high monitoring costs. In this study, temperature loggers were used to measure local and microscale variations in snowpack temperature, in order to understand snowmelt processes and rates in subarctic northern Finland. The loggers were deployed on six test plots along a hillslope with varying topography (elevation and aspect) and vegetation (forest, transitional zone and mires, i.e. treeless peatlands) during two consecutive winters (2014 and 2015). At each test plot, the sensors were installed in five locations, at two heights in a snow profile. Algorithms were developed to analyse the snowmelt rates from high-resolution snowpack temperature data. The validity of the results was evaluated using snow depth and soil moisture data from adjacent reference sensors and the results were tested using an empirical degree-day snow model calibrated for each test plot. Snowmelt rates were relatively similar in mires (median 2.3 mm d−1 °C−1) and forests (median 2.6 mm d−1 °C−1) with apparent inter-annual variation. The observed melt rates were highest in the highest elevation plots, in transition zone in 2014 (median 4.6 mm d−1 °C−1) and southwest-facing forest line in 2015 (median 3.2 mm d−1 °C−1). The timing of the modelled meltwater outflow and snowpack ablation showed good agreement with the snowpack temperature-derived estimates and the soil moisture and snow depth measurements. The simple approach used represents a novel and cost-effective method to improve the spatial accuracy of in situ snow cover ablation measurements and melt rates and the precision of snowmelt models in the subarctic. An open-access R-based model is provided with this paper for analysis of high-frequency snow temperature data.

Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer-aided modeling. In snow-influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer-aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process-based snow interception-accumulation-melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof-of-concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long-term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer-aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow-influenced catchments.

Understanding the role of groundwater for runoff generation in headwater catchments is a challenge in hydrology, particularly so in data-scarce areas. Fully-integrated surface-subsurface modelling has shown potential in increasing process understanding for runoff generation, but high data requirements and difficulties in model calibration are typically assumed to preclude their use in catchment-scale studies. We used a fully integrated surface-subsurface hydrological simulator to enhance groundwater-related process understanding in a headwater catchment with a rich background in empirical data. To set up the model we used minimal data that could be reasonably expected to exist for any experimental catchment. A novel aspect of our approach was in using simplified model parameterisation and including parameters from all model domains (surface, subsurface, evapotranspiration) in automated model calibration. Calibration aimed not only to improve model fit, but also to test the information content of the observations (streamflow, remotely sensed evapotranspiration, median groundwater level) used in calibration objective functions. We identified sensitive parameters in all model domains (subsurface, surface, evapotranspiration), demonstrating that model calibration should be inclusive of parameters from these different model domains. Incorporating groundwater data in calibration objectives improved the model fit for groundwater levels, but simulations did not reproduce well the remotely sensed evapotranspiration time series even after calibration. Spatially explicit model output improved our understanding of how groundwater functions in maintaining streamflow generation primarily via saturation excess overland flow. Steady groundwater inputs created saturated conditions in the valley bottom riparian peatlands, leading to overland flow even during dry periods. Groundwater on the hillslopes was more dynamic in its response to rainfall, acting to expand the saturated area extent and thereby promoting saturation excess overland flow during rainstorms. Our work shows the potential of using integrated surface-subsurface modelling alongside with rigorous model calibration to better understand and visualise the role of groundwater in runoff generation even with limited datasets.

The Arctic&rsquo, s winter water cycle is rapidly changing, with implications for snow moisture sources and transport processes. Stable isotope values (&delta, 18O, &delta, 2H, d-excess) of the Arctic snowpack have potential to provide proxy records of these processes, yet it is unclear how well the isotope values of individual snowfall events are preserved within snow profiles. Here, we present water isotope data from multiple taiga and tundra snow profiles sampled in Arctic Alaska and Finland, respectively, during winter 2018&ndash, 2019. We compare the snowpack isotope stratigraphy with meteoric water isotopes (vapor and precipitation) during snowfall days, and combine our measurements with satellite observations and reanalysis data. Our analyses indicate that synoptic-scale atmospheric circulation and regional sea ice coverage are key drivers of the source, amount, and isotopic composition of Arctic snowpacks. We find that the western Arctic tundra snowpack profiles in Alaska preserved the isotope values for the most recent storm, however, post depositional processes modified the remaining isotope profiles. The overall seasonal evolution in the vapor isotope values were better preserved in taiga snow isotope profiles in the eastern Arctic, where there is significantly less wind-driven redistribution than in the open Alaskan tundra. We demonstrate the potential of the seasonal snowpack to provide a useful proxy for Arctic winter-time moisture sources and propose future analyses.

Summary Springs are unique ecosystems, but in many cases they are severely threatened and there is an urgent need for better spring management and conservation. To this end, we studied water quality and quantity in springs in Oulanka National Park, north-east Finland. Multivariate statistical methods were employed to relate spring water quality and quantity to hydrogeology and land use of the spring capture zone. This revealed that most springs studied were affected by locally atypical dolostone–limestone bedrock, resulting in high calcium, pH, and alkalinity values. Using Ward’s hierarchical clustering, the springs were grouped into four clusters based on their water chemistry. One cluster consisted of springs affected by past small-scale agriculture, whereas other clusters were affected by the variable bedrock, e.g., springs only 1 km from the dolostone–limestone bedrock area were beyond its calcium-rich impact zone. According to a random forest model, the best predictors of spring water chemistry were spring altitude and the stable hydrogen isotope ratio of the water (δ2H). Thus stable water isotopes could be widely applicable for boreal spring management. They may also provide a rough estimate of groundwater flow route (i.e., whether it is mainly local or regional), which largely determines the chemical characteristics of spring water. Our approach could be applied in other boreal regions and at larger spatial scales for improved classification of springs and for better targeted spring management.

Vast regions of the northern hemisphere are exposed to snowfall and seasonal frost. This has large effects on spatiotemporal distribution of infiltration and groundwater recharge processes as well ...

Tracer-aided hydrological models are increasingly used to reveal fundamentals of runoff generation processes and water travel times in catchments. Modelling studies integrating stable water isotopes as tracers are mostly based in temperate and warm climates, leaving catchments with strong snow influences underrepresented in the literature. Such catchments are challenging, as the isotopic tracer signals in water entering the catchments as snowmelt are typically distorted from incoming precipitation due to fractionation processes in seasonal snowpack. We used the Spatially distributed Tracer-Aided Rainfall–Runoff (STARR) model to simulate fluxes, storage, and mixing of water and tracers, as well as estimating water ages in three long-term experimental catchments with varying degrees of snow influence and contrasting landscape characteristics. In the context of northern catchments the sites have exceptionally long and rich data sets of hydrometric data and – most importantly – stable water isotopes for both rain and snow conditions. To adapt the STARR model for sites with strong snow influence, we used a novel parsimonious calculation scheme that takes into account the isotopic fractionation through snow sublimation and snowmelt. The modified STARR setup simulated the streamflows, isotope ratios, and snow pack dynamics quite well in all three catchments. From this, our simulations indicated contrasting median water ages and water age distributions between catchments brought about mainly by differences in topography and soil characteristics. However, the variable degree of snow influence in catchments also had a major influence on the stream hydrograph, storage dynamics, and water age distributions, which was captured by the model. Our study suggested that snow sublimation fractionation processes can be important to include in tracer-aided modelling for catchments with seasonal snowpack, while the influence of fractionation during snowmelt could not be unequivocally shown. Our work showed the utility of isotopes to provide a proof of concept for our modelling framework in snow-influenced catchments.

Use of isotopes to quantify the temporal dynamics of the transformation of precipitation into run‐off has revealed fundamental new insights into catchment flow paths and mixing processes that influence biogeochemical transport. However, catchments underlain by permafrost have received little attention in isotope‐based studies, despite their global importance in terms of rapid environmental change. These high‐latitude regions offer limited access for data collection during critical periods (e.g., early phases of snowmelt). Additionally, spatio‐temporal variable freeze–thaw cycles, together with the development of an active layer, have a time variant influence on catchment hydrology. All of these characteristics make the application of traditional transit time estimation approaches challenging. We describe an isotope‐based study undertaken to provide a preliminary assessment of travel times at Siksik Creek in the western Canadian Arctic. We adopted a model–data fusion approach to estimate the volumes and isotopic characteristics of snowpack and meltwater. Using samples collected in the spring/summer, we characterize the isotopic composition of summer rainfall, melt from snow, soil water, and stream water. In addition, soil moisture dynamics and the temporal evolution of the active layer profile were monitored. First approximations of transit times were estimated for soil and streamwater compositions using lumped convolution integral models and temporally variable inputs including snowmelt, ice thaw, and summer rainfall. Comparing transit time estimates using a variety of inputs revealed that transit time was best estimated using all available inflows (i.e., snowmelt, soil ice thaw, and rainfall). Early spring transit times were short, dominated by snowmelt and soil ice thaw and limited catchment storage when soils are predominantly frozen. However, significant and increasing mixing with water in the active layer during the summer resulted in more damped steam water variation and longer mean travel times (~1.5 years). The study has also highlighted key data needs to better constrain travel time estimates in permafrost catchments.

Climate change is expected to alter hydrological and biogeochemical processes in high-latitude inland waters. A critical question for understanding contemporary and future responses to environmental change is how the spatio-temporal dynamics of runoff generation processes will be affected. We sampled stable water isotopes in soils, lakes and rivers on an unprecedented spatio-temporal scale along a 1700 km transect over three years in the Western Siberia Lowlands. Our findings suggest that snowmelt mixes with, and displaces, large volumes of water stored in the organic soils and lakes to generate runoff during the thaw season. Furthermore, we saw a persistent hydrological connection between water bodies and the landscape across permafrost regions. Our findings help to bridge the understanding between small and large scale hydrological studies in high-latitude systems. These isotope data provide a means to conceptualise hydrological connectivity in permafrost and wetland influenced regions, which is needed for an improved understanding of future biogeochemical changes.

The Western Siberian Lowlands (WSL) store large quantities of organic carbon that will be exposed and mobilized by the thawing of permafrost. The fate of mobilized carbon, however, is not well understood, partly because of inadequate knowledge of hydrological controls in the region which has a vast low-relief surface area, extensive lake and wetland coverage and gradually increasing permafrost influence. We used stable water isotopes to improve our understanding of dominant landscape controls on the hydrology of the WSL. We sampled rivers along a 1700 km South–North transect from permafrost-free to continuous permafrost repeatedly over three years, and derived isotope proxies for catchment hydrological responsiveness and connectivity. We found correlations between the isotope proxies and catchment characteristics, suggesting that lakes and wetlands are intimately connected to rivers, and that permafrost increases the responsiveness of the catchment to rainfall and snowmelt events, reducing catchment mean transit times. Our work provides rare isotope-based field evidence that permafrost and lakes/wetlands influence hydrological pathways across a wide range of spatial scales (10–105 km2) and permafrost coverage (0%–70%). This has important implications, because both permafrost extent and lake/wetland coverage are affected by permafrost thaw in the changing climate. Changes in these hydrological landscape controls are likely to alter carbon export and emission via inland waters, which may be of global significance.

In the Boreal region, anticline eskers aquifers are recharged in upland hillslopes and water discharges in the surrounding lowlands. Organic peat soils often confine the aquifer discharge area and drainage of these confining peat layers can decrease the flow resistance in the peat soil, which may cause unintentional groundwater level drawdown. This poses a risk to groundwater bodies and their good water status in the Boreal region. To increase awareness of the risk areas and to assess potential areas for drainage restoration, a model based on geographical information systems (GIS) was developed to predict the locations of high groundwater discharge. The output of the model is a map highlighting the potential areas where groundwater is more likely to discharge. The model output was validated with stream flow data collected from two eskers located in Finland. The developed GIS model is recommended as a tool to delineate groundwater protection areas where drainage of the peat soil should not be allowed and for assessing areas where such drainage networks could be restored to protect vulnerable groundwater systems.

Summary Water resources management is moving towards integration, where groundwater (GW), surface water (SW) and related aquatic ecosystems are considered one management unit. Because of this paradigm shift, more information and new tools are needed to understand the ecologically relevant fluxes (water, heat, solutes) at the GW–SW interface. This study estimated the magnitude, temporal variability and spatial distribution of water fluxes at the GW–SW interface using a fully integrated hydrological modelling code (HydroGeoSphere). The model domain comprised a hydrologically complex esker aquifer in Northern Finland with interconnected lakes, streams and wetlands. The model was calibrated in steady state for soil hydraulic conductivity and anisotropy and it reproduced the hydraulic head and stream baseflow distribution throughout the aquifer in both transient and steady state modes. In a novel analysis, model outputs were compared with the locations and magnitude of GW discharge to lakes estimated using field techniques. Spatial occurrence of GW–lake interaction was interpreted from airborne thermal infrared imaging. The observed GW inflow locations coincided well with model nodes showing positive exchange flux between surface and subsurface domains. Order of magnitude of simulated GW inflow to lakes showed good agreement with flux values calculated with a stable water isotope technique. Finally, time series of GW inflow, extracted as model output, showed moderate annual variability and demonstrated different interannual inflow changes in seepage and drainage lakes of the aquifer. Overall, this study demonstrated the ability of a fully integrated numerical model to reproduce observed GW–SW exchange processes in a complex unconfined aquifer system. The model-based estimates obtained for GW influx magnitude and spatial distribution, along with information on GW quality can be used to estimate ecologically relevant fluxes in future water resources management.

Interest in climate change effects on groundwater has increased dramatically during the last decade. The mechanisms of climate-related groundwater depletion have been thoroughly reviewed, but the influence of global warming on groundwater-dependent ecosystems (GDEs) remains poorly known. Here we report long-term water temperature trends in 66 northern European cold-water springs. A vast majority of the springs (82%) exhibited a significant increase in water temperature during 1968-2012. Mean spring water temperatures were closely related to regional air temperature and global radiative forcing of the corresponding year. Based on three alternative climate scenarios representing low (RCP2.6), intermediate (RCP6) and high-emission scenarios (RCP8.5), we estimate that increase in mean spring water temperature in the region is likely to range from 0.67 °C (RCP2.6) to 5.94 °C (RCP8.5) by 2086. According to the worst-case scenario, water temperature of these originally cold-water ecosystems (regional mean in the late 1970s: 4.7 °C) may exceed 12 °C by the end of this century. We used bryophyte and macroinvertebrate species data from Finnish springs and spring-fed streams to assess ecological impacts of the predicted warming. An increase in spring water temperature by several degrees will likely have substantial biodiversity impacts, causing regional extinction of native, cold-stenothermal spring specialists, whereas species diversity of headwater generalists is likely to increase. Even a slight (by 1 °C) increase in water temperature may eliminate endemic spring species, thus altering bryophyte and macroinvertebrate assemblages of spring-fed streams. Climate change-induced warming of northern regions may thus alter species composition of the spring biota and cause regional homogenization of biodiversity in headwater ecosystems.

Use of stable water isotopes has become increasingly popular in quantifying water flow paths and travel times in hydrological systems using tracer‐aided modeling. In snow‐influenced catchments, snowmelt produces a traceable isotopic signal, which differs from original snowfall isotopic composition because of isotopic fractionation in the snowpack. These fractionation processes in snow are relatively well understood, but representing their spatiotemporal variability in tracer‐aided studies remains a challenge. We present a novel, parsimonious modeling method to account for the snowpack isotope fractionation and estimate isotope ratios in snowmelt water in a fully spatially distributed manner. Our model introduces two calibration parameters that alone account for the isotopic fractionation caused by sublimation from interception and ground snow storage, and snowmelt fractionation progressively enriching the snowmelt runoff. The isotope routines are linked to a generic process‐based snow interception‐accumulation‐melt model facilitating simulation of spatially distributed snowmelt runoff. We use a synthetic modeling experiment to demonstrate the functionality of the model algorithms in different landscape locations and under different canopy characteristics. We also provide a proof‐of‐concept model test and successfully reproduce isotopic ratios in snowmelt runoff sampled with snowmelt lysimeters in two long‐term experimental catchment with contrasting winter conditions. To our knowledge, the method is the first such tool to allow estimation of the spatially distributed nature of isotopic fractionation in snowpacks and the resulting isotope ratios in snowmelt runoff. The method can thus provide a useful tool for tracer‐aided modeling to better understand the integrated nature of flow, mixing, and transport processes in snow‐influenced catchments., Key Points We present a novel modeling method for snowpack isotope fractionation to estimate spatiotemporal variability of isotope ratios in snowmeltOur parsimonious model uses two calibration parameters that alone account for the isotopic fractionation caused by snow sublimation and meltWe provide a modeling experiment to demonstrate the model functionality and a proof‐of‐concept comparison using snowmelt isotope data

Esker aquifers are common groundwater bodies in Europe. Management of these aquifers should take account of the sustainability of groundwater-dependent ecosystems and land use in an integrated way. An unconfined esker aquifer in northern Finland was modelled with MODFLOW to determine how groundwater resources are impacted by the surrounding peatland drainage scheme and to simulate scenarios for possible drainage restoration. The impacts of groundwater abstraction and climate change were also simulated. A calibration-constrained Monte Carlo method was used to provide information on the uncertainties associated with model predictions. The results suggest that peatland drainage in the vicinity of eskers can have a significant role in lowering the water table, even though climate variability may mask these impacts. Drainage restoration by filling the ditches might have positive impacts on the aquifer water levels. Comparison of water-table changes caused by peatland drainage with the changes brought by water abstraction and climate variability helped to quantify impacts of different land-use scenarios and facilitated discussion with the local stakeholders. Based on this study, more attention should be devoted to peatland drainage schemes in integrated groundwater management of esker aquifers.

summary Groundwater–surface water interactions were studied in a Finnish esker aquifer system where some lakes suffer from periodic water level decline and others from eutrophication. Natural tracers (Ca 2+ , SiO2 and PO 3� 4 ) were used on the aquifer scale and seepage meter measurements and water level recordings on the single lake scale to better understand the complex interactions between groundwater, lakes and streams in the area. The natural tracers showed that lakes located in lower landscape positions and connected with streams were richer in tracer concentrations and that their water chemistry resembled that of the groundwater. On the other hand, closed basin lakes located at higher altitudes were nutrient-poor seepage lakes with some groundwater through flow. The data suggests that the subsurface acts as a phosphate source for the surface water bodies and the nutrient rich groundwater inseepage dictates the trophic status of the lakes in the area. The seepage meter measurements verified a strong interaction between groundwater and surface water and revealed spatial distribution of lake seepage and a temporal co-variation in lake seepage rates. A statistical analysis of seepage meter and water level observation time series were useful to show groundwater flow regimes controlling lake seepage. Using the natural tracer analysis and seepage meter measurements, we developed a novel conceptual model for the study site where the differences in lake trophic state and water table behavior were explained by assigning lakes to local and regional groundwater flow regimes. The obtained results provide new knowledge on using natural tracers in complex glacial aquifer systems and can be used in integrated groundwater–surface water management.

Groundwater–surface water (GW–SW) interactions cover a broad range of hydrogeological and biological processes and are controlled by natural and anthropogenic factors at various spatio-temporal scales, from watershed to hyporheic/hypolentic zone. Understanding these processes is vital in the protection of groundwater-dependent ecosystems increasingly required in water resources legislation across the world. The use of environmental tracers and indicators that are relevant simultaneously for groundwater, surface water and biocenoses–biotope interactions constitutes a powerful tool to succeed in the management task. However, tracer type must be chosen according to the scale of interest and tracer use thus requires a good conceptual understanding of the processes to be evaluated. This paper reviews various GW–SW interaction processes and their drivers and, based on available knowledge, systemises application of conservative tracers and semi-conservative and reactive environmental indicators at different spatial scales. Biocenoses–biotopes relationships are viewed as a possible transition tool between scales. Relation between principal application of the environmental tracers and indicators, examples and guidelines are further proposed for examining GW–SW interactions from a hydrogeological and biological point of view by demonstrating the usability of the tracers/indicators and providing recommendations for the scientific community and decision makers.

Summary Groundwater discharge from an esker aquifer to a fen was studied to understand relevant hydrological processes for groundwater-surface water interaction in an esker–peatland hillslope. Piezometric levels of the peat layer and esker sand layer were continuously monitored and compared to climate data. Groundwater exfiltration points were spatially mapped and related to peat depth. The study showed a clear interaction between fen surface water and sand esker groundwater although the hydraulic conductivity of peat was low and the peat depth thick. In the artesian aquifer beneath the fen, the piezometric head showed a clear response to precipitation and evapotranspiration. Wetting of the peat is rapidly seen as pressure increase in the confined aquifer. This provides new information on hydraulics of wetlands found in aquifer discharge areas. Groundwater exfiltrated through thick peat layers in vertical preferential point discharges demonstrating how double porosity provides flow through the peat layer with low hydraulic conductivity in the peat matrix. The “pipe flow” channels were found in the deep peat area in the transition zones from deep peat to shallow peat. Diffuse seepage through the ditch bottom was found where ditches cut into the sand layer. Increased discharge through the ditch bed reduced the flow resistance at the aquifer boundary which can lower the groundwater level in the esker. The results show new processes in the groundwater discharge zone that are useful for groundwater modelling and policy development for future groundwater protection.

This article presents an application of the choice experiment method in order to provide estimates of economic values generated by water quantity improvements in the environment. More importantly, this is the first choice experiment study valuing scientific information and in particular scientific information on climate change. The case study of interest is Rokua in Northern Finland, a groundwater dependent ecosystem very sensitive to climate change and natural variability. The study deals with the uncertainty about the actual dynamics of the system and the effect of future climate change by exploring whether the public values sustained provision of resources for scientific research to better understand long-term environmental changes in Rokua. Data are analysed using a nested multinomial logit and an error component model. Evidence from this study suggests that individuals are willing to pay in order to assure scientific research so as to better understand long-term environmental changes. As a result, policy should consider investing in and supporting related research. Other aspects of water management policy valued by the public are water quantity, recreation, and total land income., We gratefully acknowledge the financial support from the European Union via the 7th Framework Program GENESIS: Groundwater and dependent ecosystems: New Scientific basis on climate change and land-use impact for the update of the EU Groundwater Directive; WP-6 Groundwater systems management: scenarios, risk assessment, cost-efficient measures and legal aspects. We finally thank two anonymous referees for constructive and insightful comments

Rokua in Northern Finland is a groundwater dependent ecosystem very sensitive to climate change and natural variability. As such, the water level of most of the lakes is a function of the level of the groundwater table of the esker which is naturally recharged. The management of an ecosystem like this is very challenging and complex because of the many associated use and non-use values. The scope of this study is to expose, apart from the use values, the nonmarket values attached to the ecosystems services of groundwater systems and reveal their importance. In particular, this chapter illustrates the contribution of stated preference methods to orient policy making and presents results from an application of a choice experiment and contingent valuation method regarding ground water quantity. General public�s elicited values highlight the importance of water management policy which contributes to the sustainability of groundwater dependent ecosystems. Importantly results highlight the need to broaden the policy options beyond the consideration of market and use values of groundwater systems. Instead these systems should be considered as part of the broader ecosystems and broader services considered in decision making.