No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Impacts of forest canopy heterogeneity on plot-scale hydrometeorological variables -Insights from an experiment in the humid boreal forest with the Canadian Land Surface Scheme
Abstract
High latitude regions, including the circumpolar boreal biome, are experiencing important changes in the availability of usable surface water because of climate change. In this context, an adequate representation of the land-atmosphere interaction is critical to ensure optimal management of current and future water resources, forest management, and climate prediction. However, the task is particularly intricate in high-latitude boreal forest, as land surface model faces several challenges due to the unique environmental conditions and ecological characteristics. The objective of this study is to quantify the impact of forest landscape heterogeneity, specifically stand leaf-area index (LAI), soil texture, and drainage regime, on surface water and energy balance in a small boreal high-latitude sub-catchment. To this end, hydrometeorological conditions at seventeen 20×20 m plots in a 1-km 2 boreal forest sub-basin are simulated using the Canadian Land Surface Scheme (CLASS), a land surface model, at the point scale. The subplot-scale soil texture, drainage regime, and vegetation characteristics and type are based as closely as possible on field measurements and observations for the 17 plots. The model-driven experiment comprises two sets of simulations using CLASS, each employing the same model setup and run for the 17 experimental plots. The main set employs meteorological forcing from a local micrometeorological tower within the sub-basin to investigate the plot-to-plot variability of albedo, energy fluxes, and soil state variables. A second set of simulations is conducted using meteorological forcing from the ERA5-Land reanalysis, which spans from 1986 to 2022. This data provides a longer time series, enabling a more accurate representation of the interannual climatic variability in the sub-basin. The results of the main and secondary sets of CLASS simulations are used to assess the plot-to-plot and temporal variability of several key hydrometeorological variables by calculating a monthly spread. In brief, the following conclusions and broader implications can be drawn from the findings: i) The simulated total annual evapotranspiration remains relatively uniform between plots despite notable variation in its partitioning from plot to plot. ii) In the presence of a full snowpack, the albedo exhibits substantial heterogeneity at the subplot scale, linked to the canopy's LAI. iii) Local soil properties, drainage regime, and vegetation structure and type exhibit substantial influence on the plot-to-plot variability in soil water content. iv) When parameterized with localized observations and measurements, CLASS can represent and be responsive to the complex dynamics of energy and water fluxes at the plot scale within the heterogeneous surface of boreal forests.
ResearchGate has not been able to resolve any citations for this publication.
Winter near-surface air temperatures have important implications for ecosystem functioning such as vegetation dynamics and carbon cycling. In cold environments, the persistence of seasonal snow cover can exert a strong control on the near-surface temperatures. However, the lack of in situ measurements of both snow cover duration and surface temperatures over high latitudes has made it difficult to estimate the spatio-temporal variability in this relationship. Here, we quantified the fine-scale variability in winter near-surface air temperatures (+2 cm) and snow cover duration (calculated from temperature time series) using a total of 441 microclimate loggers in seven study areas across boreal and tundra landscapes in Finland during 2019–2021. We further examined the drivers behind this variation using a structural equation model and the extent to which near-surface air temperatures are buffered from free-air temperatures during winter. Our results show that while average winter near-surface temperatures stay close to 0 ∘C across the study domain, there are large differences in their fine-scale variability among the study areas. Areas with large topographical variation, as well as areas with shallow snowpacks, showed the greatest variation in near-surface temperatures and in snow cover duration. In the tundra, for example, differences in minimum near-surface temperatures between study sites were close to 30 ∘C and topography was shown to be an important driver of this variability. In contrast, flat topography and long snow cover duration led to little spatial variation, as well as long periods of decoupling between near-surface and air temperatures. Quantifying and understanding the landscape-wide variation in winter microclimates improves our ability to predict the local effects of climate change in the rapidly warming boreal and tundra regions.
The snowpack has a major influence on the land surface energy budget. Accurate simulation of the snowpack energy and radiation budget is challenging due to, e.g., effects of vegetation and topography, as well as limitations in the theoretical understanding of turbulent transfer in the stable boundary layer. Studies that evaluate snow, hydrology and land surface models against detailed observations of all surface energy balance components at high latitudes are scarce. In this study, we compared different configurations of the SURFEX land surface 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, as well as the contrasting forest and peatland ecosystems typical for the boreal landscape. We tested different turbulent flux parameterizations implemented in the Crocus snowpack model. In addition, we examined common alternative approaches to conceptualize soil and vegetation, and we assessed their performance in simulating surface energy fluxes, snow conditions and soil thermal regime. Our results show that a stability correction function that increases the turbulent exchange under stable atmospheric conditions is imperative to simulate sensible heat fluxes over the peatland snowpacks and that realistic peat soil texture (soil organic content) parameterization greatly improves the soil temperature simulations. For accurate simulations of surface energy fluxes, snow and soil conditions in forests, an explicit vegetation representation is necessary. Moreover, we demonstrate the high sensitivity of surface fluxes to a poorly documented parameter involved in snow cover fraction computation. Although we focused on models within the SURFEX platform, the results have broader implications for choosing suitable turbulent flux parameterization and model structures depending on the potential use cases for high-latitude land surface modeling.
Soil moisture has a profound influence on life on Earth, and this vital water resource varies across space and time. Here, we explored soil moisture variations in boreal forest and tundra environments, where comprehensive soil moisture datasets are scarce. We installed soil moisture sensors up to 14 cm depth at 503 measurement sites within seven study areas across northern Europe. We recorded 6,138,528 measurements to capture soil moisture variations of the snowless season from April to September 2020. We described the spatio‐temporal patterns of soil moisture, and test how these patterns are linked to topography and how these links vary in space and time. We found large spatial variation and often less pronounced temporal variation in soil moisture across the measurement sites within all study areas. We found that throughout the measurement periods both univariate and multivariate models with topographic predictors showed great temporal variation in their performance and in the relative influence of the predictors within and across the areas. We found that the soil moisture‐topography relationships are site‐specific, as the topography‐based models often performed poorly when transferred from one area to another. There was no general solution that would work well in all the study areas when modeling soil moisture variation with topography. This should be carefully considered before applying topographic proxies for soil moisture. Overall, these data and results highlight the strong spatio‐temporal heterogeneity of soil moisture conditions in boreal forest and tundra environments.
Operational flood forecasting in Canada is a provincial responsibility that is carried out by several entities across the country. However, the increasing costs and impacts of floods require better and nationally coordinated flood prediction systems. A more coherent flood forecasting framework for Canada can enable implementing advanced prediction capabilities across the different entities with responsibility for flood forecasting. Recently, the Canadian meteorological and hydrological services were tasked to develop a national flow guidance system. Alongside this initiative, the Global Water Futures program has been advancing cold regions process understanding, hydrological modeling, and forecasting. A community of practice was established for industry, academia, and decision‐makers to share viewpoints on hydrological challenges. Taken together, these initiatives are paving the way towards a national flood forecasting framework. In this article, forecasting challenges are identified (with a focus on cold regions), and recommendations are made to promote the creation of this framework. These include the need for cooperation, well‐defined governance, and better knowledge mobilization. Opportunities and challenges posed by the increasing data availability globally are also highlighted. Advances in each of these areas are positioning Canada as a major contributor to the international operational flood forecasting landscape. This article highlights a route towards the deployment of capacities across large geographical domains.
Soil hydraulic properties are central for soil quality and affect forest productivity and the impacts of climate change on forests. The water retention characteristics (WRC) of mineral forest soils in Finland are not well known, and practical tools to predict them for hydrological, biogeochemical and forest models are lacking. We statistically analyzed mineral forest soils WRC from over 130 sites in Finland, focusing on the humus layer and main root zone (0–19 cm depth). We showed that mineral forest soils can be grouped into five WRC classes that are well predictable from soil bulk density, organic matter content and clay fraction. However, the majority of the forest soils are hydrologically rather similar. We found that neither topsoil maps nor any combination of open geospatial data were able to predict WRC. Thus, in the absence of site-specific soil data, parameterizing WRC as a function of forest site fertility type was proposed. We demonstrated the approach in soil moisture modeling at a small forest headwater catchment and showed that the soil moisture response to weather conditions is jointly affected by WRC, stand attributes and topography. We showed that drought risks are highest for dense mature forests at nutrient-poor, coarse-textured sites and lower for young stands on peatlands and lowland herb-rich sites with groundwater influence. The results improve hydrological predictions for Finnish forests, and the open dataset can contribute to the larger synthesis and development of boreal forest soil pedo-transfer functions.
Under climate change, drought conditions are projected to intensify and soil water stress is identified as one of the primary drivers of the decline of forests. While there is strong evidence of such megadisturbance in semi-arid regions, large uncertainties remain in North American temperate forests and fine-scale assessments of future soil water stress are needed to guide adaptation decisions. The objectives of this study were to (i) assess the impact of climate change on the severity and duration of soil water stress in a temperate forest of eastern North America and (ii) identify environmental factors driving the spatial variability of soil water stress levels. We modeled current and future soil moisture at a 1 km resolution with the Canadian Land Surface Scheme (CLASS). Despite a slight increase in precipitation during the growing season, the severity (95th percentile of absolute soil water potential) and duration (number of days where absolute soil water potential is greater than or equal to 9,000 hPa) of soil water stress were projected to increase on average by 1,680 hPa and 6.7 days in 80 years under RCP8.5, which correspond to a 33 and 158% increase compared to current levels. The largest increase in severity was projected to occur in areas currently experiencing short periods of soil water stress, while the largest increase in duration is rather likely to occur in areas already experiencing prolonged periods of soil water stress. Soil depth and, to a lesser extent, soil texture, were identified as the main controls of the spatial variability of projected changes in the severity and duration of soil water stress. Overall, these results highlight the need to disentangle impacts associated with an increase in the severity vs. in the duration of soil water stress to guide the management of temperate forests under climate change.
When modelling forest growth, capturing the effects of climate change is needed for reliable long-term predictions and management choices. This remains a challenge because commonly used mensurational forest growth and yield models, relying on inventory data, cannot account for climate change effects. We developed hybrid physiological/mensurational basal area growth and yield models, which combine physiological response to climatic conditions and empirical relations. We included climate and site effects by replacing time with light sums of photosynthetically active radiation and modifying the latter with monthly soil water, vapour pressure deficit, temperature, and frost days. When parameterised with permanent sample plot data for Scots pine and Norway spruce across Sweden, the hybrid models could reproduce observations well, although with no increase in precision compared with time-based mensurational models. When considering different climate scenarios, a significant impact on productivity from climate change emerged. For example, a 2°C warming enhanced Scots pine production by up to 14% in regions where temperatures were originally cooler and soil water deficit was low (i.e. northwest Sweden), but depressed it, up to 9%, elsewhere. Hence, climate-sensitive models that take local variations into account are necessary for accurate predictions and sustainable forest management.
Variations in hillslope soil moisture control forest hydrologic fluxes and storage pools, yet sparse observations combined with the complexity and heterogeneity of water movement and storage in the vadose zone can make temporal and spatial patterns and processes difficult to predict. We used two years of field observations of volumetric soil moisture at three depths (15, 30, and 100 cm) across five topographic positions (riparian, toeslope, sideslope, shoulder, and ridge) along three hillslope transects to better understand how soil moisture changes with hillslope position and through time. As expected, we found higher values of soil moisture at all depths at the riparian and toeslope positions. Unexpectedly, we found that ridges were particularly wet during the wet winter months and dried quickly during the summer months, indicating that topography alone cannot account for mean wet season soil moisture in our Mediterranean-climate field site. The variability in soil moisture across all soil depths and topographic positions was greatest when soils were dry and decreased under wet soil conditions; this variability remained high in the deeper soil horizons, regardless of season. Lastly, event-analysis suggests that the response to early-season rainfall was highly variable along the hillslopes and was likely dominated by localized controls such as micro-topography and vegetation as well as soil texture, antecedent moisture conditions and rainfall characteristics. Our results suggest that the drivers of wet and dry season soil moisture dynamics can vary across topographic positions along a hillslope and do not always follow topographic controls.
Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management.
The full ERA5-Land hourly and monthly averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.
The Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) is an open-source community model designed to address research questions that explore the role of the land surface in the global climate system. Here, we evaluate how well CLASSIC reproduces the energy, water, and carbon cycle when forced with quasi-observed meteorological data. Model skill scores summarize how well model output agrees with observation-based reference data across multiple statistical metrics. A lack of agreement may be due to deficiencies in the model, its forcing data, and/or reference data. To address uncertainties in the forcing, we evaluate an ensemble of CLASSIC runs that is based on three meteorological data sets. To account for observational uncertainty, we compute benchmark skill scores that quantify the level of agreement among independent reference data sets. The benchmark scores demonstrate what score values a model may realistically achieve given the uncertainties in the observations. Our results show that uncertainties associated with the forcing and observations are considerably large. For instance, for 10 out of 19 variables assessed in this study, the sign of the bias changes depending on what forcing and reference data are used. Benchmark scores are much lower than expected, implying large observational uncertainties. Model and benchmark score values are mostly similar, indicating that CLASSIC performs well when considering observational uncertainty. Future model development should address (i) a positive albedo bias and resulting shortwave radiation bias in parts of the Northern Hemisphere (NH) extratropics and Tibetan Plateau, (ii) an out-of-phase seasonal gross primary productivity cycle in the humid tropics of South America and Africa, (iii) a lacking spatial correlation of annual mean net ecosystem exchange with site-level measurements, (iv) an underestimation of fractional area burned and corresponding emissions in the boreal forests, (v) a negative soil organic carbon bias in high latitudes, and (vi) a time lag in seasonal leaf area index maxima in parts of the NH extratropics. Our results will serve as a baseline for guiding and monitoring future CLASSIC development.
Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, which is known as a reanalysis. While the current generation of NWP models output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; http://www.globalfloods.eu/, last access: 28 June 2020) by coupling surface and sub-surface runoff from the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1∘ at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 1 January 1979 until near real time (2 to 5 d behind real time). The reanalysis was evaluated against a global network of 1801 daily river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling–Gupta efficiency skill score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the user community for applications ranging from monitoring global flood and drought conditions to the identification of hydroclimatic variability and change and as raw input for post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-glofas-historical?tab=overview (last access: 28 June 2020) with the following DOI: 10.24381/cds.a4fdd6b9 (C3S, 2019).
Recent reports by the Global Carbon Project highlight large uncertainties around land surface processes such as land use change, strength of CO2 fertilization, nutrient limitation and supply, and response to variability in climate. Process-based land surface models are well suited to address these complex and emerging global change problems but will require extensive development and evaluation. The coupled Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) framework has been under continuous development by Environment and Climate Change Canada since 1987. As the open-source model of code development has revolutionized the software industry, scientific software is experiencing a similar evolution. Given the scale of the challenge facing land surface modellers, and the benefits of open-source, or community model, development, we have transitioned CLASS-CTEM from an internally developed model to an open-source community model, which we call the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) v.1.0. CLASSIC contains many technical features specifically designed to encourage community use including software containerization for serial and parallel simulations, extensive benchmarking software and data (Automated Model Benchmarking; AMBER), self-documenting code, community standard formats for model inputs and outputs, amongst others. Here, we evaluate and benchmark CLASSIC against 31 FLUXNET sites where the model has been tailored to the site-level conditions and driven with observed meteorology. Future versions of CLASSIC will be developed using AMBER and these initial benchmark results to evaluate model performance over time. CLASSIC remains under active development and the code, site-level benchmarking data, software container, and AMBER are freely available for community use.
The Canadian Land Surface Scheme (CLASS) has been applied over the years in coupled and uncoupled (offline) modes at local, regional, and global scale using various forcing data sets. In this study, CLASS is applied at a local scale in the offline configuration to evaluate its performance when driven by the ERA5 reanalysis. Simulated surface energy fluxes, as well as several other water balance components, are investigated at four sites across the Canadian boreal biome. The results from CLASS driven by ERA5 (CLASS-RNL) are compared with available in situ measurements, as well as with results from CLASS driven by observations (CLASS-CTL). Additional simulations are conducted to evaluate the effects of biases in the ERA5 precipitation, where CLASS is forced by ERA5 data, but with ERA5 precipitation being replaced by observed precipitation (CLASS-RNL-ObsP). The results show that simulated surface variables in CLASS-RNL are in good agreement with observations as well as with those simulated in CLASS-CTL. The CLASS-RNL captures well the observed annual cycles of the surface energy and water fluxes, as well as the year-to-year variation of snow depth, soil temperature, and soil moisture. A strong correlation is found between the observed and CLASS-RNL simulated snow depth and soil temperature. Biases in the ERA5 precipitation did not affect the simulation of soil state variables, whereas the simulated surface heat and water fluxes, as well as the snow depth, were significantly affected. For instance, the simulated runoff in CLASS-RNL is much higher than in CLASS-RNL-ObsP and CLASS-CTL at the most humid sites due to significant positive bias in ERA5 precipitation.
The recently developed Soil, Vegetation, and Snow (SVS) land surface model is being progressively implemented at Environment and Climate Change Canada (ECCC) for operational numerical weather and hydrological predictions. The objective of this study is to evaluate the ability of SVS, in offline point-scale mode and under snow-free conditions, to simulate the surface heat fluxes and soil moisture when compared to flux tower observations and simulations from the Canadian Land Surface Scheme (CLASS), used here as a benchmark model. To do this, we performed point-scale simulations of between 4 and 12 years of data records at six selected sites of the FLUXNET network under arid, Mediterranean and tropical climates. At all sites, SVS shows realistic simulations of latent heat flux, sensible heat flux and net radiation. Soil heat flux is reasonably well simulated for the arid sites and one Mediterranean site and poorly simulated for the tropical sites. On the other hand, surface soil moisture was reasonably well simulated at the arid and Mediterranean sites and poorly simulated at the tropical sites. SVS performance was comparable to CLASS not only for energy fluxes and soil moisture, but also for more specific processes such as evapotranspiration and water balance.
Abstract Land surface models (LSMs) are a vital tool for understanding, projecting, and predicting the dynamics of the land surface and its role within the Earth system, under global change. Driven by the need to address a set of key questions, LSMs have grown in complexity from simplified representations of land surface biophysics to encompass a broad set of interrelated processes spanning the disciplines of biophysics, biogeochemistry, hydrology, ecosystem ecology, community ecology, human management, and societal impacts. This vast scope and complexity, while warranted by the problems LSMs are designed to solve, has led to enormous challenges in understanding and attributing differences between LSM predictions. Meanwhile, the wide range of spatial scales that govern land surface heterogeneity, and the broad spectrum of timescales in land surface dynamics, create challenges in tractably representing processes in LSMs. We identify three “grand challenges” in the development and use of LSMs, based around these issues: managing process complexity, representing land surface heterogeneity, and understanding parametric dynamics across the broad set of problems asked of LSMs in a changing world. In this review, we discuss progress that has been made, as well as promising directions forward, for each of these challenges.
A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than -0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.
Vegetation is known to have strong influence on evapotranspiration (ET), a major component of terrestrial water balance. Yet hydrological models often describe ET by methods unable to include the variability of vegetation characteristics in their predictions. To take advantage of the increasing availability of high-resolution open GIS data on land use, vegetation and soil characteristics in the boreal zone, a modular, spatially distributed model for predicting ET and other hydrological processes from grid cell to catchment level is presented and validated. An improved approach to upscale stomatal conductance to canopy scale using information on plant type (conifer/deciduous) and stand leaf-area index (LAI) is proposed by coupling a common leaf-scale stomatal conductance model with a simple canopy radiation transfer scheme. Further, a generic parametrization for vegetation-related hydrological processes for Nordic boreal forests is derived based on literature and data from a boreal FluxNet site. With the generic parametrization, the model was shown to reproduce daily ET measured using an eddy-covariance technique well at 10 conifer-dominated Nordic forests whose LAI ranged from 0.2 to 6.8 m2 m-2. Topography, soil and vegetation properties at 21 small boreal headwater catchments in Finland were derived from open GIS data at 16 m × 16 m grid size to upscale water balance from stand to catchment level. The predictions of annual ET and specific discharge were successful in all catchments, located from 60 to 68∘ N, and daily discharge was also reasonably well predicted by calibrating only one parameter against discharge measurements. The role of vegetation heterogeneity in soil moisture and partitioning of ET was demonstrated. The proposed framework can support, for example, forest trafficability forecasting and predicting impacts of climate change and forest management on stand and catchment water balance. With appropriate parametrization it can be generalized outside the boreal coniferous forests.
Hydrological projections under future climate change have been shown to be sensitive to the formulation of evapotranspiration. Many hydrological models still rely on empirical formulations of this flux, and hence do not take into account the surface energy budget. On the other hand, land surface schemes, which are used within the climate models to describe land hydrology and associated surface heat fluxes (SHF), rely on the energy conservation. Due to land surface schemes complexity, they are not suitable for integration in traditional hydrological models. A newly developed and relatively simple Maximum Entropy Production model, which operates under the constraint of energy conservation and allows for an appropriate partitioning of available energy into SHF, appears to be a good alternative for integration in hydrological models. However, the MEP's performances still need to be evaluated under various environmental conditions. This study aims to evaluate the SHF simulated by MEP using observations over a multiyear period from several carefully chosen snow‐free sites located in low‐latitude regions. Moreover, simulated fluxes were compared to those derived from the Canadian Land Surface Scheme (CLASS), which was run at the same sites. The analysis of simulated and observed fluxes associated with different water stress conditions suggests that the abilities of MEP and CLASS in estimating sensible and latent heat fluxes are comparable. It was also found that the MEP and CLASS fluxes are in a good agreement with observations. However, the simulated nocturnal fluxes show that both models are in less agreement with the observations.
The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990-2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to -0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.
Peatlands, which contain large carbon stocks that must be accounted for in
the global carbon budget, are poorly represented in many earth system models.
We integrated peatlands into the coupled Canadian Land Surface Scheme (CLASS)
and the Canadian Terrestrial Ecosystem Model (CTEM), which together simulate
the fluxes of water, energy, and CO2 at the land surface–atmosphere
boundary in the family of Canadian Earth system models (CanESMs). New
components and algorithms were added to represent the unique features of
peatlands, such as their characteristic ground floor vegetation (mosses), the
slow decomposition of carbon in the water-logged soils and the interaction
between the water, energy, and carbon cycles. This paper presents the
modifications introduced into the CLASS–CTEM modelling framework together
with site-level evaluations of the model performance for simulated water,
energy and carbon fluxes at eight different peatland sites. The simulated
daily gross primary production (GPP) and ecosystem respiration are well
correlated with observations, with values of the Pearson correlation
coefficient higher than 0.8 and 0.75 respectively. The simulated mean annual
net ecosystem production at the eight test sites is
87 g C m−2 yr−1, which is 22 g C m−2 yr−1 higher
than the observed annual mean. The general peatland model compares well with
other site-level and regional-level models for peatlands, and is able to
represent bogs and fens under a range of climatic and geographical
conditions.
A new approach of coordinated global and regional climate modeling is presented. It is applied to the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) and its parent global climate model CanESM2. CanRCM4 was developed specifically to downscale climate predictions and climate projections made by its parent global model. The close association of a regional climate model (RCM) with a parent global climate model (GCM) offers novel avenues of model development and application that are not typically available to independent regional climate modeling centers. For example, when CanRCM4 is driven by its parent model, driving information for all of its prognostic variables is available (including aerosols and chemical species), significantly improving the quality of their simulation. Additionally, CanRCM4 can be driven by its parent model for all downscaling applications by employing a spectral nudging procedure in CanESM2 designed to constrain its evolution to follow any large-scale driving data. Coordination offers benefit to the development of physical parameterizations and provides an objective means to evaluate the scalability of such parameterizations across a range of spatial resolutions. Finally, coordinating regional and global modeling efforts helps to highlight the importance of assessing RCMs' value added relative to their driving global models. As a first step in this direction, a framework for identifying appreciable differences in RCM versus GCM climate change results is proposed and applied to CanRCM4 and CanESM2.
Regional and global climate model simulated streamflows for high-latitude regions show systematic biases, particularly in the timing and magnitude of spring peak flows. Though these biases could be related to the snow water equivalent and spring temperature biases in models, a good part of these biases is due to the unaccount- ed effects of non-uniform infiltration capacity of the frozen ground and other related processes. In this paper, the treat- ment of frozen water in the Canadian Land Surface Scheme (CLASS), which is used in the Canadian regional and global climate models, is modified to include fractional permeable area, supercooled liquid water and a new formulation for hydraulic conductivity. The impact of these modifications on the regional hydrology, particularly streamflow, is assessed by comparing three simulations performed with the original and two modified versions of CLASS, driven by atmospheric forcing data from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis (ERA-Interim) for the 1990–2001 period over a northeast Canadian domain. The two modified versions of CLASS differ in the soil hydraulic conductivity and matric potential formulations, with one version being based on formulations from a previous study and the other one is newly proposed. Results suggest statistically significant decreases in infiltra- tion and therefore soil moisture during the snowmelt season for the simulation with the new hydraulic conductivity and matric potential formulations and fractional permeable area concept compared to the original version of CLASS, which is also reflected in the increased spring surface runoff and streamflows in this simulation with modified CLASS over most of the study domain. The simulated spring peaks and their timing in this simulation are also in better agreement to those observed. This study thus demonstrates the importance of treatment of frozen water for realistic simulation of streamflows.
The boreal forest, one of the largest biomes on Earth, provides ecosystem services that benefit society at levels ranging from local to global. Currently, about two-thirds of the area covered by this biome is under some form of management, mostly for wood production. Services such as climate regulation are also provided by both the unmanaged and managed boreal forests. Although most of the boreal forests have retained the resilience to cope with current disturbances, projected environmental changes of unprecedented speed and amplitude pose a substantial threat to their health. Management options to reduce these threats are available and could be implemented, but economic incentives and a greater focus on the boreal biome in international fora are needed to support further adaptation and mitigation actions.
Copyright © 2015, American Association for the Advancement of Science.
Abstract: http://www.sciencemag.org/cgi/content/abstract/349/6250/819?ijkey=9E/LoNrjj1ASk&keytype=ref&siteid=sci
Reprint: http://www.sciencemag.org/cgi/rapidpdf/349/6250/819?ijkey=9E/LoNrjj1ASk&keytype=ref&siteid=sci
Full Text: http://www.sciencemag.org/cgi/content/full/349/6250/819?ijkey=9E/LoNrjj1ASk&keytype=ref&siteid=sci
Abstract. The System for Automated Geoscientific Analyses
(SAGA) is an open source geographic information system
(GIS), mainly licensed under the GNU General Public
License. Since its first release in 2004, SAGA has rapidly
developed from a specialized tool for digital terrain analysis
to a comprehensive and globally established GIS platform
for scientific analysis and modeling. SAGA is coded
in CCC in an object oriented design and runs under several
operating systems including Windows and Linux. Key
functional features of the modular software architecture comprise
an application programming interface for the development
and implementation of new geoscientific methods, a
user friendly graphical user interface with many visualization
options, a command line interpreter, and interfaces to
interpreted languages like R and Python. The current version
2.1.4 offers more than 600 tools, which are implemented in
dynamically loadable libraries or shared objects and represent
the broad scopes of SAGA in numerous fields of geoscientific
endeavor and beyond. In this paper, we inform about
the system’s architecture, functionality, and its current state
of development and implementation. Furthermore, we highlight
the wide spectrum of scientific applications of SAGA in
a review of published studies, with special emphasis on the
core application areas digital terrain analysis, geomorphology,
soil science, climatology and meteorology, as well as
remote sensing.
Small-scale gap disturbance and gap regeneration can be common in boreal forests that have escaped catastrophic fire disturbance for prolonged time periods. Tree regeneration in gaps is enhanced by fine-scale heterogeneity at the forest floor created by soil disturbance and woody debris, which create favourable microsites for seed germination and seedling establishment. The significance of specific microsites for seedling establishment varies among forest types, being smallest in dry Scots pine Pinus sylvestris dominated forests with scanty understory vegetation and thin humus layer, and greatest in moist Norway spruce Picea abies dominated forests with abundant understory vegetation and thick humus layer. In a gap the survival, growth and recruitment of tree seedlings is determined by gap size and long-term below- and above-ground interferences between tree seedlings, understory vegetation and adjacent large trees. Gap disturbance contributes to the structural, functional and species diversity of the boreal forest both at local and areal levels. At local level gap disturbance increases fine-scale variation in soil properties and microtopography. At areal scale gap disturbance creates horizontally and vertically heterogeneous forest structures and contributes to the coexistence of coniferous and broad-leaved tree species. -Author
This study investigates the sensitivity of the Canadian Regional Climate Model (CRCM5) simulated near surface permafrost and its climate interactions to soil and snow formulations. In particular, sensitivities to the depth of the soil column, inclusion of organic soils and modified snow conductivity formulation are investigated. The impact of these modifications are first assessed in offline simulations performed with the Canadian Land Surface Scheme (CLASS), which is the land surface scheme used in CRCM5, when driven by ERA-40/ERA-Interim for the 1957–2008 period. Analysis of CLASS simulations shows major improvements in the simulated permafrost extent, particularly with a deeper soil column. Inclusion of organic soil decreased the summer ground heat flux and therefore the summer soil temperatures, leading to improvements in the simulated active layer thickness (ALT). The impact of the new snow thermal conductivity formulation is moderate compared to the effect of organic soils, but reduces the cold biases in winter soil tempera-tures. CRCM5 experiments revealed similar sensitivities to soil depth, organic soil and snow conductivity changes as with the offline simulations. Significant changes are noted in the land–atmosphere interactions, through modified energy and moisture partitioning at the surface resulting from the inclusion of the organic soils. The inter-annual variability of the ALT shows larger sensitivities to summer temperatures for mineral soil while experiments including organic soils show increased sensitivities to annual tem-peratures. The ALT trends in the CRCM5 are similar to the observed values, despite the overestimation of ALT asso-ciated with a warm bias in the CRCM5 climate.
The sensitivity of sonic anemometer-derived stress estimates to the tilt of the anemometer is investigated. The largest stress errors are shown to occur for unstable stratification (z/L Keywords: Anemometers; Coordinate systems; Sloping terrain; Surface layer; Tilt corrections Document Type: Regular Paper Affiliations: 1: National Oceanic and Atmospheric Administration, Environmental Research Laboratories, Environmental Technology Laboratory, Boulder, CO 80303, U.S.A. 2: National Center for Atmospheric Research, Boulder, CO 80303, U.S.A. 3: nnovative Emergency Management, Baton Rouge, LA 70809, U.S.A. Publication date: April 1, 2001 (document).ready(function() { var shortdescription = (".originaldescription").text().replace(/\\&/g, '&').replace(/\\, '<').replace(/\\>/g, '>').replace(/\\t/g, ' ').replace(/\\n/g, ''); if (shortdescription.length > 350){ shortdescription = "" + shortdescription.substring(0,250) + "... more"; } (".shortdescription a").click(function() { (".originaldescription").slideDown(); return false; }); }); Related content In this: publication By this: publisher By this author: Wilczak, J.M. ; Oncley, S.P. ; Stage, S.A. GA_googleFillSlot("Horizontal_banner_bottom");
This study evaluates key aspects of the snow cover, cloud cover, and radiation budget simulated by the
Canadian Regional Climate Model, version 4 (CRCM4), coupled with two versions of the Canadian Land
Surface Scheme (CLASS). CRCM4 coupled with CLASS version 2.7 has been used operationally at Ouranos
since 2006, while, more recently, CRCM4 has been coupled experimentally with CLASS 3.5, which includes
a number of improvements to the representation of snow cover processes. The simulations showed evidence
of a systematic cold temperature bias. Evaluation of cloud cover and radiation fluxes with satellite data
suggests this bias is related to insufficient cloud radiative forcing from a combination of underestimated cloud
cover, excessive cloud albedo, and too low cloud emissivity in the model. This cold bias is reinforced by
a positive snow albedo feedback manifest through earlier snow cover onset in the fall and early winter period.
Snow albedo was found to be very sensitive to the treatment of albedo refresh but insignificantly influenced by
the partitioning of solid precipitation in CLASS. This study demonstrates that atmospheric forcing can exert
a significant impact on the simulation of snow cover and surface albedo. The results highlight the need to
evaluate parameterizations in land surface models designed for climate models in fully coupled mode.
Canadian boreal woodlands and forests cover approximately 3.09 × 10⁶ km², located within a larger boreal zone characterized by cool summers and long cold winters. Warming since the 1850s, increases in annual mean temperature of at least 2 °C between 2000 and 2050 are highly probable. Annual mean temperatures across the Canadian boreal zone could be 4–5 °C warmer than today’s by 2100. All aspects of boreal forest ecosystem function are likely to be affected. Further, several potential “tipping elements” — where exposure to increasing changes in climate may trigger distinct shifts in ecosystem state — can be identified across the Canadian boreal zone. Approximately 40% of the forested area is underlain by permafrost, some of which is already degrading irreversibly, triggering a process of forest decline and re-establishment lasting several decades, while also releasing significant quantities of greenhouse gases that will amplify the future global warming trend. Warmer temperatures coupled with significant changes in the distribution and timing of annual precipitation are likely to cause serious tree-killing droughts in the west; east of the Great Lakes, however, where precipitation is generally nonlimiting, warming coupled with increasing atmospheric carbon dioxide may stimulate higher forest productivity. Large wildfires, which can cause serious economic losses, are expected to become more frequent, but increases in mean annual area burned will be relatively gradual. The most immediate threats could come from endemic forest insect pests that have the potential for population outbreaks in response to relatively small temperature increases. Quantifying the multiple effects of climate change will be challenging, particularly because there are great uncertainties attached to possible interactions among them, as well as with other land-use pressures. Considerable ingenuity will be needed from forest managers and scientists to address the formidable challenges posed by climate change to boreal ecosystems and develop effective strategies to adapt sustainable forest management practices to the impending changes.
The boreal zone and its ecosystems provide numerous provisioning, regulating, cultural, and supporting services. Because of its resources and its hydroelectric potential, Canada’s boreal zone is important to the country’s resource-based economy. The region presently occupied by Canada’s boreal zone has experienced dramatic changes during the past 3 million years as the climate cooled and repeated glaciations affected both the biota and the landscape. For about the past 7000 years, climate, fire, insects, diseases, and their interactions have been the most important natural drivers of boreal ecosystem dynamics, including rejuvenation, biogeochemical cycling, maintenance of productivity, and landscape variability. Layered upon natural drivers are changes increasingly caused by people and development and those related to human-caused climate change. Effects of these agents vary spatially and temporally, and, as global population increases, the demands and impacts on ecosystems will likely increase. Understanding how humans directly affect terrestrial and aquatic ecosystems in Canada’s boreal zone and how these effects and actions interact with natural disturbance agents is a prerequisite for informed and adaptive decisions about management of natural resources, while maintaining the economy and environment upon which humans depend. This paper reports on the genesis and present condition of the boreal zone and its ecosystems and sets the context for a detailed scientific investigation in subsequent papers published in this journal on several key aspects: carbon in boreal forests; climate change consequences, adaptation, and mitigation; nutrient and elemental cycling; protected areas; status, impacts, and risks of non-native species; factors affecting sustainable timber harvest levels; terrestrial and aquatic biodiversity; and water and wetland resources.
One of the roles of Land Surface Models (LSMs) is to partition evapotranspiration (E) into overstory transpiration (E_T), understory evapotranspiration (E_G), and wet canopy evaporation (E_C). Unfortunately, only a handful of studies have evaluated the performance of LSMs with E partitioning. Unlike dry canopies which are dominated by transpiration, wet canopies lead to the evaporation of intercepted water. In this respect, there is no better testing site for LSMs than the humid boreal forest, which is characterized by frequent precipitation and sustained evapotranspiration. This study assesses the performance of the Canadian Land Surface Scheme (CLASS) in simulating evapotranspiration and its components by applying detailed observations of water pathways and residence times in a forest canopy using a variety of methods (eddy covariance; sap flow, throughfall and stemflow measurements; and the stem compression approach). The study site was located in Montmorency Forest in Québec, Canada, a balsam fir boreal forest with ~1600 mm of annual precipitation. The field campaign was conducted during the summers of 2017 and 2018. To improve the accuracy of the simulations, we adjusted the definition of maximum canopy water storage, S = c x LAI by using c = 0.49 mm instead of the default value of 0.20 mm. This simple modification improved the performance of CLASS when simulating components of evapotranspiration, indicated by the increase in the modified Kling-Gupta efficiency (KGE') with rises between 0.01 and 0.23. Overall, CLASS performed well in simulating evapotranspiration and overstory transpiration in dry canopy conditions at half-hourly time steps with KGE' values between 0.67 and 0.81. The contribution of simulated E_T, E_G and E_C to total E during the two sampling periods were 46%, 24% and 30% respectively, which were comparable with the field observations (35%, 22% and 25%, respectively). In conclusion, CLASS was able to simulate E partitioning in dry and wet canopy conditions reasonably well at both seasonal and half-hourly time scales.
We quantified the response of peatland water table level (WTL) and energy fluxes to harvesting of a drained peatland forest. Two alternative harvests (clear-cut and partial harvest) were carried out in a mixed-species ditch-drained peatland forest in southern Finland, where water and energy balance components were monitored for six pre-treatment and three post-treatment growing seasons. To explore the responses caused by harvestings, we applied a mechanistic multi-layer soil-plant-atmosphere transfer model. At the clear-cut site, the mean growing season WTL rose by 0.18 ± 0.02 m (error estimate based on measurement uncertainty), while net radiation, and sensible and latent heat fluxes decreased after harvest. On the contrary, we observed only minor changes in energy fluxes and mean WTL (0.05 ± 0.03 m increase) at the partial harvest site, although as much as 70% of the stand basal area was removed and leaf-area index was reduced to half. The small changes were mainly explained by increased water use of spruce undergrowth and field layer vegetation, as well as increased forest floor evaporation. The rapid establishment of field layer vegetation had a significant role in energy balance recovery at the clear-cut site. At partial harvest, chlorophyll fluorescence measurements and model-data comparison suggested the shade-adapted spruce undergrowth was suffering from light stress during the first post-harvest growing season. We conclude that in addition to stand basal area, species composition and stand structure need to be considered when controlling WTL in peatland forests with partial harvesting. Our results have important implications on the operational use of continuous cover forestry on drained peatlands. A continuously maintained tree cover with significant evapotranspiration capacity could enable optimizing WTL from both tree growth and environmental perspectives.
In snow‐fed catchments, it is crucial to monitor and model the snow water equivalent (SWE), particularly when simulating the melt water runoff. SWE distribution can, however, be highly heterogeneous, particularly in forested environments. Within these locations, scant studies have explored the spatiotemporal variability in SWE in relation with vegetation characteristics, with only few successful attempts. The aim of this paper is to fill this knowledge gap, through a detailed monitoring at nine locations within a 3.49 km2 forested catchment in southern Québec, Canada (47°N, 71°W). The catchment receives an annual average of 633 mm of solid precipitation and is predominantly covered with balsam fir stands. Extracted from intensive field campaign and high‐resolution LiDAR data, this study explores the effect of fine scale forest features (tree height, tree diameter, canopy density, leaf area index, tree density and gap fraction) on the spatiotemporal variability in the SWE distribution. A nested stratified random sampling design was adopted to quantify small‐scale variability across the catchment and 1810 manual snow samples were collected throughout the consecutive winters of 2016–17 and 2017–18. This study explored the variability of SWE using coefficients of variation (CV) and relating to the leaf area index (LAI). We also present existing spatiotemporal differences in maximum snow depth across different stands and its relationship with average tree diameter. Furthermore, exploiting key vegetation characteristics, this paper explores different approaches to model SWE, such as multiple linear regression (MLR), binary regression tree (BRT) and neural networks (NN). We were unable to establish any relationship between the CV of SWE and the LAI. However, we observed an increase in maximum snow depth with decreasing tree diameter, suggesting an association between these variables. NN modelling (NSE = 0.71) revealed that, snow depth, snowpack age and forest characteristics (tree diameter and tree density) are key controlling variables on SWE. Using only variables that are deemed to be more readily available (snow depth, tree height, snowpack age and elevation), NN performance falls by only 7% (NSE = 0.66). This article is protected by copyright. All rights reserved.
We designed an open source device (EVAP-FOR) to monitor soil profile (soil water content and soil temperature at 3 depths) and air conditions (temperature, relative humidity)in terrestrial ecosystem. Assessement of soil water content is done using low-cost capacitance probe. We first converted the measured voltage unit for each capacitance probe into relative permittivity using 10 standard solutions and developed a physical based model that used relative permittivity, bulk density and particles density to assess soil water content in soil cores. To test the robustness of our design and model, we built 12 data logger sand used 36 probes to assess water content in 36 soil cores that representing 12 soil texture (clayey to sandy). Preliminary results showed that capacitance probe were able to explain 86% of the total variance of soil water content in an independent dataset of 12 soil cores. The analysis of the residuals extracted from the calibration curves developed using standard solutions showed that there was no effect of the device (0% of the variance) on the measurement, but a small effect of the capacitance probe was detected (15%). This effect can be alleviated by the use of calibration curves that are specific to each probe. The low-cost of EVAP-FOR data logger provides the opportunity to develop local high spatial density networks of monitoring stations. Such networks will be useful to understand the spatio-temporal dynamics of terrestrial ecosystem productivity and resilience, for example, at watershed scale.
The boreal forest will be strongly affected by climate change and in turn, these vast ecosystems may significantly impact global climatology and hydrology due to their exchanges of carbon and water with the atmosphere. It is now crucial to understand the intricate relationships between precipitation and evapotranspiration in these environments, particularly in less-studied locations characterized by a cold and humid climate. This study
presents state-of-the-art measurements of energy and water budgets components over three years (2016–2018) at the Montmorency Forest, Québec, Canada: a balsam fir boreal forest that receives ∼1600mm of precipitation annually (continental subarctic climate; Köppen classification subtype Dfc). Precipitation, evapotranspiration and potential evapotranspiration at the site are compared with observations from thirteen experimental sites around the world. These intercomparison sites (89 study-years) encompass various types of climate and vegetation (black spruces, jack pines, etc.) encountered in boreal forests worldwide. The Montmorency Forest stands out by receiving the largest amount of precipitation. Across all sites, water availability seems to be the principal evapotranspiration constraint, as precipitation tends to be more influential than potential evapotranspiration and other factors. This leads to the Montmorency Forest generating the largest amount of evapotranspiration, on average ∼550mm y−1. This value appears to be an ecosystem maximum for evapotranspiration, which may be explained either by a physiological limit or a limited energy availability due to the presence of cloud cover. The
Montmorency Forest water budget evacuates the precipitation excess mostly by watershed discharges, at an average rate of ∼1050mm y−1, with peaks during the spring freshet. This behaviour, typical of mountainous headwater basins, necessarily influence downstream hydrological regimes to a large extent. This study provides a much needed insight in the hydrological regimes of a humid boreal-forested mountainous watershed, a type of
basin rarely studied with precise energy and water budgets before.
Forest canopies act as permeable barriers between the atmosphere and the ground, reflecting and absorbing solar radiation. In the boreal forest, the large number of gaps and heterogeneities further complicates these processes. Several studies have adequately measured and modeled the transmittance of solar radiation through forest canopies in western North America and Scandinavia, but few have addressed those of Eastern North America. Furthermore, most of these studies have assessed the effects of solar radiation transmittance on snowpack energetics, but few have focused on the hydrological impacts during the growing season. This paper addresses this knowledge gap with precise measurements of sub-canopy solar radiation in a juvenile balsam fir forest located in the Montmorency Forest, Quebec, Canada. Twenty (20) sub-canopy stations were deployed in a 200 m by 150 m gridded box around a flux tower measuring above canopy radiation and eddy covariance fluxes during late summer and early fall 2016. Results show that the heterogeneous forest has substantial spatial variability of transmittance, with site-specific seasonal averages ranging between 0.07 and 0.69. Canopy gaps of size relative to tree height (H) between 0.1H and H had a temporal influence on solar radiation transmittance in canopy gaps at the sub-daily scale, but do not influence seasonal trends. This is attributed to very frequent cloudiness at the site, which renders the solar radiation mostly diffuse. As a result, a Beer-Lambert extinction law proved adequate at modeling site-specific or spatially averaged transmittance on a seasonal basis. We complement the observations by modeling canopy and soil moisture balances at 20 sites using the Canadian Land Surface Scheme (CLASS). The modeling results exhibit the following trend: a thicker (thinner) vegetation leads to more (less) evapotranspiration, because there is more (less) evaporation of intercepted precipitation and more (less) transpiration, but less (more) ground evaporation. During drier periods, the latter leads to wetter soil conditions for the thicker vegetation. These modeling results of sensitivity to vegetation density, while informative, still need to be confirmed with observations.
To address certain limitations with their current operational model, Environment and Climate Change Canada recently developed the Soil, Vegetation, and Snow (SVS) land surface model and the representation of subsurface hydrological processes was targeted as an area for improvement. The objective of this study is to evaluate the ability of HydroSVS, the component of SVS responsible for the vertical redistribution of water, to simulate soil moisture under snow-free conditions when using flux-tower observations of evapotranspiration as forcing data. We assessed (1) model fidelity by comparing soil moisture modelled with HydroSVS to point-scale measurements of volumetric soil water content and (2) model complexity by comparing the performance of HydroSVS to that of HydroGeoSphere, a state-of-the-art integrated surface and subsurface hydrologic model. To do this, we performed one-dimensional soil column simulations at four sites of the AmeriFlux network. Results indicate that under Mediterranean and temperate climates, HydroSVS satisfactorily simulated soil moisture (Nash-Sutcliffe efficiency between 0.26 and 0.70; R² ≥ 0.80), with a performance comparable to HydroGeoSphere (Nash-Sutcliffe efficiency ≥0.60; R² ≥ 0.80). However, HydroSVS performed weakly under a semiarid climate while HydroGeoSphere performed relatively well. By decoupling the magnitude and sourcing of evapotranspiration, this study proposes a powerful diagnostic tool to evaluate the representation of subsurface hydrological processes in land surface models. Overall, this study highlights the potential of SVS for hydrological applications.
Leaf area index (LAI) is increasing throughout the globe, implying Earth greening. Global modeling studies support this contention, yet satellite observations and model simulations have never been directly compared. Here, for the first time, a coupled land-climate model was used to quantify the potential impact of the satellite-observed Earth greening over the past 30 years on the terrestrial water cycle. The global LAI enhancement of 8% between the early 1980s and the early 2010s is modeled to have caused increases of 12.0 ± 2.4 mm yr⁻¹ in evapotranspiration and 12.1 ± 2.7 mm yr⁻¹ in precipitation-about 55% ± 25% and 28% ± 6% of the observed increases in land evapotranspiration and precipitation, respectively. In wet regions, the greening did not significantly decrease runoff and soil moisture because it intensified moisture recycling through a coincident increase of evapotranspiration and precipitation. But in dry regions, including the Sahel, west Asia, northern India, the western United States, and the Mediterranean coast, the greening was modeled to significantly decrease soil moisture through its coupling with the atmospheric water cycle. This modeled soil moisture response, however, might have biases resulting from the precipitation biases in the model. For example, the model dry bias might have underestimated the soil moisture response in the observed dry area (e.g., the Sahel and northern India) given that the modeled soil moisture is near the wilting point. Thus, an accurate representation of precipitation and its feedbacks in Earth system models is essential for simulations and predictions of how soil moisture responds to LAI changes, and therefore how the terrestrial water cycle responds to climate change.
This paper outlines the design and experimentation of an Empirical Multistructure Framework (EMF) for lumped conceptual hydrological modelling. This concept is inspired from modular frameworks, empirical model development, and multimodel applications, and encompasses the overproduce and select paradigm. The EMF concept aims to reduce subjectivity in conceptual hydrological modelling practice and includes model selection in the optimisation steps, reducing initial assumptions on the prior perception of the dominant rainfall-runoff transformation processes. EMF generates thousands of new modelling options from, for now, twelve parent models that share their functional components and parameters. Optimisation resorts to ensemble calibration, ranking and selection of individual child time series based on optimal bias and reliability trade-offs, as well as accuracy and sharpness improvement of the ensemble. Results on 37 snow-dominated Canadian catchments and 20 climatically-diversified American catchments reveal the excellent potential of the EMF in generating new individual model alternatives, with high respective performance values, that may be pooled efficiently into ensembles of seven to sixty constitutive members, with low bias and high accuracy, sharpness, and reliability. A group of 1446 new models is highlighted to offer good potential on other catchments or applications, based on their individual and collective interests. An analysis of the preferred functional components reveals the importance of the production and total flow elements. Overall, results from this research confirm the added value of ensemble and flexible approaches for hydrological applications, especially in uncertain contexts, and open up new modelling possibilities.
Earth Observing Systems are now routinely used to infer leaf-area index (LAI) given its significance in spatial aggregation of land-surface fluxes. Whether LAI is an appropriate scaling parameter for daytime growing-season energy budget, surface conductance (Gs ), water and light use efficiency and surface-atmosphere coupling of European boreal coniferous forests was explored using eddy-covariance (EC) energy and CO2 fluxes. The observed scaling relations were then explained using a biophysical multi-layer soil-vegetation-atmosphere transfer model as well as by a bulk Gs representation. The LAI variations significantly alter radiation regime, within canopy microclimate, sink/source distributions of CO2 , H2 O and heat, and forest floor fluxes. The contribution of forest floor to ecosystem scale energy exchange is shown to decrease asymptotically with increased LAI, as expected. Compared to other energy budget components, dry-canopy evapotranspiration (ET) was reasonably 'conservative' over the studied LAI range 0.5-7.0 m(2) m(-2) . Both ET and Gs experienced a minimum in the LAI range 1-2 m(2) m(-2) caused by opposing non-proportional response of stomatally controlled transpiration and 'free' forest floor evaporation to changes in canopy density. The young forests had strongest coupling with the atmosphere while stomatal control of energy partitioning was strongest in relatively sparse (LAI ~2 m(2) m(-2) ) pine stands growing on mineral soils. The data analysis and model results suggest that LAI may be an effective scaling parameter for net radiation and its partitioning but only in sparse stands (LAI <3 m(2) m(-2) ). This finding emphasizes the significance of stand-replacing disturbances on the controls of surface energy exchange. In denser forests, any LAI -dependency varies with physiological traits such as light-saturated water use efficiency. The results suggest that incorporating species traits and site conditions are necessary when LAI is used in upscaling energy exchanges of boreal coniferous forests. This article is protected by copyright. All rights reserved.
Few of the infiltration models in current use are suitable for the
situation in which the rainfall intensity is initially less than the
infiltration capacity of the soil. In this paper a simple two-stage
model is developed for infiltration under a constant intensity rainfall
into a homogeneous soil with uniform initial moisture content. The first
stage predicts the volume of infiltration to the moment at which surface
ponding begins. The second stage, which is the Green-Ampt model modified
for the infiltration prior to surface saturation, describes the
subsequent infiltration behavior. A method for estimating the mean
suction of the wetting front is given. Comparison of the model
predictions with experimental data and numerical solutions of the
Richards equation for several soil types shows excellent agreement.
The observation program of the International Geophysical Year has provided new meteorological data that have made possible more precise computations of the heat balance of the earth. Greater accuracy has also been achieved through improvement or computation methods. Using data for 2,000 stations, including 300 points in ocean areas, the authors have constructed world maps for major components of the heat balance. From these maps they have derived mean latitudinal values of the components (radiation balance, loss of heat in evaporation, turbulent heat exchange, redistribution of heat through ocean currents). The findings have been generalized for continents, oceans and the earth as a whole.
Aims
In the mid- and high-latitude regions, three quarters of the land surface is covered by boreal conifer forests, and snow lasts for 6–8 months of the year. Correctly modeling surface energy balance and snowmelt at mid- and high-latitudes has a significant influence on climate and hydrological processes. However, the heterogeneous and clumped forest structure exerts important control over the radiative energy at the forest floor, which results in large variations of underneath snow cover and snowmelt rate. The goal of this study is to investigate the impact of hierarchically clumped vegetation structure in boreal forest on snowmelt and exchanges of energy and water.
Methods
We used a simple Clumped Canopy Scheme (CCS) for canopy radiation transfer to characterize the impact of the clumped forest structure on net radiation at the snow surface underneath forests. The CCS was integrated with the Variable Infiltration Capacity macroscale hydrological model (herein referred to as VIC-CCS) to characterize the impact of clumped vegetation structure on surface energy balance and snowmelt during the snow season. A twin simulation, VIC-CCS and the standard VIC model, was performed to isolate the impact of CCS on the energy and water fluxes and snowmelt rates. The simulation results were compared to in situ measurements at four different forest stands: old aspen forest in the Southern Study Area (SOA), black spruce forests in the Southern and Northern Study Areas (SOBS and NOBS) and fen wetland in the Northern Study Area (NFEN) within the Boreal Ecosystem–Atmosphere Study (BOREAS) region in central Canada during 1994 to1996.
Important Findings
Simulations showed that the implementation of CCS has reduced incoming long-wave radiation at the underlying snow surface and, thereby, lowered the snowmelt rate. Comparison against ground observations of net radiation and surface flux rates showed a reasonable agreement while demonstrating implementation of CCS can markedly improve model surface energy budget and energy inputs computation for snowmelt. The modeled snowmelt matches reasonably well with observations with root mean square error (RMSE) ranging from 16.51 to 19.81 mm using VIC-CCS versus 29.86 to 32.61 mm for VIC only in the four forest sites. The improvement is the most significant for the deciduous forest (old aspen) site, reducing RMSE by16 mm. This study demonstrates that taking into account the effect of the clumped forest structure in land surface parameterization schemes is critical for snowmelt prediction in the boreal regions.
The response was measured of stomatal conductance and leaf photosynthesis to changing leaf water potential in the legume siratro subjected to a sequence of I-week cycles of increasing soil water deficit followed by watering. The response of stomatal conductance was described using a continuous mathematical function, which is more robust and accurate than the usual discontinuous linear function used to analyse such data.
After seven successive cycles of water deficit, there was no apparent adjustment of the short-term response of leaf conductance to leaf water potential.