Impact of variability in measured and simulated tundra snowpack properties on heat transfer metrics

2021 ◽  
Author(s):  
Victoria Dutch ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Mel Sandells ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Field Measurements ◽  
Density Profiles ◽  
Community Land Model ◽  
Spatially Distributed ◽  
Snowpack Properties ◽  
The Impact

<p>Tundra snowpack properties are highly heterogenous over a variety of spatial scales and evolve over the course of the winter. Variations in snowpack properties such as snow density and microstructure control the transfer of heat through the snowpack. Thermal properties of the snowpack impact the subnivean environment; snow insulates the underlying soil, allowing films of liquid water to remain unfrozen, enabling biological processes to take place. In this study, field measurements from four field campaigns across two different winters (March and November 2018, January and March 2019) are used to capture and constrain the spatial variability of the snowpack. These include 1050 spatially distributed Snow MicroPenetrometer (SMP) profiles throughout the Trail Valley Creek catchment in the Northwest Territories, Canada. Bespoke coefficients for tundra snowpacks were calculated (based on the work of King et al., 2020) to convert raw SMP force measurements to densities. This allowed density changes of vertical profiles to be assessed and spatial variability in the thickness and properties of three snowpack layers (wind slab, indurated hoar and depth hoar) to be quantified. 105 needleprobe measurements from 37 snowpits were used to contrast the density and thermal conductivity of snowpack layers, as well as thermal conductivities estimated from recalibrated SMP density profiles. These in-situ measurements will be compared to 1-D simulations of snowpack properties from the Community Land Model (PTCLM 5.0) over the two winter seasons. The impact of snowpack layering on snow heat transfer metrics will be investigated using both 2-layer (wind slab: depth hoar) and 3-layer (wind slab: indurated hoar: depth hoar) snowpack configurations. The spatial variability of heat transfer metrics across the Trail Valley Creek catchment will also be considered.</p>

scholarly journals Impact of measured and simulated tundra snowpack properties on heat transfer

2021 ◽  
Author(s):  
Victoria R. Dutch ◽  
Nick Rutter ◽  
Leanne Wake ◽  
Melody Sandells ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Field Measurements ◽  
Cold Bias ◽  
Vertical Resolution ◽  
Community Land Model ◽  
Snow Properties ◽  
Soil Temperatures ◽  
Snowpack Properties ◽  
Larger Sample

Abstract. Snowpack microstructure controls the transfer of heat to, and the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two different winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow MicroPenetrometer profiles allowed snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n = 1050) compared to traditional snowpit observations (3 cm vertical resolution; n = 115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE = 5.8 °C). Bias-correction of the simulated thermal conductivity (relative to field measurements) improved simulated soil temperatures (RMSE = 2.1 °C). Multiple linear regression shows the required correction factor is strongly related to snow depth (R2 = 0.77, RMSE = 0.066) particularly early in the winter. Furthermore, CLM simulations did not adequately represent the observed high proportions of depth hoar. Addressing uncertainty in simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures act as a control on subnivean soil respiration, and hence impact Arctic winter carbon fluxes and budgets.

scholarly journals Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

2014 ◽  
Vol 11 (23) ◽  
pp. 6827-6840 ◽  
Author(s):  
M. Réjou-Méchain ◽  
H. C. Muller-Landau ◽  
M. Detto ◽  
S. C. Thomas ◽  
T. Le Toan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Spatial Scales ◽  
Forest Biomass ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Forest Degradation ◽  
Forest Carbon ◽  
Mitigation Strategies ◽  
Large Plot

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor

2020 ◽  
Author(s):  
Dragana Panic ◽  
Isabella Pfeil ◽  
Andreas Salentinig ◽  
Mariette Vreugdenhil ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Frequency ◽  
Cosmic Ray ◽  
Irrigation Scheduling ◽  
Water Index ◽  
The Impact

<p>Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.</p><p>Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.</p><p>In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria’s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.</p><p>[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004<br>[2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030<br>[3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X</p>

scholarly journals Understanding Intra-Annual Dynamics of Ecosystem Services Using Satellite Image Time Series

2020 ◽  
Vol 12 (4) ◽  
pp. 710 ◽  
Author(s):  
Trinidad del Río-Mena ◽  
Louise Willemen ◽  
Anton Vrieling ◽  
Andy Nelson
Keyword(s):  
Ecosystem Services ◽  
Spatial Variability ◽  
Ecosystem Service ◽  
Vegetation Cover ◽  
Agricultural Land ◽  
Supply And Demand ◽  
Satellite Image ◽  
Field Measurements ◽  
Temporal Variations ◽  
The Impact

Landscape processes fluctuate over time, influencing the intra-annual dynamics of ecosystem services. However, current ecosystem service assessments generally do not account for such changes. This study argues that information on the dynamics of ecosystem services is essential for understanding and monitoring the impact of land management. We studied two regulating ecosystem services (i. erosion prevention, ii. regulation of water flows) and two provisioning services (iii. provision of forage, iv. biomass for essential oil production) in thicket vegetation and agricultural fields in the Baviaanskloof, South Africa. Using models based on Sentinel-2 data, calibrated with field measurements, we estimated the monthly supply of ecosystem services and assessed their intra-annual variability within vegetation cover types. We illustrated how the dynamic supply of ecosystem services related to temporal variations in their demand. We also found large spatial variability of the ecosystem service supply within a single vegetation cover type. In contrast to thicket vegetation, agricultural land showed larger temporal and spatial variability in the ecosystem service supply due to the effect of more intensive management. Knowledge of intra-annual dynamics is essential to jointly assess the temporal variation of supply and demand throughout the year to evaluate if the provision of ecosystem services occurs when most needed.

Using fractal geometry to make rapid field measurements of riverbed topography at ecologically useful spatial scales

2002 ◽  
Vol 53 (6) ◽  
pp. 999 ◽  
Author(s):  
B. J. Robson ◽  
E. T. Chester ◽  
L. A. Barmuta
Keyword(s):  
Fractal Dimension ◽  
Fractal Geometry ◽  
Random Sampling ◽  
Spatial Scales ◽  
Field Measurements ◽  
Habitat Types ◽  
River Bed ◽  
Bed Roughness ◽  
Rolling Balls

A method is described for making rapid in situ field measurements of riverbed topography over spatial scales of ≅1–10 m. This method uses rolling balls to make quick, accurate measurements of river-bed roughness at several spatial scales. Random sampling and replication generate multiple estimates of the fractal dimension (d) that can be used to test for significant differences in the complexity of riverbed architecture between habitat types and spatial scales.

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Snow Layer ◽  
Data Set ◽  
Correlation Lengths ◽  
Snow Water ◽  
Snowpack Properties ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across 9 trenches) collected over two winters at Trail Valley Creek, NWT, Canada, were applied in synthetic radiative transfer experiments. This allowed robust assessment of the impact of first guess information of snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability of total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths were sub-metre for all layers. Depth hoar was consistently ~ 30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and SSA of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks

Global In-Situ Observations of Essential Climate and Ocean Variables by the Global Drifter Program. Applications and Impacts

2020 ◽  
Author(s):  
Luca Centurioni ◽  
Verena Hormann
Keyword(s):  
North Atlantic Ocean ◽  
Spatial Scales ◽  
Data Access ◽  
Ocean Climate ◽  
The North ◽  
Free Data ◽  
Forecast Models ◽  
In Situ Observations ◽  
The Impact

<p>Accurate estimates and forecasts of physical and biogeochemical processes at the air-sea interface must rely on integrated in-situ and satellite surface observations of essential Ocean/Climate Variables (EOVs /ECVs). Such observations, when sustained over appropriate temporal and spatial scales, are particularly powerful in constraining and improving the skills, impact and value of weather, ocean and climate forecast models. The calibration and validation of satellite ocean products also rely on in-situ observations, thus creating further positive high-impact applications of observing systems designed for global sustained observations of EOV and ECVs.</p><p>The Global Drifter Program has operated uninterrupted for several decades and constitutes a particular successful example of a network of multiparametric platforms providing observations of climate, weather and oceanographic relevance (e.g. air-pressure, sea surface temperature, ocean currents). This presentation will review the requirements of sustainability of an observing system such as the GDP (i.e. cost effectiveness, peer-review of the observing methodology and of the technology, free data access and international cooperation), will present some key metrics recently used to quantify the impact of drifter observations, and will discuss two prominent examples of GDP regional observations and the transition to operations of novel platforms, such us wind and directional wave spectra drifters, in sparsely sampled regions of the Arabian Sea and of the North Atlantic Ocean.</p>

Modeling global anthropogenic erosion in the Holocene

The Holocene ◽  
2018 ◽  
Vol 29 (3) ◽  
pp. 367-379 ◽  
Author(s):  
Zhengang Wang ◽  
Kristof Van Oost
Keyword(s):  
Land Use ◽  
Spatial Variability ◽  
Natural Vegetation ◽  
Catchment Scale ◽  
Land Use Scenario ◽  
Soil Production ◽  
Wide Range ◽  
Spatially Distributed ◽  
Agricultural Use ◽  
The Impact

A large proportion of natural vegetation has been converted to agricultural use, and this typically accelerates erosion by one to two orders of magnitude. Quantification of this accelerated erosion is important to understand the impact of human activities on soil ecosystem service given that soil erosion induces soil degradation and changes in soil organic carbon (SOC) stocks. Until now, few studies have evaluated the accumulated impact of agricultural erosion, since the start of agriculture (ca. 6000 BC), on the soils system and the carbon cycle. In this study, we mainly focused on the enhanced water erosion by conversion of natural vegetation to crops, while wind erosion on the cropland is not assessed. We first evaluated and constrained existing anthropogenic land cover change (ALCC) scenarios by comparing observed cumulative erosion for the agricultural period under a wide range of global agro-ecological conditions with model simulations. An optimized land-use scenario that makes the best fit between the simulation and the observation was derived in the model calibration. We further applied a spatially distributed erosion model, which was modified based on Revised Universal Soil Loss Equation (RUSLE), under the optimized land-use scenario across globe to estimate the total anthropogenic cumulative erosion and characterize their spatial variability. Simulations suggest that conversion from natural vegetation to cropland has caused a global cumulative agricultural erosion of 27,187 ± 9030 Pg for the period of agriculture. This results in an average cumulative sediment mobilization of 1829 ± 613 kg m−2 on croplands, corresponding to a soil truncation of ca. 1.34 ± 0.45 m. Regions of early civilization, particularly with high cropland fractions such as South Asia, Southeast Asia, and Central America have higher area-averaged anthropogenic erosion than other regions. This results in spatial variability in soil truncation rates because of erosion, which would further affect the soil production rate. Our study shows that observations of long-term anthropogenic erosion at the catchment scale can be used to constrain the reconstructed land-use scenarios.

scholarly journals Fractional Vegetation Cover Derived from UAV and Sentinel-2 Imagery as a Proxy for In Situ FAPAR in a Dense Mixed-Coniferous Forest?

2022 ◽  
Vol 14 (2) ◽  
pp. 380
Author(s):  
Birgitta Putzenlechner ◽  
Philip Marzahn ◽  
Philipp Koal ◽  
Arturo Sánchez-Azofeifa
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Vegetation Cover ◽  
Satellite Remote Sensing ◽  
Coniferous Forest ◽  
Spatiotemporal Variability ◽  
Fractional Vegetation Cover ◽  
Spatially Distributed ◽  
Sentinel 2

The fraction of absorbed photosynthetic active radiation (FAPAR) is an essential climate variable for assessing the productivity of ecosystems. Satellite remote sensing provides spatially distributed FAPAR products, but their accurate and efficient validation is challenging in forest environments. As the FAPAR is linked to the canopy structure, it may be approximated by the fractional vegetation cover (FCOVER) under the assumption that incoming radiation is either absorbed or passed through gaps in the canopy. With FCOVER being easier to retrieve, FAPAR validation activities could benefit from a priori information on FCOVER. Spatially distributed FCOVER is available from satellite remote sensing or can be retrieved from imagery of Unmanned Aerial Vehicles (UAVs) at a centimetric resolution. We investigated remote sensing-derived FCOVER as a proxy for in situ FAPAR in a dense mixed-coniferous forest, considering both absolute values and spatiotemporal variability. Therefore, direct FAPAR measurements, acquired with a Wireless Sensor Network, were related to FCOVER derived from UAV and Sentinel-2 (S2) imagery at different seasons. The results indicated that spatially aggregated UAV-derived FCOVER was close (RMSE = 0.02) to in situ FAPAR during the peak vegetation period when the canopy was almost closed. The S2 FCOVER product underestimated both the in situ FAPAR and UAV-derived FCOVER (RMSE > 0.3), which we attributed to the generic nature of the retrieval algorithm and the coarser resolution of the product. We concluded that UAV-derived FCOVER may be used as a proxy for direct FAPAR measurements in dense canopies. As another key finding, the spatial variability of the FCOVER consistently surpassed that of the in situ FAPAR, which was also well-reflected in the S2 FAPAR and FCOVER products. We recommend integrating this experimental finding as consistency criteria in the context of ECV quality assessments. To facilitate the FAPAR sampling activities, we further suggest assessing the spatial variability of UAV-derived FCOVER to benchmark sampling sizes for in situ FAPAR measurements. Finally, our study contributes to refining the FAPAR sampling protocols needed for the validation and improvement of FAPAR estimates in forest environments.

scholarly journals Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales

2017 ◽  
Vol 21 (5) ◽  
pp. 2301-2320 ◽  
Author(s):  
Gabriele Baroni ◽  
Matthias Zink ◽  
Rohini Kumar ◽  
Luis Samaniego ◽  
Sabine Attinger
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Spatial Scales ◽  
Strong Support ◽  
Grid Resolution ◽  
Temporal Scales ◽  
Soil Map ◽  
The Impact ◽  
Soil Spatial Variability

Abstract. Soil properties show high heterogeneity at different spatial scales and their correct characterization remains a crucial challenge over large areas. The aim of the study is to quantify the impact of different types of uncertainties that arise from the unresolved soil spatial variability on simulated hydrological states and fluxes. Three perturbation methods are presented for the characterization of uncertainties in soil properties. The methods are applied on the soil map of the upper Neckar catchment (Germany), as an example. The uncertainties are propagated through the distributed mesoscale hydrological model (mHM) to assess the impact on the simulated states and fluxes. The model outputs are analysed by aggregating the results at different spatial and temporal scales. These results show that the impact of the different uncertainties introduced in the original soil map is equivalent when the simulated model outputs are analysed at the model grid resolution (i.e. 500 m). However, several differences are identified by aggregating states and fluxes at different spatial scales (by subcatchments of different sizes or coarsening the grid resolution). Streamflow is only sensitive to the perturbation of long spatial structures while distributed states and fluxes (e.g. soil moisture and groundwater recharge) are only sensitive to the local noise introduced to the original soil properties. A clear identification of the temporal and spatial scale for which finer-resolution soil information is (or is not) relevant is unlikely to be universal. However, the comparison of the impacts on the different hydrological components can be used to prioritize the model improvements in specific applications, either by collecting new measurements or by calibration and data assimilation approaches. In conclusion, the study underlines the importance of a correct characterization of uncertainty in soil properties. With that, soil maps with additional information regarding the unresolved soil spatial variability would provide strong support to hydrological modelling applications.

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