scholarly journals Snow-Covered Soil Temperature Retrieval in Canadian Arctic Permafrost Areas, Using a Land Surface Scheme Informed with Satellite Remote Sensing Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1703
Author(s):  
Nicolas Marchand ◽  
Alain Royer ◽  
Gerhard Krinner ◽  
Alexandre Roy ◽  
Alexandre Langlois ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Conductivity ◽  
Soil Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Thermal State ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Land Surface Scheme ◽  
Surface Scheme

High-latitude areas are very sensitive to global warming, which has significant impacts on soil temperatures and associated processes governing permafrost evolution. This study aims to improve first-layer soil temperature retrievals during winter. This key surface state variable is strongly affected by snow’s geophysical properties and their associated uncertainties (e.g., thermal conductivity) in land surface climate models. We used infrared MODIS land-surface temperatures (LST) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) brightness temperatures (Tb) at 10.7 and 18.7 GHz to constrain the Canadian Land Surface Scheme (CLASS), driven by meteorological reanalysis data and coupled with a simple radiative transfer model. The Tb polarization ratio (horizontal/vertical) at 10.7 GHz was selected to improve snowpack density, which is linked to the thermal conductivity representation in the model. Referencing meteorological station soil temperature measurements, we validated the approach at four different sites in the North American tundra over a period of up to 8 years. Results show that the proposed method improves simulations of the soil temperature under snow (Tg) by 64% when using remote sensing (RS) data to constrain the model, compared to model outputs without satellite data information. The root mean square error (RMSE) between measured and simulated Tg under the snow ranges from 1.8 to 3.5 K when using RS data. Improved temporal monitoring of the soil thermal state, along with changes in snow properties, will improve our understanding of the various processes governing soil biological, hydrological, and permafrost evolution.

A LONG TERM FIELD EXPERIMENT FOR RADIATIVE TRANSFER MODEL DEVELOPMENT AND LAND SURFACE PROCESSES REMOTE SENSING

2008 ◽  
Vol 52 ◽  
pp. 13-18
Author(s):  
Hui LU ◽  
Toshio KOIKE ◽  
Hiroyuki TSUTSUI ◽  
David Ndegwa KURIA ◽  
Tobias GRAF ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Field Experiment ◽  
Land Surface ◽  
Model Development ◽  
Surface Processes ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Term Field

scholarly journals On the Performance of the Canadian Land Surface Scheme Driven by the ERA5 Reanalysis over the Canadian Boreal Forest

2020 ◽  
Vol 21 (6) ◽  
pp. 1383-1404 ◽  
Author(s):  
M. Alves ◽  
D. F. Nadeau ◽  
B. Music ◽  
F. Anctil ◽  
A. Parajuli
Keyword(s):  
Surface Energy ◽  
Soil Temperature ◽  
Land Surface ◽  
Snow Depth ◽  
Water Fluxes ◽  
State Variables ◽  
Land Surface Scheme ◽  
Canadian Land Surface Scheme ◽  
Simulated Surface ◽  
Surface Scheme

AbstractThe Canadian Land Surface Scheme (CLASS) has been applied over the years in coupled and uncoupled (offline) modes at local, regional, and global scales using various forcing datasets. 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.

scholarly journals Representation of Water Table Dynamics in a Land Surface Scheme. Part II: Subgrid Variability

2005 ◽  
Vol 18 (12) ◽  
pp. 1881-1901 ◽  
Author(s):  
Pat J-F. Yeh ◽  
Elfatih A. B. Eltahir
Keyword(s):  
Groundwater Recharge ◽  
Water Table ◽  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Land Surface Scheme ◽  
Groundwater Runoff ◽  
Aquifer Storage ◽  
Mosaic Approach ◽  
Surface Scheme

Abstract A lumped unconfined aquifer model has been developed and interactively coupled to a land surface scheme in a companion paper. Here, the issue of the representation of subgrid variability of water table depths (WTDs) is addressed. A statistical–dynamical (SD) approach is used to account for the effects of the unresolved subgrid variability of WTD in the grid-scale groundwater runoff. The dynamic probability distribution function (PDF) of WTD is specified as a two-parameter gamma distribution based on observations. The grid-scale groundwater rating curve (i.e., aquifer storage–discharge relationship) is derived statistically by integrating a point groundwater runoff model with respect to the PDF of WTD. Next, a mosaic approach is utilized to account for the effects of subgrid variability of WTD in the grid-scale groundwater recharge. A grid cell is categorized into different subgrids based on the PDF of WTD. The grid-scale hydrologic fluxes are computed by averaging all of the subgrid fluxes weighted by their fractions. This new methodology combines the strengths of the SD approach and the mosaic approach. The results of model testing in Illinois from 1984 to 1994 indicate that the simulated hydrologic variables (soil saturation and WTD) and fluxes (evaporation, runoff, and groundwater recharge) agree well with the observations. Because of the paucity of the large-scale observations on WTD, the development of a practical parameter estimation procedure is indispensable before the global implementation of the developed scheme of water table dynamics in climate models.

scholarly journals Estimating Potential Evaporation from Vegetated Surfaces for Water Management Impact Assessments Using Climate Model Output

2011 ◽  
Vol 12 (5) ◽  
pp. 1127-1136 ◽  
Author(s):  
Victoria A. Bell ◽  
Nicola Gedney ◽  
Alison L. Kay ◽  
Roderick N. B. Smith ◽  
Richard G. Jones ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Composition ◽  
Hydrological Models ◽  
Potential Evaporation ◽  
Land Surface Scheme ◽  
Surface Scheme

Abstract River basin managers concerned with maintaining water supplies and mitigating flood risk in the face of climate change are taking outputs from climate models and using them in hydrological models for assessment purposes. While precipitation is the main output used, evaporation is attracting increasing attention because of its significance to the water balance of river basins. Climate models provide estimates of actual evaporation that are consistent with their simplified land surface schemes but do not naturally provide the estimates of potential evaporation (PE) commonly required as input to hydrological models. There are clear advantages in using PE estimates controlled by atmospheric forcings when using stand-alone hydrological models with integral soil-moisture accounting schemes. The atmosphere–land decoupling approximation that PE provides can prove to be of further benefit if it is possible to account for the effect of different, or changing, land cover on PE outside of the climate model. The methods explored here estimate Penman–Monteith PE from vegetated surfaces using outputs from climate models that have an embedded land surface scheme. The land surface scheme enables an examination of the dependence of canopy stomatal resistance on atmospheric composition, and the sensitivity of PE estimates to the choice of canopy resistance values under current and changing climates is demonstrated. The conclusions have practical value for climate change impact studies relating to flood, drought, and water management applications.

scholarly journals Dust Storm Remote Sensing Monitoring Supported by MODIS Land Surface Reflectance Database

2019 ◽  
Vol 11 (15) ◽  
pp. 1772
Author(s):  
Ke Sun ◽  
Qinghua Su ◽  
Yanfang Ming
Keyword(s):  
Remote Sensing ◽  
Dust Storm ◽  
Land Surface ◽  
Dust Storms ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Surface Reflectance ◽  
Monitoring Method ◽  
Remote Sensing Monitoring ◽  
Land Surface Reflectance

MODIS (Moderate Resolution Imaging Spectroradiometer) land product subsets can provide high-quality prior knowledge for the quantitative inversion of land and atmospheric parameters. Using the LSR (Land Surface Reflectance) dataset, dust storm remote sensing monitoring in this study was carried out via quality control and data synthesis. A dynamic threshold supported dust storm monitoring method was proposed based on a monthly synthesized LSR database, which is produced using MOD09A1 data. The apparent reflectance of clear-pixels with different atmospheric conditions was simulated by the radiative transfer model. A pixel can be identified as a dust pixel if the apparent reflectance is larger than that of the simulated data. The proposed method was applied to the monitoring of four dust storms, the results of which were evaluated and analyzed via visual interpretation, MICAPS (Meteorological Information Comprehensive Analysis and Process System), and the OMI AI (Ozone Monitoring Instrument Aerosol Index) with the following conclusions: the dust storm monitoring results showed that most of the dust areas could be accurately detected when compared with the true color composite images, and the dust monitoring results agreed well with the MICAPS observation station data and the OMI AI dust products.

scholarly journals A synthesis dataset of permafrost thermal state for the Qinghai–Tibet (Xizang) Plateau, China

2021 ◽  
Vol 13 (8) ◽  
pp. 4207-4218 ◽  
Author(s):  
Lin Zhao ◽  
Defu Zou ◽  
Guojie Hu ◽  
Tonghua Wu ◽  
Erji Du ◽  
...  
Keyword(s):  
Soil Temperature ◽  
Land Surface ◽  
Climate Models ◽  
Thermal State ◽  
Monitoring Network ◽  
Climatic Conditions ◽  
Process Models ◽  
Tibet Plateau ◽  
Observation System ◽  
Observation Network

Abstract. Permafrost has great influences on the climatic, hydrological, and ecological systems on the Qinghai–Tibet Plateau (QTP). The changing permafrost and its impact have been attracting great attention worldwide like never before. More observational and modeling approaches are needed to promote an understanding of permafrost thermal state and climatic conditions on the QTP. However, limited data on the permafrost thermal state and climate background have been sporadically reported in different pieces of literature due to the difficulties of accessing and working in this region where the weather is severe, environmental conditions are harsh, and the topographic and morphological features are complex. From the 1990s, we began to establish a permafrost monitoring network on the QTP. Meteorological variables were measured by automatic meteorological systems. The soil temperature and moisture data were collected from an integrated observation system in the active layer. Deep ground temperature (GT) was observed from boreholes. In this study, a comprehensive dataset consisting of long-term meteorological, GT, soil moisture, and soil temperature data was compiled after quality control from an integrated, distributed, and multiscale observation network in the permafrost regions of QTP. The dataset is helpful for scientists with multiple study fields (i.e., climate, cryospheric, ecology and hydrology, meteorology science), which will significantly promote the verification, development, and improvement of hydrological models, land surface process models, and climate models on the QTP. The datasets are available from the National Tibetan Plateau/Third Pole Environment Data Center (https://data.tpdc.ac.cn/en/disallow/789e838e-16ac-4539-bb7e-906217305a1d/, last access: 24 August 2021, https://doi.org/10.11888/Geocry.tpdc.271107, Lin et al., 2021).

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