scholarly journals Reducing soil moisture measurement scale mismatch to improve surface energy flux estimation

2016 ◽  
Author(s):  
Joost Iwema ◽  
Rafael Rosolem ◽  
Mostaquimur Rahman ◽  
Eleanor Blyth ◽  
Thorsten Wagener
Keyword(s):  
Soil Moisture ◽  
Surface Energy ◽  
Energy Flux ◽  
Spatial Scales ◽  
Cosmic Ray ◽  
Energy Partitioning ◽  
Surface Model ◽  
Surface Energy Flux ◽  
Soil Moisture Data ◽  
Scale Mismatch

Abstract. At the so-called hyper-resolution scale (i.e. grid cells of 1 km2) Land Surface Model (LSM) parameters are sometimes calibrated with Eddy-Covariance (EC) data and Point Scale (PS) soil moisture data. However, measurement scales of EC and PS data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux data and soil moisture data by replacing PS soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNS) made at larger spatial scales. Five soil-evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against PS and CRNS soil moisture data separately. We calibrated the model for twelve sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in surface energy partitioning for the two calibration solutions was assessed by comparing to EC data and to a version of JULES runs with default parameter values. We found that simulated surface energy partitioning did not differ substantially between the PS and CRNS calibrations, despite their differences in actual soil moisture observations. We concluded that potential differences due to distinct spatial scales represented by the PS and CRNS soil moisture sensor techniques were substantially undermined by the weak coupling between soil moisture and evapotranspiration within JULES.

scholarly journals Land surface model performance using cosmic-ray and point-scale soil moisture measurements for calibration

2017 ◽  
Vol 21 (6) ◽  
pp. 2843-2861 ◽  
Author(s):  
Joost Iwema ◽  
Rafael Rosolem ◽  
Mostaquimur Rahman ◽  
Eleanor Blyth ◽  
Thorsten Wagener
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Surface Energy ◽  
Eddy Covariance ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
Point Scale ◽  
Soil Moisture Data

Abstract. At very high resolution scale (i.e. grid cells of 1 km2), land surface model parameters can be calibrated with eddy-covariance flux data and point-scale soil moisture data. However, measurement scales of eddy-covariance and point-scale data differ substantially. In our study, we investigated the impact of reducing the scale mismatch between surface energy flux and soil moisture observations by replacing point-scale soil moisture data with observations derived from Cosmic-Ray Neutron Sensors (CRNSs) made at larger spatial scales. Five soil and evapotranspiration parameters of the Joint UK Land Environment Simulator (JULES) were calibrated against point-scale and Cosmic-Ray Neutron Sensor soil moisture data separately. We calibrated the model for 12 sites in the USA representing a range of climatic, soil, and vegetation conditions. The improvement in latent heat flux estimation for the two calibration solutions was assessed by comparison to eddy-covariance flux data and to JULES simulations with default parameter values. Calibrations against the two soil moisture products alone did show an advantage for the cosmic-ray technique. However, further analyses of two-objective calibrations with soil moisture and latent heat flux showed no substantial differences between both calibration strategies. This was mainly caused by the limited effect of calibrating soil parameters on soil moisture dynamics and surface energy fluxes. Other factors that played a role were limited spatial variability in surface fluxes implied by soil moisture spatio-temporal stability, and data quality issues.

scholarly journals Modeled Land Atmosphere Coupling Response to Soil Moisture Changes with Different Generations of Land Surface Models

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 46 ◽  
Author(s):  
Prabhakar Shrestha ◽  
Clemens Simmer
Keyword(s):  
Soil Moisture ◽  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Small Scale ◽  
Land Surface Models ◽  
Surface Energy Flux ◽  
Community Land Model ◽  
Flux Partitioning ◽  
Surface Models

An idealized study with two land surface models (LSMs): TERRA-Multi Layer (TERRA-ML) and Community Land Model (CLM) alternatively coupled to the same atmospheric model COSMO (Consortium for Small-Scale Modeling), reveals differences in the response of the LSMs to initial soil moisture. The bulk parameterization of evapotranspiration pathways, which depends on the integrated soil moisture of active layers rather than on each discrete layer, results in a weaker response of the surface energy flux partitioning to changes in soil moisture for TERRA-ML, as compared to CLM. The difference in the resulting surface energy flux partitioning also significantly affects the model response in terms of the state of the atmospheric boundary layer. For vegetated land surfaces, both models behave quite differently for drier regimes. However, deeper reaching root fractions in CLM align both model responses with each other. In general, differences in the parameterization of the available root zone soil moisture, evapotranspiration pathways, and the soil-vegetation structure in the two LSMs are mainly responsible for the diverging tendencies of the simulated land atmosphere coupling responses.

Different energetics of global monsoon over land and ocean

2021 ◽  
Author(s):  
Arindam Chakraborty ◽  
Chetankumar Jalihal ◽  
Jayaraman Srinivasan
Keyword(s):  
Surface Energy ◽  
Energy Flux ◽  
Global Climate ◽  
Lower Troposphere ◽  
Summer Precipitation ◽  
Reanalysis Data ◽  
Vertical Transport ◽  
Net Energy ◽  
Global Monsoon ◽  
Surface Energy Flux

<p>Monsoons were traditionally considered to be land-based systems. Recent definitions of monsoons based on either the seasonal reversal of winds or the local summer precipitation accounting for more than 50% of the annual precipitation suggests that monsoon domains extend over oceanic regions as well. The concept of global monsoon combines all the monsoon domains into a single entity. Modern observations show that the variations in precipitation are nearly coherent across all the individual monsoon domains on decadal timescales. Using a transient simulation of the global climate over the last 22,000 years as well as reanalysis data of the modern climate, we have shown that tropical precipitation has different characteristics over land and ocean grids. This is due to the differences in the energetics of monsoon over land and ocean grids. With a lower thermal heat capacity, the net surface energy flux over land is negligible, whereas it is quite large over the ocean. In fact, the orbital scale variability of net energy flux into the atmosphere over the ocean is controlled by the surface energy flux. Another major difference between land and ocean grids of the global monsoon is in the vertical profile of the vertical pressure velocity. It is bottom-heavy over land and top-heavy over the ocean. This results in smaller vertical transport of moist static energy (which has a minimum in the lower troposphere) over land, and a larger vertical transport over the ocean. These differences between the land and ocean, suggest that the land and ocean grids should not be combined as is traditionally done. Global monsoon-land and global monsoon-ocean should be studied separately.</p>

scholarly journals Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1362 ◽  
Author(s):  
Mustafa Berk Duygu ◽  
Zuhal Akyürek
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Land Surface Models ◽  
Statistical Measures ◽  
Surface Models ◽  
Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

QA4SM – An online tool for satellite soil moisture data validation

2021 ◽  
Author(s):  
Samuel Scherrer ◽  
Wolfgang Preimesberger ◽  
Monika Tercjak ◽  
Zoltan Bakcsa ◽  
Alexander Boresch ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Global Climate ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Space Applications ◽  
Online Tool ◽  
Soil Moisture Data

<p>To validate satellite soil moisture products and compare their quality with other products, standardized, fully traceable validation methods are required. The QA4SM (Quality Assurance for Soil Moisture; ) free online validation tool provides an easy-to-use implementation of community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellites. It sets the basis for a community wide standard for validation studies.</p><p>QA4SM can be used to preprocess, intercompare, store, and visualise validation results. It uses state-of-the-art open-access soil moisture data records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Change Services (C3S) soil moisture datasets, as well as single-sensor products, e.g. H-SAF Metop-A/B ASCAT surface soil moisture, SMOS-IC, and SMAP L3 soil moisture. Non-satellite data include in-situ data from the International Soil Moisture Network (ISMN: ), as well as land surface model or reanalysis products, e.g. ERA5 soil moisture.</p><p>Users can interactively choose temporal or spatial subsets of the data and apply filters on quality flags. Additionally, validation of anomalies and application of different scaling methods are possible. The tool provides traditional validation metrics for dataset pairs (e.g. correlation, RMSD) as well as triple collocation metrics for dataset triples. All results can be visualised on the webpage, downloaded as figures, or downloaded in NetCDF format for further use. Archiving and publishing features allow users to easily store and share validation results. Published validation results can be cited in reports and publications via DOIs.</p><p>The new version of the service provides support for high-resolution soil moisture products (from Sentinel-1), additional datasets, and improved usability.</p><p>We present an overview and examples of the online tool, new features, and give an outlook on future developments.</p><p><em>Acknowledgements: This work was supported by the QA4SM & QA4SM-HR projects, funded by the Austrian Space Applications Programme (FFG).</em></p>

scholarly journals Satellite‐Based Assessment of Land Surface Energy Partitioning–Soil Moisture Relationships and Effects of Confounding Variables

2019 ◽  
Vol 55 (12) ◽  
pp. 10657-10677 ◽  
Author(s):  
Andrew F. Feldman ◽  
Daniel J. Short Gianotti ◽  
Isabel F. Trigo ◽  
Guido D. Salvucci ◽  
Dara Entekhabi
Keyword(s):  
Soil Moisture ◽  
Surface Energy ◽  
Land Surface ◽  
Energy Partitioning ◽  
Confounding Variables
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