scholarly journals Connection Between Root Zone Soil Moisture and Surface Energy Flux Partitioning Using Modeling, Observations, and Data Assimilation for a Temperate Grassland Site in Germany

2018 ◽  
Vol 123 (9) ◽  
pp. 2839-2862 ◽  
Author(s):  
P. Shrestha ◽  
W. Kurtz ◽  
G. Vogel ◽  
J.-P. Schulz ◽  
M. Sulis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Flux ◽  
Root Zone ◽  
Temperate Grassland ◽  
Surface Energy Flux ◽  
Flux Partitioning ◽  
Grassland Site

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.

scholarly journals A dual-pass data assimilation scheme for estimating surface energy fluxes with FY3A-VIRR land surface temperature

2013 ◽  
Vol 10 (3) ◽  
pp. 3927-3972
Author(s):  
T. R. Xu ◽  
S. M. Liu ◽  
Z. W. Xu ◽  
S. Liang ◽  
L. Xu
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Surface Energy Flux ◽  
Data Assimilation Scheme ◽  
Dual Pass ◽  
Assimilation Scheme

Abstract. A dual-pass data assimilation scheme is developed to improve predictions of surface energy fluxes. Pass 1 of the dual-pass data assimilation scheme optimizes model vegetation parameters at the weekly temporal scale and pass 2 optimizes soil moisture at the daily temporal scale. Based on the ensemble Kalman filter (EnKF), land surface temperature (LST) data derived from the new generation of Chinese meteorology satellite (FY3A-VIRR) is assimilated into common land model (CoLM) for the first time. Four sites are selected for the data assimilation experiments, namely Arou, BJ, Guantao, and Miyun that include alpine meadow, grass, crop, and orchard land cover types. The results are compared with data set generated by a multi-scale surface energy flux observation system that includes an automatic weather station (AWS), an eddy covariance (EC) and a large aperture scintillometer (LAS). Results indicate that the CoLM can simulate the diurnal variations of surface energy flux, but usually overestimates sensible heat flux and underestimates latent heat flux and evaporation fraction (EF). With FY3A-VIRR LST data, the dual-pass data assimilation scheme can reduce model uncertainties and improve predictions of surface energy flux. Compared with EC measurements, the average model biases (BIAS) values change from 37.8 to 7.7 W m−2 and from −27.6 to 18.8 W m−2; the root mean square error (RMSE) values drop from 74.7 to 39.1 W m−2 and from 95.1 to 62.7 W m−2 for sensible and latent heat fluxes respectively. For evaporation fraction (EF), the average BIAS values change from −0.29 to 0.0 and the average RMSE values drop from 0.38 to 0.12. To compare the results with LAS-measured sensible heat flux, the source areas are calculated using a footprint model and overlaid with FY3A pixels. The four sites averaged BIAS values drop from 63.7 to −8.5 W m−2 and RMSE values drop from 118.2 to 69.8 W m−2. Ultimately, the error sources in surface energy flux predictions are investigated, and the results show that both soil moisture and vegetation parameters caused the big model biases in surface energy flux predictions. With Pass 1 and Pass 2, the dual-pass data assimilation scheme can cut down the surface energy flux prediction biases (BIAS) to nearly zero.

scholarly journals Deforestation reshapes land-surface energy-flux partitioning

2021 ◽  
Author(s):  
Kunxiaojia Yuan ◽  
Qing Zhu ◽  
Shiyu Zheng ◽  
Lei Zhao ◽  
Min Chen ◽  
...  
Keyword(s):  
Surface Energy ◽  
Energy Flux ◽  
Land Surface ◽  
Surface Energy Flux ◽  
Flux Partitioning

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.

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 The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

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