Evaluation of Reanalysis Soil Moisture Simulations Using Updated Chinese Soil Moisture Observations

2005 ◽  
Vol 6 (2) ◽  
pp. 180-193 ◽  
Author(s):  
Haibin Li ◽  
Alan Robock ◽  
Suxia Liu ◽  
Xingguo Mo ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Land Surface ◽  
Soil Layer ◽  
Land Surface Model ◽  
Cold Season ◽  
Temporal Scale ◽  
Department Of Energy ◽  
Surface Model ◽  
Deep Soil

Abstract Using 19 yr of Chinese soil moisture data from 1981 to 1999, the authors evaluate soil moisture in three reanalysis outputs: the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis 1 (R-1); and the NCEP–Department of Energy (DOE) reanalysis 2 (R-2) over China. R-2 shows improved interannual variability and better seasonal patterns of soil moisture than R-1 as the result of the incorporation of observed precipitation, but not for all stations. ERA-40 produces a better mean value of soil moisture for most Chinese stations and good interannual variability. Limited observations in the spring indicate a spring soil moisture peak for most of the stations. ERA-40 generally reproduced this event, while R-1 or R-2 generally did not capture this feature, either because the soil was already saturated or the deep soil layer was too thick and damped such a response. ERA-40 and R-1 have a temporal time scale comparable to observations, but R-2 has a memory of nearly 5 months for the growing season, about twice the temporal scale of the observations. The cold season tends to prolong soil moisture memory by about 3 months for R-2 and 1 month for ERA-40. The unrealistic long temporal scale of R-2 can be attributed to the deep layer of the land surface model, which is too thick and dominates the soil moisture variability. R-1 has the same land surface scheme as R-2, but shows a temporal scale close to observations, which is actually because of soil moisture nudging to a fixed climatology. This new long time series of observed soil moisture will prove valuable for other studies of climate change, remote sensing, and model evaluation.

Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S

2021 ◽  
Author(s):  
Adam Pasik ◽  
Wolfgang Preimesberger ◽  
Bernhard Bauer-Marschallinger ◽  
Wouter Dorigo
Keyword(s):  
Soil Moisture ◽  
Soil Profile ◽  
Land Surface ◽  
Root Zone ◽  
Soil Layer ◽  
Land Surface Model ◽  
In Situ Measurements ◽  
Surface Model ◽  
T Values

<p>Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.</p><p>Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters.<br>Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.</p>

Incorporating Hydraulic Lift into a Land Surface Model and Its Effects on Surface Soil Moisture Prediction

2004 ◽  
Vol 5 (6) ◽  
pp. 1181-1191 ◽  
Author(s):  
Diandong Ren ◽  
Ming Xue ◽  
Ann Henderson-Sellers
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Energy Exchange ◽  
Atmospheric Surface Layer ◽  
Soil Layer ◽  
Land Surface Model ◽  
Hydraulic Lift ◽  
Surface Model ◽  
Daily Cycle ◽  
Dry Conditions

Abstract In comparison with the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) measurements, the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS), a multilayer soil hydrological model, simulates a much faster drying of the superficial soil layer (5 cm) for a densely vegetated area at the OASIS site in Norman, Oklahoma, under dry conditions. Further, the measured superficial soil moisture contents also show a counterintuitive daily cycle that moistens the soil during daytime and dries the soil at night. The original SHEELS model fails to simulate this behavior. This work proposes a treatment of hydraulic lift processes associated with stressed vegetation and shows via numerical experiments that both problems reported above can be much alleviated by including the hydraulic lift effect associated with stressed vegetation.

An Indirect Data Assimilation Scheme for Deep Soil Temperature in the Pleim–Xiu Land Surface Model

2009 ◽  
Vol 48 (7) ◽  
pp. 1362-1376 ◽  
Author(s):  
Jonathan E. Pleim ◽  
Robert Gilliam
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Deep Soil ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract The Pleim–Xiu land surface model (PX LSM) has been improved by the addition of a second indirect data assimilation scheme. The first, which was described previously, is a technique in which soil moisture is nudged according to the biases in 2-m air temperature and relative humidity between the model- and observation-based analyses. The new technique involves nudging the deep soil temperature in the soil temperature force–restore (FR) model according to model bias in 2-m air temperature only during nighttime. While the FR technique is computationally efficient and very accurate for the special conditions for which it was derived, it is very dependent on the deep soil temperature that drives the restoration term of the surface soil temperature equation. Thus, adjustment of the deep soil temperature to optimize the 2-m air temperature during the night, when surface forcing is minimal, provides significant advantages over other methods of deep soil moisture initialization. Simulations of the Weather Research and Forecasting Model (WRF) using the PX LSM with and without the new deep soil temperature nudging scheme demonstrate substantial benefits of the new scheme for reducing error and bias of the 2-m air temperature. The effects of the new nudging scheme are most pronounced in the winter (January 2006) during which the model’s cold bias is greatly reduced. Air temperature error and bias are also reduced in a summer simulation (August 2006) with the greatest benefits in less vegetated and more arid regions. Thus, the deep temperature nudging scheme complements the soil moisture nudging scheme because it is most effective for conditions in which the soil moisture scheme is least effective, that is, when evapotranspiration is not important (winter and arid climates).

scholarly journals A global analysis of soil moisture derived from satellite observations and a land surface model

2012 ◽  
Vol 16 (3) ◽  
pp. 833-847 ◽  
Author(s):  
K. T. Rebel ◽  
R. A. M. de Jeu ◽  
P. Ciais ◽  
N. Viovy ◽  
S. L. Piao ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Global Analysis ◽  
Soil Layer ◽  
Land Surface Model ◽  
Surface Model ◽  
Rain Event ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics

Abstract. Soil moisture availability is important in regulating photosynthesis and controlling land surface-climate feedbacks at both the local and global scale. Recently, global remote-sensing datasets for soil moisture have become available. In this paper we assess the possibility of using remotely sensed soil moisture – AMSR-E (LPRM) – to similate soil moisture dynamics of the process-based vegetation model ORCHIDEE by evaluating the correspondence between these two products using both correlation and autocorrelation analyses. We find that the soil moisture product of AMSR-E (LPRM) and the simulated soil moisture in ORCHIDEE correlate well in space and time, in particular when considering the root zone soil moisture of ORCHIDEE. However, the root zone soil moisture in ORCHIDEE has on average a higher temporal autocorrelation relative to AMSR-E (LPRM) and in situ measurements. This may be due to the different vertical depth of the two products – AMSR-E (LPRM) at the 2–5 cm surface depth and ORCHIDEE at the root zone (max. 2 m) depth – to uncertainty in precipitation forcing in ORCHIDEE, and to the fact that the structure of ORCHIDEE consists of a single-layer deep soil, which does not allow simulation of the proper cascade of time scales that characterize soil drying after each rain event. We conclude that assimilating soil moisture, using AMSR-E (LPRM) in a land surface model like ORCHIDEE with an improved hydrological model of more than one soil layer, may significantly improve the soil moisture dynamics, which could lead to improved CO2 and energy flux predictions.

scholarly journals The incorporation of an organic soil layer in the Noah-MP land surface model and its evaluation over a boreal aspen forest

2016 ◽  
Vol 16 (13) ◽  
pp. 8375-8387 ◽  
Author(s):  
Liang Chen ◽  
Yanping Li ◽  
Fei Chen ◽  
Alan Barr ◽  
Michael Barlage ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Latent Heat ◽  
Land Surface ◽  
Soil Layer ◽  
Land Surface Model ◽  
Organic Soil ◽  
Heat Fluxes ◽  
Surface Model ◽  
The Impact

Abstract. A thick top layer of organic matter is a dominant feature in boreal forests and can impact land–atmosphere interactions. In this study, the multi-parameterization version of the Noah land surface model (Noah-MP) was used to investigate the impact of incorporating a forest-floor organic soil layer on the simulated surface energy and water cycle components at the BERMS Old Aspen site (OAS) field station in central Saskatchewan, Canada. Compared to a simulation without an organic soil parameterization (CTL), the Noah-MP simulation with an organic soil (OGN) improved Noah-MP-simulated soil temperature profiles and soil moisture at 40–100 cm, especially the phase and amplitude (Seasonal cycle) of soil temperature below 10 cm. OGN also enhanced the simulation of sensible and latent heat fluxes in spring, especially in wet years, which is mostly related to the timing of spring soil thaw and warming. Simulated top-layer soil moisture is better in OGN than that in CTL. The effects of including an organic soil layer on soil temperature are not uniform throughout the soil depth and are more prominent in summer. For drought years, the OGN simulation substantially modified the partitioning of water between direct soil evaporation and vegetation transpiration. For wet years, the OGN-simulated latent heat fluxes are similar to CTL except for the spring season when OGN produced less evaporation, which was closer to observations. Including organic soil produced more subsurface runoff and resulted in much higher runoff throughout the freezing periods in wet years.

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.

scholarly journals Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model

2016 ◽  
Vol 20 (12) ◽  
pp. 4895-4911 ◽  
Author(s):  
Gabriëlle J. M. De Lannoy ◽  
Rolf H. Reichle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Incidence Angle ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Errors ◽  
Surface Soil Moisture ◽  
Brightness Temperatures

Abstract. Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40° incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

scholarly journals Improved representations of coupled soil-canopy processes in the CABLE land surface model

2016 ◽  
Author(s):  
Vanessa Haverd ◽  
Matthias Cuntz ◽  
Lars P. Nieradzik ◽  
Ian N. Harman
Keyword(s):  
Water Use ◽  
Land Surface ◽  
Land Surface Model ◽  
Heat Fluxes ◽  
Soil Conditions ◽  
Surface Model ◽  
Drought Response ◽  
Deep Soil ◽  
Stable Conditions ◽  
Water Use Efficiencies

Abstract. CABLE is a global land surface model, which has been used extensively in offline and coupled simulations. While CABLE performs well in comparison with other land surface models, results are impacted by decoupling of transpiration and photosynthesis fluxes under drying soil conditions, often leading to implausibly high water use efficiencies. Here we present a solution to this problem, ensuring that modeled transpiration is always consistent with modeled photosynthesis, while introducing a parsimonious single-parameter drought response function which is coupled to root water uptake. We further improve CABLE’s simulation of coupled soil-canopy processes by introducing an alternative hydrology model with a physically accurate representation of coupled energy and water fluxes at the soil/air interface, including a more realistic formulation of transfer under atmospherically stable conditions within the canopy and in the presence of leaf litter. The effects of these model developments are assessed using data from 18 stations from the global Eddy covariance flux network FLUXNET, selected to span a large climatic range. Marked improvements are demonstrated, with root-mean-squared errors for monthly latent heat fluxes and water use efficiencies being reduced by 40 %. Results highlight the important roles of deep soil moisture in mediating drought response and litter in dampening soil evaporation.

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