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.

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>

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 Evaluation of ORCHIDEE-MICT-simulated soil moisture over China and impacts of different atmospheric forcing data

2018 ◽  
Vol 22 (10) ◽  
pp. 5463-5484 ◽  
Author(s):  
Zun Yin ◽  
Catherine Ottlé ◽  
Philippe Ciais ◽  
Matthieu Guimberteau ◽  
Xuhui Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Yellow River ◽  
Climatic Research Unit ◽  
Surface Model ◽  
Atmospheric Forcing ◽  
Meteorological Forcing ◽  
Soil Moisture Dynamics ◽  
Moisture Variation ◽  
Moisture Dynamics

Abstract. Soil moisture is a key variable of land surface hydrology, and its correct representation in land surface models is crucial for local to global climate predictions. The errors may come from the model itself (structure and parameterization) but also from the meteorological forcing used. In order to separate the two source of errors, four atmospheric forcing datasets, GSWP3 (Global Soil Wetness Project Phase 3), PGF (Princeton Global meteorological Forcing), CRU-NCEP (Climatic Research Unit-National Center for Environmental Prediction), and WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data), were used to drive simulations in China by the land surface model ORCHIDEE-MICT(ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature). Simulated soil moisture was compared with in situ and satellite datasets at different spatial and temporal scales in order to (1) estimate the ability of ORCHIDEE-MICT to represent soil moisture dynamics in China; (2) demonstrate the most suitable forcing dataset for further hydrological studies in Yangtze and Yellow River basins; and (3) understand the discrepancies of simulated soil moisture among simulations. Results showed that ORCHIDEE-MICT can simulate reasonable soil moisture dynamics in China, but the quality varies with forcing data. Simulated soil moisture driven by GSWP3 and WFDEI shows the best performance according to the root mean square error (RMSE) and correlation coefficient, respectively, suggesting that both GSWP3 and WFDEI are good choices for further hydrological studies in the two catchments. The mismatch between simulated and observed soil moisture is mainly explained by the bias of magnitude, suggesting that the parameterization in ORCHIDEE-MICT should be revised for further simulations in China. Underestimated soil moisture in the North China Plain demonstrates possible significant impacts of human activities like irrigation on soil moisture variation, which was not considered in our simulations. Finally, the discrepancies of meteorological variables and simulated soil moisture among the four simulations are analyzed. The result shows that the discrepancy of soil moisture is mainly explained by differences in precipitation frequency and air humidity rather than differences in precipitation amount.

scholarly journals Contrasting roles of interception and transpiration in the hydrological cycle – Part 1: Temporal characteristics over land

2014 ◽  
Vol 5 (2) ◽  
pp. 441-469 ◽  
Author(s):  
L. Wang-Erlandsson ◽  
R. J. van der Ent ◽  
L. J. Gordon ◽  
H. H. G. Savenije
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Open Water ◽  
Water Evaporation ◽  
Surface Model ◽  
Rain Event ◽  
Temporal Characteristics ◽  
Moisture Recycling

Abstract. Moisture recycling, the contribution of terrestrial evaporation to precipitation, has important implications for both water and land management. Although terrestrial evaporation consists of different fluxes (i.e. transpiration, vegetation interception, floor interception, soil moisture evaporation, and open-water evaporation), moisture recycling (terrestrial evaporation–precipitation feedback) studies have up to now only analysed their combined total. This paper constitutes the first of two companion papers that investigate the characteristics and roles of different evaporation fluxes for land–atmosphere interactions. Here, we investigate the temporal characteristics of partitioned evaporation on land and present STEAM (Simple Terrestrial Evaporation to Atmosphere Model) – a hydrological land-surface model developed to provide inputs to moisture tracking. STEAM estimates a mean global terrestrial evaporation of 73 900 km3 year-1, of which 59% is transpiration. Despite a relatively simple model structure, validation shows that STEAM produces realistic evaporative partitioning and hydrological fluxes that compare well with other global estimates over different locations, seasons, and land-use types. Using STEAM output, we show that the terrestrial residence timescale of transpiration (days to months) has larger inter-seasonal variation and is substantially longer than that of interception (hours). Most transpiration occurs several hours or days after a rain event, whereas interception is immediate. In agreement with previous research, our simulations suggest that the vegetation's ability to transpire by retaining and accessing soil moisture at greater depth is critical for sustained evaporation during the dry season. We conclude that the differences in temporal characteristics between evaporation fluxes are substantial and reasonably can cause differences in moisture recycling, which is investigated more in the companion paper (van der Ent et al., 2014, hereafter Part 2).

The Contributions of Gauge-Based Precipitation and SMAP Brightness Temperature Observations to the Skill of the SMAP Level-4 Soil Moisture Product

2020 ◽  
Author(s):  
Rolf H. Reichle ◽  
Qing Liu ◽  
Joseph V. Ardizzone ◽  
Wade T. Crow ◽  
Gabrielle J. M. De Lannoy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Root Zone ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Forcing ◽  
Earth Observing System ◽  
Error Surface

AbstractSoil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 Soil Moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with ¼-degree, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, ½-degree, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the Instrumental Variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10-0.11 compared to an increase of 0.02-0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous U.S. reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.

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.

scholarly journals A global analysis of satellite derived and DGVM surface soil moisture products

2011 ◽  
Vol 8 (2) ◽  
pp. 4281-4312
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 ◽  
Single Layer ◽  
Global Scale ◽  
Rain Event ◽  
Deep Soil ◽  
Surface Climate ◽  
Soil Moisture Availability

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) to evaluate the results of the process-based vegetation model ORCHIDEE during the period 2003–2004. We find that the soil moisture products of AMSR-E and ORCHIDEE correlate well, in particular when considering the root zone soil moisture of ORCHIDEE. However, the root zone soil moisture in ORCHIDEE consistently overestimated the temporal autocorrelation relative to AMSR-E and in situ measurements. This may be due to the different vertical depth of the two products, to the uncertainty in precipitation forcing in ORCHIDEE, and to the fact that the structure of ORCHIDEE consisting of a single-layer deep soil, 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 in ORCHIDEE using AMSR-E with the current hydrological model may significantly improve the soil moisture dynamics in ORCHIDEE.

Increasing the depth of a Land Surface Model. Part II: Temperature sensitivity to improved subsurface thermodynamics and associated permafrost response

2021 ◽  
Author(s):  
N. J. Steinert ◽  
J. F. González-Rouco ◽  
P. de Vrese ◽  
E. García-Bustamante ◽  
S. Hagemann ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Land Surface Model ◽  
Phase Changes ◽  
Surface Model ◽  
Climate Projections ◽  
Stable States ◽  
Soil Parameter ◽  
The Impact

AbstractThe impact of various modifications of the JSBACH Land Surface Model to represent soil temperature and cold-region hydro-thermodynamic processes in climate projections of the 21st century is examined. We explore the sensitivity of JSBACH to changes in the soil thermodynamics, energy balance and storage, and the effect of including freezing and thawing processes. The changes involve 1) the net effect of an improved soil physical representation and 2) the sensitivity of our results to changed soil parameter values and their contribution to the simulation of soil temperatures and soil moisture, both aspects being presented in the frame of an increased bottom boundary depth from 9.83 m to 1418.84 m. The implementation of water phase changes and supercooled water in the ground creates a coupling between the soil thermal and hydrological regimes through latent heat exchange. Momentous effects on subsurface temperature of up to ±3 K, together with soil drying in the high northern latitudes, can be found at regional scales when applying improved hydro-thermodynamic soil physics. The sensitivity of the model to different soil parameter datasets occurs to be low but shows important implications for the root zone soil moisture content. The evolution of permafrost under pre-industrial forcing conditions emerges in simulated trajectories of stable states that differ by 4 – 6 • 106 km2 and shows large differences in the spatial extent of 105 –106 km2 by 2100, depending on the model configuration.

scholarly journals Analysis of the linearised observation operator in a soil moisture and temperature analysis scheme

2015 ◽  
Vol 2 (1) ◽  
pp. 505-535 ◽  
Author(s):  
I. Dharssi ◽  
B. Candy ◽  
P. Steinle
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Soil Layers ◽  
Model Soil ◽  
Screen Level

Abstract. Several weather forecasting agencies have developed advanced land data assimilation systems that, in principle, can analyse any model land variable. Such systems can make use of a wide variety of observation types, such as screen level (2 m above the surface) observations and satellite based measurements of surface soil moisture and skin temperature. Indirect measurements can be used and information propagated from the surface into the deeper soil layers. A key component of the system is the calculation of the linearised observation operator matrix (Jacobian matrix) which describes the link between the observations and the land surface model variables. The elements of the Jacobian matrix (Jacobians) are estimated using finite difference by performing short model forecasts with perturbed initial conditions. The calculated Jacobians show that there can be strong coupling between the screen level and the soil. The coupling between the screen level and surface soil moisture is found to be due to a number of processes including bare soil evaporation, soil thermal conductivity as well as transpiration by plants. Therefore, there is significant coupling both during the day and at night. The coupling between the screen level and root-zone soil moisture is primarily through transpiration by plants. Therefore the coupling is only significant during the day and the vertical variation of the coupling is modulated by the vegetation root depths. The calculated Jacobians that link screen level temperature to model soil temperature are found to be largest for the topmost model soil layer and become very small for the lower soil layers. These values are largest during the night and generally positive in value. It is found that the Jacobians that link observations of surface soil moisture to model soil moisture are strongly affected by the soil hydraulic conductivity. Generally, for the Joint UK Land Environment Simulator (JULES) land surface model, the coupling between the surface and root zone soil moisture is weak. Finally, the Jacobians linking observations of skin temperature to model soil temperature and moisture are calculated. These Jacobians are found to have a similar spatial pattern to the Jacobians for observations of screen level temperature. Analysis is also performed of the sensitivity of the calculated Jacobians to the magnitude of the perturbations used.

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