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

2018 ◽  
Author(s):  
Zun Yin ◽  
Catherine Ottlé ◽  
Philippe Ciais ◽  
Matthieu Guimberteau ◽  
Xuhui Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Yellow River ◽  
Precipitation Amount ◽  
Climatic Research Unit ◽  
Surface Model ◽  
Atmospheric Forcing ◽  
Soil Moisture Dynamics ◽  
Moisture Variation ◽  
Moisture Dynamics

Abstract. 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), are used to drive simulations in China by the land surface model ORCHIDEE-MICT. Simulated soil moisture is compared with in-situ and satellite datasets at different spatial and temporal scales in order to: 1) estimate the ability of ORCHIDEE-MICT (ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature) to represent soil moisture dynamics in China; 2) demonstrate the most suitable forcing dataset for further hydrological studies in Yangtze and Yellow river basins; 3) understand the discrepancies of simulated soil moisture among simulations. Results showed that ORCHIDEE-MICT can simulate reasonable soil moisture dynamics in China (median r = 0.53; RMSE = 0.06 m3 m−3), but the quality varies with forcing data. Simulated soil moisture driven by GSWP3 and WFDEI shows the best performance according to RMSE (RMSEGSWP3 = 0.05 m3 m−3) and correlation coefficient (rWFDEI = 0.64) respectively, suggesting that both GSWP3 and WFDEI are good choices for further hydrological studies. The mismatch between simulated and observed soil moisture is mainly explained by squared bias (SB) and lack of correlation weighted by the standard deviation (LCS). Large SB suggests that the parameterization in ORCHIDEE-MICT should be calibrated for further study in China. High LCS and underestimated soil moisture in the North China Plain demonstrate possible significant impacts of human activities like irrigation on soil moisture variation, which was not considered in our simulations. Finally, the discrepancies (D) of meteorological variables and simulated soil moisture among the four simulations are analyzed. The result shows that the D of soil moisture is mainly caused by the D in precipitation frequency and air humidity rather than precipitation amount.

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 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.

The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

2011 ◽  
Vol 42 (2-3) ◽  
pp. 95-112 ◽  
Author(s):  
Venkat Lakshmi ◽  
Seungbum Hong ◽  
Eric E. Small ◽  
Fei Chen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Area Index ◽  
Vegetation Fraction ◽  
Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

scholarly journals Combined measurement and modeling of the hydrological impact of hydraulic redistribution using CLM4.5 at eight AmeriFlux sites

2016 ◽  
Author(s):  
C. Fu ◽  
G. Wang ◽  
M. L. Goulden ◽  
R. L. Scott ◽  
K. Bible ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Hydraulic Redistribution ◽  
Energy Budgets ◽  
Soil Moisture Dynamics ◽  
Community Land Model ◽  
The Us ◽  
Hydrological Impact ◽  
The Impact ◽  
Moisture Dynamics

Abstract. Effects of hydraulic redistribution (HR) on hydrological, biogeochemical, and ecological processes have been demonstrated in the field, but the current generation of standard earth system models does not include a representation of HR. Though recent studies have examined the effect of incorporating HR into land surface models, few (if any) has tackled the magnitude of the HR flux itself or the soil moisture dynamics from which HR magnitude can be directly inferred. Here we incorporated Ryel et al.'s (2002) empirical equation describing HR into the NCAR Community Land Model Version 4.5 (CLM4.5), and examined the ability of the resulting hybrid model to capture the magnitude of HR flux and/or soil moisture dynamics from which HR can be directly inferred, to assess the impact of HR on surface water and energy budgets, and to explore how it may depend on climate regimes and vegetation conditions. Eight AmeriFlux sites characterized by contrasting climate regimes and multiple vegetation types were studied, including the US-Wrc Wind River Crane site in Washington State, the US-SRM Santa Rita Mesquite Savanna site in southern Arizona, and six sites along the Southern California Climate Gradient (US-SCs, g, f, w, c, and d). HR flux, evapotranspiration, and soil moisture were properly simulated in the present study, even in the face of various uncertainties. Our cross-ecosystem comparison showed that the timing, magnitude, and direction (upward or downward) of HR vary across ecosystems, and incorporation of HR into CLM4.5 improved the model-measurement match particularly during dry seasons. Our results also reveal that HR has important hydrological impact (on evapotranspiration, Bowen ratio, and soil moisture) in ecosystems that have a pronounced dry season but are not overall so dry that sparse vegetation and very low soil moisture limit HR.

scholarly journals Fully coupled high-resolution medium-range forecasts: evaluation of the hydrometeorological impact in an ensemble framework

2021 ◽  
Author(s):  
Luca Furnari ◽  
Linus Magnusson ◽  
Giuseppe Mendicino ◽  
Alfonso Senatore
Keyword(s):  
Boundary Layer ◽  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Ensemble Forecasts ◽  
Soil Moisture Dynamics ◽  
Medium Range ◽  
Fully Coupled ◽  
Moisture Dynamics ◽  
Range Forecasts

Fully coupled atmospheric-hydrological models allow a more realistic representation of the land surface–boundary layer continuum, representing both high-resolution land-surface/subsurface water lateral redistribution and the related feedback towards the atmosphere. This study evaluates the potential contribution of the fully coupled approach in extended-range mesoscale hydrometeorological ensemble forecasts. Previous studies have shown, for deterministic simulations, that the effect of fully coupling for short-range forecasts is minor compared to other sources of uncertainty, however, it becomes not negligible when increasing the forecast period. Through a proof-of-concept consisting of an ensemble (50 members from the ECMWF Ensemble Prediction System) seven-days-in-advance forecast of a high impact event affecting the Calabrian peninsula (southern Italy, Mediterranean basin) on November 2019, the paper elucidates the extent to which the improved representation of the terrestrial water lateral transport in the Weather Research and Forecasting (WRF) – Hydro modeling system affects the ensemble water balance, focusing on the precipitation and the hydrological response, in terms of both soil moisture dynamics and streamflow in 14 catchments spanning over 42% of the region. The fully coupled approach caused an increase of surface soil moisture and latent heat flux from land in the days preceding the event, partially affecting the lower Planetary Boundary Layer. However, when shoreward moisture transport from surrounding sea rapidly increased becoming the dominant process, only a weak signature of soil moisture contribution could be detected, resulting in only slightly higher precipitation forecast and not clear variation trend of peak flow, even though the latter variable increased up to 10% in some catchments. Overall, this study highlighted a remarkable performance of the medium-range ensemble forecasts, suggesting a profitable use of the fully coupled approach for forecasting purposes in circumstances in which soil moisture dynamics is more relevant and needs to be better addressed.

scholarly journals Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France)

2008 ◽  
Vol 5 (4) ◽  
pp. 1903-1926 ◽  
Author(s):  
T. Paris Anguela ◽  
M. Zribi ◽  
S. Hasenauer ◽  
F. Habets ◽  
C. Loumagne
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Model Verification ◽  
Atmospheric Conditions ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics ◽  
Rms Errors

Abstract. Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport and vegetation productivity. Its characterization, offers an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, soil moisture dynamics at soil surface (first centimeters) and root-zone (up to 1.5 m depth) are investigated at three spatial scales: local scale (field measurements), 8×8 km2 (hydrological model) and 25×25 km2 scale (ERS scatterometer) in a French watershed. This study points out the quality of surface and root-zone soil moisture data for SIM model and ERS scatterometer for a three year period. Surface soil moisture is highly variable because is more influenced by atmospheric conditions (rain, wind and solar radiation), and presents RMS errors up to 0.08 m3 m−3. On the other hand, root-zone moisture presents lower variability with small RMS errors (between 0.02 and 0.06 m3 m-3). These results will contribute to satellite and model verification of moisture, but also to better application of radar data for data assimilation in future.

Evaluation of multiple soil moisture products using in-situ observations over China

2021 ◽  
Author(s):  
Huiqing Li ◽  
Aizhong Ye ◽  
Yuhang Zhang ◽  
Wenwu Zhao
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Yellow River ◽  
Eastern China ◽  
Land Surface Model ◽  
Spatial And Temporal Variation ◽  
Surface Model ◽  
Drought Assessment ◽  
In Situ Observations

<p>Soil moisture (SM), a vital variable in the climate system, is applied in many fields. But the existing SM data sets from different sources have great uncertainty, hence need comprehensive verification. In this study, we collected and evaluated ten latest commonly used SM products over China, including four reanalysis data (ERA-Interim, ERA5, NCEP R2 and CFSR/CFSV2), three land surface model products (GLDAS 2.1 Noah, CLSM and VIC) and three remote sensing products (ESA CCI ACTIVE, COMBINED and PASSIVE). These products in their overlap period (2000-2018) were inter-compared in spatial and temporal variation. In addition, their accuracy was verified by a large quantity of in-situ observations. The results show that the ten SM products have roughly similar spatial patterns and small inter-annual differences, but there are still some deviations varying in regions and products. ERA5 displays the most encouraging overall performance in China. The estimates of SM in the northwest of China among all products generally perform poorly on capturing in-situ SM variability due to less coverage of observations. CLSM and ERA5 have a satisfactory correlation coefficient with the observed SM (R>0.7) in the northeast and south of China, respectively. ESA CCI ACTIVE performs with the optimal mean Equitable Threat Score (ETS) value, which indicates the promising ability to drought assessment, followed by CFSR/CFSV2 and ERA5. Specifically, ESA CCI ACTIVE expresses higher ETS in the Yellow River Basin, while CFSR/CFSV2 and ERA5 are more applicable in most areas of the eastern China. This study provides a reasonable reference for the application of SM products in China.</p>

scholarly journals Potential and limitations of multidecadal satellite soil moisture observations for selected climate model evaluation studies

2013 ◽  
Vol 17 (9) ◽  
pp. 3523-3542 ◽  
Author(s):  
A. Loew ◽  
T. Stacke ◽  
W. Dorigo ◽  
R. de Jeu ◽  
S. Hagemann
Keyword(s):  
Soil Moisture ◽  
Model Evaluation ◽  
Climate Model ◽  
Global Climate Models ◽  
Satellite Observations ◽  
Surface Model ◽  
Climate Variable ◽  
Soil Moisture Dynamics ◽  
Climate Model Evaluation ◽  
Moisture Dynamics

Abstract. Soil moisture is an essential climate variable (ECV) of major importance for land–atmosphere interactions and global hydrology. An appropriate representation of soil moisture dynamics in global climate models is therefore important. Recently, a first multidecadal, observation-based soil moisture dataset has become available that provides information on soil moisture dynamics from satellite observations (ECVSM, essential climate variable soil moisture). The present study investigates the potential and limitations of this new dataset for several applications in climate model evaluation. We compare soil moisture data from satellite observations, reanalysis and simulations from a state-of-the-art land surface model and analyze relationships between soil moisture and precipitation anomalies in the different dataset. Other potential applications like model parameter optimization or model initialization are not investigated in the present study. In a detailed regional study, we show that ECVSM is capable to capture well the interannual and intraannual soil moisture and precipitation dynamics in the Sahelian region. Current deficits of the new dataset are critically discussed and summarized at the end of the paper to provide guidance for an appropriate usage of the ECVSM dataset for climate studies.

scholarly journals Estimates of Global Surface Hydrology and Heat Fluxes from the Community Land Model (CLM4.5) with Four Atmospheric Forcing Datasets

2016 ◽  
Vol 17 (9) ◽  
pp. 2493-2510 ◽  
Author(s):  
Aihui Wang ◽  
Xubin Zeng ◽  
Donglin Guo
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Heat Fluxes ◽  
Surface Model ◽  
Atmospheric Forcing ◽  
Surface Hydrology ◽  
Community Land Model ◽  
Global Land ◽  
Ensemble Mean ◽  
The Impact

Abstract Global land surface hydrology and heat fluxes can be estimated by running a land surface model (LSM) driven by the atmospheric forcing dataset. Previous multimodel studies focused on the impact of different LSMs on model results. Here the sensitivity of the Community Land Model, version 4.5 (CLM4.5), results to the atmospheric forcing dataset is documented. Together with the model default global forcing dataset (CRU–NCEP, hereafter CRUNCEP), three newly developed, reanalysis-based, near-surface meteorological datasets (i.e., MERRA, CFSR, and ERA-Interim) with the precipitation adjusted by the Global Precipitation Climatology Project monthly product were used to drive CLM4.5. All four simulations were run at 0.5° × 0.5° grids from 1979 to 2009 with the identical initialization. The simulated monthly surface hydrology variables, fluxes, and the forcing datasets were then evaluated against various observation-based datasets (soil moisture, runoff, snow depth and water equivalent, and flux tower measurements). To partially avoid the mismatch between model gridbox values and point measurements, three approaches were taken. The model simulations based on three newly constructed forcing datasets are overall better than the simulation from CRUNCEP, in particular for soil moisture and snow quantities. The ensemble mean from the CLM4.5 simulations using the four forcing datasets is generally superior to individual simulations, and the ensemble mean latent and sensible heat fluxes over global land (60°S–90°N) are 42.8 and 40.3 W m−2, respectively. The differences in both precipitation and other atmospheric forcing variables (e.g., air temperature and downward solar radiation) contribute to the differences in simulated results. The datasets are available from the authors for further evaluation and for various applications.

scholarly journals On the use of AMSU-based products for the description of soil water content at basin scale

2011 ◽  
Vol 15 (9) ◽  
pp. 2839-2852 ◽  
Author(s):  
S. Manfreda ◽  
T. Lacava ◽  
B. Onorati ◽  
N. Pergola ◽  
M. Di Leo ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Skin Surface ◽  
Soil Moisture Dynamics ◽  
Basin Scale ◽  
Moisture Dynamics

Abstract. Characterizing the dynamics of soil moisture fields is a key issue in hydrology, offering a strategy to improve our understanding of complex climate-soil-vegetation interactions. Besides in-situ measurements and hydrological models, soil moisture dynamics can be inferred by analyzing data acquired by sensors on board of airborne and/or satellite platforms. In this work, we investigated the use of the National Oceanic and Atmospheric Administration – Advanced Microwave Sounding Unit-A (NOAA-AMSU-A) radiometer for the remote characterization of soil water content. To this aim, a field measurement campaign, lasted about three months (3 March 2010–18 May 2010), was carried out using a portable time-domain reflectometer (TDR) to get soil water content measures over five different locations within an experimental basin of 32.5 km2, located in the South of Italy. In detail, soil moisture measurements were carried out systematically at the times of satellite overpasses, over two square areas of 400 m2, a triangular area of 200 m2 and two transects of 60 and 170 m, respectively. Each monitored site is characterized by different land covers and soil textures, to account for spatial heterogeneity of land surface. Afterwards, a more extensive comparison (i.e. analyzing a 5 yr data time series) was made using soil moisture simulated by a hydrological model. Measured and modeled soil moisture data were compared with two AMSU-based indices: the Surface Wetness Index (SWI) and the Soil Wetness Variation Index (SWVI). Both time series of indices have been filtered by means of an exponential filter to account for the fact that microwave sensors only provide information at the skin surface. This allowed to understand the ability of each satellite-based index to account for soil moisture dynamics and to understand its performances under different conditions. As a general remark, the comparison shows a higher ability of the filtered SWI to describe the general trend of soil moisture, while the SWVI can capture soil moisture variations with a precision that increases at the higher values of SWVI.

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