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

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.

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

10.5194/hess-2017-699 ◽

2018 ◽

Cited By ~ 1

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.

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

Hydrology and Earth System Sciences ◽

10.5194/hess-16-833-2012 ◽

2012 ◽

Vol 16 (3) ◽

pp. 833-847 ◽

Cited By ~ 52

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.

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

Hydrology Research ◽

10.2166/nh.2011.071 ◽

2011 ◽

Vol 42 (2-3) ◽

pp. 95-112 ◽

Cited By ~ 31

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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0217.1 ◽

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.

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

10.5194/hess-2016-24 ◽

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.

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

10.22541/au.161276443.31431062/v1 ◽

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.

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

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-5-1903-2008 ◽

2008 ◽

Vol 5 (4) ◽

pp. 1903-1926 ◽

Cited By ~ 12

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.

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

10.5194/egusphere-egu21-14049 ◽

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>

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

Hydrology and Earth System Sciences ◽

10.5194/hess-17-3523-2013 ◽

2013 ◽

Vol 17 (9) ◽

pp. 3523-3542 ◽

Cited By ~ 77

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.

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Confronting Soil Moisture Dynamics from the ORCHIDEE Land Surface Model With the ESA-CCI Product: Perspectives for Data Assimilation

Remote Sensing ◽

10.3390/rs10111786 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1786 ◽

Cited By ~ 3

Author(s):

Nina Raoult ◽

Bertrand Delorme ◽

Catherine Ottlé ◽

Philippe Peylin ◽

Vladislav Bastrikov ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Climate Modelling ◽

Potential Candidate ◽

Meteorological Forcing ◽

Model Parameterization ◽

Atmospheric Forcings

Soil moisture plays a key role in water, carbon and energy exchanges between the land surface and the atmosphere. Therefore, a better representation of this variable in the Land-Surface Models (LSMs) used in climate modelling could significantly reduce the uncertainties associated with future climate predictions. In this study, the ESA-CCI soil moisture (SM) combined product (v4.2) has been confronted to the simulated top-first layers/cms of the ORCHIDEE LSM (the continental part of the IPSL Earth System Model) for the years 2008-2016, to evaluate its potential to improve the model using data assimilation techniques. The ESA-CCI data are first rescaled to match the climatology of the model and the signal representative depth is selected. Results are found to be relatively consistent over the first 20 cm of the model. Strong correlations found between the model and the ESA-CCI product show that ORCHIDEE can adequately reproduce the observed SM dynamics. As well as considering two different atmospheric forcings to drive the model, we consider two different model parameterizations related to the soil resistance to evaporation. The correlation metric is shown to be more sensitive to the choice of meteorological forcing than to the choice of model parameterization. Therefore, the metric is not optimal in highlighting structural deficiencies in the model. In contrast, the temporal autocorrelation metric is shown to be more sensitive to this model parameterization, making the metric a potential candidate for future data assimilation experiments.

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