scholarly journals The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

2017 ◽  
Vol 21 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
David Fairbairn ◽  
Alina Lavinia Barbu ◽  
Adrien Napoly ◽  
Clément Albergel ◽  
Jean-François Mahfouf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Land Surface ◽  
River Discharge ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture

2006 ◽  
Vol 7 (3) ◽  
pp. 421-432 ◽  
Author(s):  
Wade T. Crow ◽  
Emiel Van Loon
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Soil ◽  
Random Noise ◽  
Land Surface Model ◽  
Model Error ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Incorrect Model

Abstract Data assimilation approaches require some type of state forecast error covariance information in order to optimally merge model predictions with observations. The ensemble Kalman filter (EnKF) dynamically derives such information through a Monte Carlo approach and the introduction of random noise in model states, fluxes, and/or forcing data. However, in land data assimilation, relatively little guidance exists concerning strategies for selecting the appropriate magnitude and/or type of introduced model noise. In addition, little is known about the sensitivity of filter prediction accuracy to (potentially) inappropriate assumptions concerning the source and magnitude of modeling error. Using a series of synthetic identical twin experiments, this analysis explores the consequences of making incorrect assumptions concerning the source and magnitude of model error on the efficiency of assimilating surface soil moisture observations to constrain deeper root-zone soil moisture predictions made by a land surface model. Results suggest that inappropriate model error assumptions can lead to circumstances in which the assimilation of surface soil moisture observations actually degrades the performance of a land surface model (relative to open-loop assimilations that lack a data assimilation component). Prospects for diagnosing such circumstances and adaptively correcting the culpable model error assumptions using filter innovations are discussed. The dual assimilation of both runoff (from streamflow) and surface soil moisture observations appears to offer a more robust assimilation framework where incorrect model error assumptions are more readily diagnosed via filter innovations.

scholarly journals The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages

2019 ◽  
Author(s):  
Xinxuan Zhang ◽  
Viviana Maggioni ◽  
Azbina Rahman ◽  
Paul Houser ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Global Water

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.

scholarly journals Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

2014 ◽  
Vol 18 (1) ◽  
pp. 173-192 ◽  
Author(s):  
A. L. Barbu ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
S. Lafont
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Land Data Assimilation ◽  
The Impact

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations.

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 Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

2018 ◽  
Vol 19 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Y. Malbéteau ◽  
O. Merlin ◽  
G. Balsamo ◽  
S. Er-Raki ◽  
S. Khabba ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Surface Soil ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Spatiotemporal Resolution

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

scholarly journals A comparison between remotely-sensed and modelled surface soil moisture (and frozen status) at high latitudes

2013 ◽  
Vol 10 (8) ◽  
pp. 11241-11291 ◽  
Author(s):  
I. Gouttevin ◽  
A. Bartsch ◽  
G. Krinner ◽  
V. Naeimi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Remotely Sensed ◽  
Hydrological Modelling ◽  
Northern Eurasia ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
North Eastern

Abstract. In this study, the combined surface status and surface soil moisture products retrieved by the ASCAT sensor within the ESA-DUE Permafrost project are compared to the hydrological outputs of the land surface model ORCHIDEE over Northern Eurasia. The objective is to derive broad conclusions as to the strengths and weaknesses of hydrological modelling and, to a minor extent, remote sensing of soil moisture over an area where data is rare and hydrological modelling is though crucial for climate and ecological applications. The spatial and temporal resolutions of the ASCAT products make them suitable for comparison with model outputs. Modelled and remotely-sensed surface frozen and unfrozen statuses agree reasonably well, which allows for a seasonal comparison between modelled and observed (liquid) surface soil moisture. The atmospheric forcing and the snow scheme of the land surface model are identified as causes of moderate model-to-data divergence in terms of surface status. For unfrozen soils, the modelled and remotely-sensed surface soil moisture signals are positively correlated over most of the study area. The correlation deteriorates in the North-Eastern Siberian regions, which is consistent with the lack of accurate model parameters and the scarcity of meteorological data. The model shows a reduced ability to capture the main seasonal dynamics and spatial patterns of observed surface soil moisture in Northern Eurasia, namely a characteristic spring surface moistening resulting from snow melt and flooding. We hypothesize that these weak performances mainly originate from the non-representation of flooding and surface ponding in the model. Further identified limitations proceed from the coarse treatment of the hydrological specificities of mountainous areas and spatial inaccuracies in the meteorological forcing in remote, North-Eastern Siberian areas. Investigations are currently underway to determine to which extent plausible inaccuracies in the satellite data could also contribute to the diagnosed model-to-data discrepancies.

scholarly journals An Ensemble Square Root Filter for the joint assimilation of surface soil moiture and leaf area index within LDAS-Monde: application over the Euro-Mediterranean region

2019 ◽  
Author(s):  
Bertrand Bonan ◽  
Clément Albergel ◽  
Yongjun Zheng ◽  
Alina Lavinia Barbu ◽  
David Fairbairn ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Land Surface ◽  
Mediterranean Region ◽  
Surface Soil ◽  
Positive Impact ◽  
Square Root ◽  
Area Index ◽  
Square Root Filter

Abstract. This paper introduces an Ensemble Square Root Filter (EnSRF), a deterministic Ensemble Kalman Filter, to the context of assimilating jointly observations of surface soil moisture (SSM) and leaf area index (LAI) in the Land Data Assimilation System LDAS-Monde. By ingesting those satellite-derived products, LDAS-Monde constrains the Interaction between Soil, Biosphere and Atmosphere (ISBA) land surface model (LSM), coupled with the CNRM (Centre National de Recherches Météorologiques) version of the Total Runoff Integrating Pathways (CTRIP), to improve the reanalysis of land surface variables (LSVs). To evaluate its ability to produce improved LSVs reanalyses, the EnSRF is compared with the Simplified Extended Kalman, which has been routinely operated in LDAS-Monde, in a real case over the well-studied Euro-Mediterranean region at a 0.25° spatial resolution between 2008 and 2017. Both data assimilation approaches provide a positive impact on SSM and LAI estimates with respect to the model alone, putting them closer to assimilated observations. SEKF and EnSRF have a similar behaviour for LAI showing performances that are influenced by the vegetation type. For SSM, EnSRF estimates tend to be closer to observations than SEKF. The impact of assimilating SSM and LAI is also assessed on unobserved soil moisture in the other layers of soil. Unobserved control variables are updated in the EnSRF through covariances and correlations sampled from the ensemble linking them to observed control variables. In our context, a strong correlation between SSM and soil moisture in deeper soil layers is exhibited, as expected, showing seasonal patterns that vary geographically. Moderate correlation and anti-correlations are also noticed between LAI and soil moisture in spring, summer and autumn, their absolute value tending to be larger for soil moisture in root-zone areas, showing that assimilating LAI can have an influence on soil moisture. Finally an independent evaluation of both assimilation approaches is conducted using satellite estimates of evapotranspiration and gross primary production (GPP) as well as measures of river discharges from gauging stations. The EnSRF shows a systematic albeit moderate improvement for evapotranspiration and GPP and a highly positive impact on river discharges, while the SEKF exhibits a more contrasting performance.

scholarly journals Assimilation of vegetation optical depth retrievals from passive microwave radiometry

2020 ◽  
Vol 24 (7) ◽  
pp. 3431-3450
Author(s):  
Sujay V. Kumar ◽  
Thomas R. Holmes ◽  
Rajat Bindlish ◽  
Richard de Jeu ◽  
Christa Peters-Lidard
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Optical Sensors ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Weather Conditions ◽  
Passive Microwave ◽  
Surface Model ◽  
Surface Soil Moisture

Abstract. Vegetation optical depth (VOD) retrievals from passive microwave sensors provide analog estimates of above-ground canopy biomass. This study presents the development and analysis of assimilating VOD retrievals from X-, C-, and L-band passive microwave instruments within the Noah-MP land surface model over the Continental U.S. The results from this study demonstrate that the assimilation of VOD retrievals have a significant beneficial impact on the simulation of evapotranspiration and GPP, particularly over the agricultural areas of the U.S. The improvements in the water and carbon fluxes from the assimilation of VOD from X- and C-band sensors are found to be comparable to those obtained from the assimilation of vegetation indices from optical sensors. The study also quantifies the relative and joint impacts of assimilating surface soil moisture and VOD from the Soil Moisture Active Passive (SMAP) mission. The utility of soil moisture assimilation for improving evapotranspiration (ET) is more significant over water-limited regions, whereas VOD DA is more impactful over areas where soil moisture is not the primary controlling factor on ET. The results also indicate that the information on moisture and vegetation states from SMAP can be simultaneously exploited through the joint assimilation of surface soil moisture and VOD. Since passive microwave-based VOD retrievals are available in nearly all weather conditions, their use within data assimilation systems offers the ability to extend and improve the utility obtained from the use of optical/infrared-based vegetation retrievals.

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