Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Albergel, Clément; Zheng, Yongjun; Bonan, Bertrand; Dutra, Emanuel; Rodriguez‐fernandez, Nemesio; Munier, Simon; Draper, C.; Rosnay, Patricia de; Muñoz‐Sabater, Joaquín; Balsamo, Gianpaolo; Fairbairn, David; Meurey, Catherine; Calvet, Jean‐Christophe

doi:10.5194/hess-24-4291-2020

Cited by 25 publications

(27 citation statements)

References 97 publications

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“…This experiment showed improvements in ET and gross primary production. Albergel et al (2020) has jointly assimilated SSM and LAI using the Simplified Extended Kalman Filter data assimilation technique to predict the impact of extreme events like heatwaves and droughts on land surface conditions over the globe. They have used LDAS-Monde as the land surface model and assimilated ASCAT soil water index (SWI) and LAIGEOV1 LAI observation data within that model.…”

Section: Introductionmentioning

confidence: 99%

Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

Rahman¹,

Zhang²,

Houser³

et al. 2022

Front. Water

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As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land area thanks to the assimilation of leaf area index with and without updating leaf mass, respectively. Random errors in transpiration are also reduced, with an improvement of the unbiased root mean square error in 70% (74%) of the total area without the update of leaf mass (with the update of leaf mass). Similarly, net ecosystem exchange anomaly correlation coefficients improve from 52 to 75% and random errors improve from 49 to 62% of the total pixels after the update of leaf mass. Better performances for both transpiration and net ecosystem exchange are observed across croplands, but the largest improvement is shown over forests and woodland. The global scope of this work makes it particularly important in data poor regions (e.g., Africa, South Asia), where ground observations are sparse or not available altogether but where an accurate estimation of carbon and energy variables can be critical to improve ecosystem and crop management.

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Section: Introductionmentioning

confidence: 99%

Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

Rahman¹,

Zhang²,

Houser³

et al. 2022

Front. Water

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“…ISBA interfaces with an atmospheric and a hydrological model but simulates above-ground plant biomass and LAI with a simplistic plant growth model (ISBA-A-gs) [30]. In another recent work, Albergel et al [31] performed a joint assimilation of satellite-derived LAI and soil water index into the ISBA model to monitor the impact of extreme events (heat waves and droughts), using the Simplified Extended Kalman Filter. This work stems from the studies discussed above and further investigates the potential of jointly assimilating satellite LAI and soil moisture products.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, for the first time, it jointly assimilates GLASS LAI and SMAP soil moisture within the Noah-MP model over the CONUS domain. Noah-MP uses a more sophisticated dynamic vegetation model scheme compared to other similar models used in the literature (e.g., ISBA) [31]. As past work showed that LAI assimilation alone could not improve the estimation of soil moisture (specifically surface soil moisture), the main goal of this work is to improve our estimation and understanding of soil moisture storages in addition to analyzing the impact on vegetation variables.…”

Section: Introductionmentioning

confidence: 99%

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

et al. 2022

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This work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.

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“…Recent extreme droughts in Europe have provided strong research foci in the hydrological community to better understand and forecast periods of water stress (Albergel et al., 2020; Bakke et al., 2020; Kleine et al., 2020; Smith et al., 2020a). In line with long‐term climate change projections, the extreme drought in 2018–2019 provided a unique opportunity to investigate expected regional climate change impacts both in terms of monitoring (e.g., the TERENO observatories in Germany [Heinrich et al., 2019; Wollschläger et al., 2016]) and modeling (e.g., Samaniego et al., 2018; Smith et al., 2020a) flux‐storage dynamics.…”

Section: Introductionmentioning

confidence: 99%

Catchment Functioning Under Prolonged Drought Stress: Tracer‐Aided Ecohydrological Modeling in an Intensively Managed Agricultural Catchment

Yang

Tetzlaff

Soulsby

et al. 2021

Water Resources Research

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In line with long-term climate change projections, the extreme drought in 2018-2019 provided a unique opportunity to investigate expected regional climate change impacts both in terms of monitoring (e.g., the TERENO observatories in Germany [Heinrich et al., 2019; Wollschläger et al., 2016]) and modeling (e.g., Samaniego et al., 2018; Smith et al., 2020a) flux-storage dynamics. As drought impacts propagate through a catchment's ecohydrological cycle (Wilhite & Glantz, 1985), increasingly dry conditions (i.e., a meteorological drought of reduced precipitation and high temperatures) typically deplete soil water storage, gradually supressing "green" (i.e., soil evaporation and vegetation transpiration) and "blue" (i.e., groundwater recharge and runoff generation) water fluxes. However, resilience to droughts and subsequent recovery patterns differ for different compartments of the ecohydrological cycle. Orth and Destouni (2018) showed that blue water fluxes generally decrease more strongly and rapidly than green water fluxes as soil moisture deficits increase. In prolonged droughts, impacts propagate to aquifers, and it generally takes a longer time for recharge fluxes to recover during rewetting periods (Orth & Destouni, 2018). Of course, the general patterns vary under specific physiographic and climate conditions. In regions where surface waters are substantially fed by deeper aquifer storage, blue water fluxes likely

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Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Cited by 25 publications

References 97 publications

Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

Global Assimilation of Remotely Sensed Leaf Area Index: The Impact of Updating More State Variables Within a Land Surface Model

The Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface Model

Catchment Functioning Under Prolonged Drought Stress: Tracer‐Aided Ecohydrological Modeling in an Intensively Managed Agricultural Catchment

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