Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

Albergel, Clément; Dutra, Emanuel; Bonan, Bertrand; Zheng, Yongjun; Munier, Simon; Balsamo, Gianpaolo; Rosnay, Patricia de; Muñoz‐Sabater, Joaquín; Calvet, Jean‐Christophe

doi:10.3390/rs11050520

Cited by 55 publications

(44 citation statements)

References 77 publications

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“…LDAS-Monde could also be forced by ECMWF Integrated Forecasting System High Resolution operational analysis (~0.1 • × 0.1 • spatial resolution). In addition to its higher spatial resolution, it offers the possibility to monitor and forecast LSVs, as shown in [85]. Our results over Burkina-Faso are very promising and pave the way toward large-scale long-term reanalyses of the land surface conditions in Western Africa.…”

Section: Discussionmentioning

confidence: 67%

Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

et al. 2019

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This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

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Section: Discussionmentioning

confidence: 67%

Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

et al. 2019

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“…By assimilating observed/remotely sensed LAI into the Biome-BGC (BioGeochemical Cycles) model in the Harvard forest region, considerable improvements in the simulation of water and carbon fluxes have been found (Zhang et al, 2013). Rüdiger et al (2010), Albergel et al (2010Albergel et al ( , 2017Albergel et al ( , 2019, and Barbu et al (2011) have assimilated satellite-derived vegetation and soil moisture products based on a Simplified Extended Kalman Filter and have found a strong impact on LAI itself, as well as significant improvements for river discharge, land ET, and gross primary production (GPP). Similar studies were conducted in the fields of agriculture (Bao et al, 2015;Dong et al, 2013) and hydrology, and most were conducted at a single site or on regional scales (Fox et al, 2018;Li et al, 2017;Montzka et al, 2012;Pauwels et al, 2007;Sabater et al, 2008).…”

Section: 1029/2019ms001634mentioning

confidence: 99%

Assimilation of Remotely Sensed LAI Into CLM4CN Using DART

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et al. 2019

J Adv Model Earth Syst

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Plant leaves play an important role in water, carbon, and energy exchanges between terrestrial ecosystems and atmosphere. Assimilating remotely sensed leaf area index (LAI) into land surface models is a promising approach to improve our understanding of those processes. Toward this goal, this study uses the Community Land Model with carbon and nitrogen components (CLM4CN) coupled with the Data Assimilation Research Testbed (DART). Global Land Surface Satellite (GLASS) LAI data are assimilated via the Ensemble Adjustment Kalman Filter. A random 40‐member atmospheric forcing ensemble is used to drive the CLM4CN to provide background error covariance. The results show that assimilating GLASS LAI and updating both LAI and leaf C/N is an effective method to provide a high‐accuracy estimate of LAI. The simulations always systematically overestimate LAI, especially in low‐latitude regions, with the largest bias up to 5 m2/m2, which are effectively corrected in the analyzed LAI, with the bias reduced to ±1 m2/m2. Significantly improved regions are located in central Africa, Amazonia, southern Eurasia, northeastern China, and western Europe, where evergreen/deciduous forests and mixed forests are dominant. Except for the temperate zone in the Southern Hemisphere, the analyzed LAI can well represent seasonal variations. The most pronounced assimilation impact in low‐latitude regions is attributed to large initial forecast error covariance and sufficient background errors. The MOD 16 evapotranspiration estimates and upscaled gross primary production have been used to evaluate the assimilation impact, which highlight neutral to highly positive improvement.

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“…Soil moisture analysis data are from the Land Data Assimilation System Monde (LDAS-Monde) [32], which has recently been applied to monitor and forecast the impact of the 2018 summer drought on vegetation over central Europe [33]. We run the LDAS-Monde system over the Nordic region using ERA-5 reanalysis atmospheric forcing data and the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model [34,35] within the SURFEX v.8.1 (SURFace EXternalisée) modelling framework [36].…”

Section: Ldas-monde Soil Moisture Datamentioning

confidence: 99%

Monitoring Soil Moisture Drought over Northern High Latitudes from Space

et al. 2019

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Mapping drought from space using, e.g., surface soil moisture (SSM), has become viable in the last decade. However, state of the art SSM retrieval products suffer from very poor coverage over northern latitudes. In this study, we propose an innovative drought indicator with a wider spatial and temporal coverage than that obtained from satellite SSM retrievals. We evaluate passive microwave brightness temperature observations from the Soil Moisture and Ocean Salinity (SMOS) satellite as a surrogate drought metric, and introduce a Standardized Brightness Temperature Index (STBI). We compute the STBI by fitting a Gaussian distribution using monthly brightness temperature data from SMOS; the normal assumption is tested using the Shapior-Wilk test. Our results indicate that the assumption of normally distributed brightness temperature data is valid at the 0.05 significance level. The STBI is validated against drought indices from a land surface data assimilation system (LDAS-Monde), two satellite derived SSM indices, one from SMOS and one from the ESA CCI soil moisture project and a standardized precipitation index based on in situ data from the European Climate Assessment & Dataset (ECA&D) project. When comparing the temporal dynamics of the STBI to the LDAS-Monde drought index we find that it has equal correlation skill to that of the ESA CCI soil moisture product ( 0.71 ). However, in addition the STBI provides improved spatial coverage because no masking has been applied over regions with dense boreal forest. Finally, we evaluate the STBI in a case study of the 2018 Nordic drought. The STBI is found to provide improved spatial and temporal coverage when compared to the drought index created from satellite derived SSM over the Nordic region. Our results indicate that when compared to drought indices from precipitation data and a land data assimilation system, the STBI is qualitatively able to capture the 2018 drought onset, severity and spatial extent. We did see that the STBI was unable to detect the 2018 drought recovery for some areas in the Nordic countries. This false drought detection is likely linked to the recovery of vegetation after the drought, which causes an increase in the passive microwave brightness temperature, hence the STBI shows a dry anomaly instead of normal conditions, as seen for the other drought indices. We argue that the STBI could provide additional information for drought monitoring in regions where the SSM retrieval problem is not well defined. However, it then needs to be accompanied by a vegetation index to account for the recovery of the vegetation which could cause false drought detection.

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Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation

Cited by 55 publications

References 77 publications

Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

Assimilation of Remotely Sensed LAI Into CLM4CN Using DART

Monitoring Soil Moisture Drought over Northern High Latitudes from Space

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