scholarly journals Droughts and heatwaves in the Western Mediterranean: impact on vegetation and wildfires using the coupled WRF-ORCHIDEE regional model (RegIPSL)

2021 ◽  
Author(s):  
Antoine Guion ◽  
Solène Turquety ◽  
Jan Polcher ◽  
Romain Pennel ◽  
Sophie Bastin ◽  
...  
Keyword(s):  
Extreme Events ◽  
Land Surface ◽  
Synergistic Effects ◽  
Regional Model ◽  
Western Mediterranean ◽  
Extreme Weather Events ◽  
Fuel Load ◽  
Burned Area ◽  
Area Index ◽  
The Impact

AbstractDroughts and heatwaves in the Mediterranean can induce plant activity decline and severe wildfires leading to considerable economic, social and environmental damages. This study aims at statistically quantifying the isolated and combined impacts of these extreme events based on a combination of regional land surface-atmosphere modeling and satellite observations of surface properties (MODIS). A simulation by the RegIPSL coupled regional model (ORCHIDEE-WRF) over the 1979–2016 period in the Western Mediterranean is used to identify heatwaves and droughts. After an evaluation of the model performance against surface observations of temperature and precipitation, a spatio-temporal analysis is conducted using specific indicators of extreme events: Percentile Limit Anomalies (PLA) and the Standardized Precipitation Evapotranspiration Index (SPEI). The impact on vegetation and wildfires is assessed using the MODIS observations of Leaf Area Index (LAI), burned area (BA) and fire radiative power (FRP), clustered by simulated extreme weather events. Due to water stress, droughts lead to significant biomass decrease (− 10$$\%$$ % LAI on average and reaching − 23$$\%$$ % in some areas). The isolated effect of heatwaves is smaller ($$\sim$$ ∼  − 3$$\%$$ % LAI) so that the combined effect is dominated by the impact of droughts. Heatwaves and droughts significantly exacerbate wildfire regimes. Through synergistic effects, simultaneous droughts and heatwaves increase BA and FRP by 2.1 and 2.9 times, respectively, compared to normal conditions. By reducing biomass, droughts slightly decrease fuel availability. However, our results show that the inter-annual variation in fire activity is mainly driven by weather conditions rather than fuel load.

Identification of droughts and heatwaves in the Western Mediterranean, variability and impacts on vegetation and wildfires using the coupled ORCHIDEE-WRF regional model

2020 ◽  
Author(s):  
Antoine Guion ◽  
Solène Turquety ◽  
Jan Polcher ◽  
Romain Pennel ◽  
Lluis Fita
Keyword(s):  
Extreme Events ◽  
Regional Model ◽  
Western Mediterranean ◽  
Plant Phenology ◽  
Extreme Weather Events ◽  
Area Index ◽  
Fire Weather Index ◽  
Mitigation And Adaptation ◽  
Extreme High Temperature ◽  
The Impact

<p>In line with what is expected in a context of global warming, droughts and heatwaves have increased both in frequency and intensity over the last century. Severe wildfires and vegetation depletion can result from those extreme weather events with considerable economic, social and environmental damages.</p><p>For the development of mitigation and adaptation strategies, there is a need for exhaustive vulnerability assessments, including the impacts of droughts and heatwaves on the Mediterranean environment.</p><p>If heatwave characteristics are well documented, similar studies about droughts are partial. Most of them are focused on meteorological droughts while agronomical ones are more complex to identify.</p><p> </p><p>Using a coupled land surface–atmosphere regional model (ORCHIDEE-WRF) with the integration of plant phenology, we present an analysis of droughts and heatwaves occurring in the Western Mediterranean over the last 40 years. These extreme events are identified using two complementary methods: the Percentile Limit Anomalies (PLA) and the Standardized Precipitation Evapotranspiration Index (SPEI).</p><p>Impact assessment analysis show significant and dominant effect of droughts on plant phenology during summer. Evaluated using the Leaf Area Index (LAI), plant depletion can reach more than 50%. Response to drought depends on the vegetation type (long vs short root system) and biome (temperate vs semi-arid).</p><p>The impact of these extreme events on fire risk will be presented based on calculations of the wildfire meteorological risk (Fire Weather Index) and an analysis of the fire activity observed by the MODIS satellite instrument. We show that, even if extreme high temperature is the dominant cause, drought contributes to an increase of risk. Simultaneous heatwaves and droughts are the worst environmental conditions. The observed burned area can be <span>±</span>4 times greater than during non-extreme conditions and the fire duration <span>±</span>0.25 times longer.</p>

scholarly journals Modelling African biomass burning emissions and the effect of spatial resolution

2019 ◽  
Author(s):  
Dave van Wees ◽  
Guido R. van der Werf
Keyword(s):  
Spatial Resolution ◽  
Input Data ◽  
Large Scale ◽  
Sub Saharan Africa ◽  
Fuel Load ◽  
Burned Area ◽  
Fire Emissions ◽  
Continental Scale ◽  
Coarse Resolution ◽  
The Impact

Abstract. Large-scale fire emission estimates may be influenced by the spatial resolution of the model and input datasets used. Especially in areas with relatively heterogeneous land cover, a coarse model resolution might lead to substantial errors in estimates. In this paper, we developed a model using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations of burned area and vegetation characteristics to study the impact of spatial resolution on modelled fire emission estimates. We estimated fire emissions for sub-Saharan Africa at 500-meter spatial resolution (native MODIS burned area) for the 2002–2017 period, using a simplified version of the Global Fire Emissions Database (GFED) modelling framework, and compared this to model runs at a range of coarser resolutions (0.050°, 0.125°, 0.250°). We estimated fire emissions of 0.68 PgC yr−1 at 500-meter resolution and 0.82 PgC yr−1 at 0.25° resolution; a difference of 24 %. At 0.25° resolution, our model results were relatively similar to GFED4, which also runs at 0.25° resolution, whereas our 500-meter estimates were substantially lower. We found that lower emissions at finer resolutions are mainly the result of reduced representation errors when comparing modelled estimates of fuel load and consumption to field measurements, as part of the model calibration. Additional errors stem from the model simulation at coarse resolution and lead to an additional 0.02 PgC yr−1 difference in estimates. These errors exist due to the aggregation of quantitative and qualitative model input data; the average- or majority- aggregated values are propagated in the coarse resolution simulation and affect the model parameterization and the final result. We identified at least three error mechanisms responsible for the differences in estimates between 500-meter and 0.25° resolution simulations, besides those stemming from representation errors in the calibration process, namely: 1. biome misclassification leading to errors in parameterization, 2. errors due to the averaging of input data and the associated reduction in variability, and 3. a temporal mechanism related to the aggregation of burned area in particular. Even though these mechanisms largely neutralized each other and only modestly affect estimates at a continental scale, they lead to substantial error at regional scales with deviations up to a factor 4, and may affect large-scale estimates differently for other continents. These findings could prove valuable in improving coarse resolution models and suggest the need for increased spatial resolution in global fire emission models.

scholarly journals Effects of high spatial and temporal resolution Earth observations on simulated hydrometeorological variables in a cropland (southwestern France)

2017 ◽  
Vol 21 (11) ◽  
pp. 5693-5708 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Tiphaine Tallec ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Surface Model ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes are often constituted by a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes and must be taken into account in simulations of land surface and distributed hydrological models. The Sentinel-2 mission allows for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy fluxes via the Interactions between the Surface Biosphere Atmosphere (ISBA) land surface model included in the EXternalized SURface (SURFEX) modeling platform. The study focuses on the effect of the leaf area index (LAI) spatial and temporal variability on these fluxes. We compare the use of the LAI climatology from ECOCLIMAP-II, used by default in SURFEX-ISBA, and time series of LAI derived from the high-resolution Formosat-2 satellite data (8 m). The study area is an agricultural zone in southwestern France covering 576 km2 (24 km  ×  24 km). An innovative plot-scale approach is used, in which each computational unit has a homogeneous vegetation type. Evaluation of the simulations quality is done by comparing model outputs with in situ eddy covariance measurements of latent heat flux (LE). Our results show that the use of LAI derived from high-resolution remote sensing significantly improves simulated evapotranspiration with respect to ECOCLIMAP-II, especially when the surface is covered with summer crops. The comparison with in situ measurements shows an improvement of roughly 0.3 in the correlation coefficient and a decrease of around 30 % of the root mean square error (RMSE) in the simulated evapotranspiration. This finding is attributable to a better description of LAI evolution processes with Formosat-2 data, which further modify soil water content and drainage of soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels with simulations using Formosat-2 data in comparison to the reference simulation values. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

Assessment of different indices from Landsat time series in burned area mapping

2020 ◽  
Author(s):  
Jinxiu Liu
Keyword(s):  
Time Series ◽  
Burkina Faso ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Burned Area ◽  
Free Access ◽  
Landsat Images ◽  
Area Index ◽  
Burned Area Mapping

<p>Fire is recognized as an important land surface disturbance, as it influences terrestrial carbon cycle, climate and biodiversity. Accurate and efficient mapping of burned area is beneficial for social and environmental applications. Remote sensing plays a key role in detecting burned areas and active fires from reginal to global scales. Due to the free access to the Landsat archive, studies using dense time series of Landsat imagery for burned area mapping are appearing and increasing. However, the performance of Landsat time series when using different indices for burned area mapping has not been assessed. In this study, the objective was to identify which indices can detect burned area better when using Landsat time series in savanna area of southern Burkina Faso. We selected Burned Area Index (BAI), Normalized Burned Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Global Environmental Monitoring Index (GEMI) for comparison as they are commonly used indices for burned area detection. The algorithm was based on breakpoint identification and burned pixel detection using harmonic model fitting with different indices Landsat time series. It was tested in savanna area in southern Burkina Faso over 16 years with 281 Landsat images ranging from October 2000 to April 2016.The same reference data was used to evaluate the performance of burned area detection with different indices Landsat time series. The result demonstrated that BAI was the most accurate in burned area detection from Landsat time series, followed by NBR, GEMI and NDVI.</p>

scholarly journals Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

2010 ◽  
Vol 14 (10) ◽  
pp. 2073-2084 ◽  
Author(s):  
F. Zabel ◽  
T. B. Hank ◽  
W. Mauser
Keyword(s):  
Land Cover ◽  
Central Europe ◽  
Land Surface ◽  
Vegetation Index ◽  
Arable Land ◽  
Area Index ◽  
Temporal Behaviour ◽  
Danube Catchment ◽  
Crop Types ◽  
The Impact

Abstract. Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI) measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC) 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI) data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

scholarly journals Implementation and validation of a new irrigation scheme in the ISBA land surface model

2021 ◽  
Author(s):  
Arsène Druel ◽  
Simon Munier ◽  
Anthony Mucia ◽  
Clément Albergel ◽  
Jean-Christophe Calvet
Keyword(s):  
Land Surface ◽  
Vegetation Type ◽  
Spatial Scales ◽  
Terrestrial Ecosystems ◽  
Moisture Stress ◽  
Surface Model ◽  
Irrigation Scheme ◽  
Area Index ◽  
Natural Processes ◽  
The Impact

Abstract. With an increase in the number of natural processes represented, global land surface models (LSMs) have become more and more accurate in representing natural terrestrial ecosystems. However, they are still limited, especially in the representation of the impact of agriculture on land surface variables. This is particularly true for agro-hydrological processes related to a strong human control on freshwater. While most LSMs consider natural processes only, the development of human-related processes, e.g. crop phenology and irrigation in LSMs, is key. In this study we present the implementation of a new irrigation scheme in the ISBA (Interaction between Soil, Biosphere, and Atmosphere) LSM. This highly flexible scheme is designed to account for various configurations and can be applied at different spatial scales. For each vegetation type within a model grid cell, three irrigation systems can be used at the same time. A limited number of parameters are used to control (1) the amount of water used for irrigation, (2) irrigation triggering (based on the soil moisture stress) and (3) crop seasonality (emergence, harvesting). After a presentation of the simulations of the new scheme at a plot scale, an evaluation is proposed over Nebraska (USA). This region is chosen for its high irrigation density and because independent observations of irrigation practices can be used to verify the simulated irrigation amounts. The ISBA simulations with and without the irrigation scheme are compared to different satellite-based observations. The comparison shows that the irrigation scheme improves the simulated vegetation variables such as leaf area index and gross primary productivity and other variables largely impacted by irrigation such as evapotranspiration and land surface temperature. In addition to a better representation of land surface processes, the results point to potential applications of this new version of the ISBA model for water resource monitoring and climate change impact studies.

scholarly journals LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the CONtiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables

2018 ◽  
Author(s):  
Clément Albergel ◽  
Simon Munier ◽  
Aymeric Bocher ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Initial Conditions ◽  
The United States ◽  
Agricultural Drought ◽  
Reference Network ◽  
Surface Model ◽  
Area Index ◽  
Medium Range Weather Forecast ◽  
The Impact

LDAS-Monde, an offline land data assimilation system with global capacity, is applied over the CONtiguous US (CONUS) domain to enhance monitoring accuracy for water and energy states and fluxes. LDAS-Monde ingests satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) estimates to constrain the Interactions 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) continental hydrological system (ISBA-CTRIP). LDAS-Monde is forced by the ERA-5 atmospheric reanalysis from the European Center For Medium Range Weather Forecast (ECMWF) from 2010 to 2016 leading to a 7-yr, quarter degree spatial resolution offline reanalysis of Land Surface Variables (LSVs) over CONUS. The impact of assimilating LAI and SSM into LDAS-Monde is assessed over North America, by comparison to satellite-driven model estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project, and upscaled ground-based observations of gross primary productivity from the FLUXCOM project. Also, taking advantage of the relatively dense data networks over CONUS, we also evaluate the impact of the assimilation against in-situ measurements of soil moisture from the USCRN network (US Climate Reference Network) are used in the evaluation, together with river discharges from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). Those data sets highlight the added value of assimilating satellite derived observations compared to an open-loop simulation (i.e. no assimilation). It is shown that LDAS-Monde has the ability not only to monitor land surface variables but also to forecast them, by providing improved initial conditions which impacts persist through time. LDAS-Monde reanalysis has a potential to be used to monitor extreme events like agricultural drought, also. Finally, limitations related to LDAS-Monde and current satellite-derived observations are exposed as well as several insights on how to use alternative datasets to analyze soil moisture and vegetation state.

scholarly journals JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator

2014 ◽  
Vol 7 (5) ◽  
pp. 6773-6809
Author(s):  
T. Osborne ◽  
J. Gornall ◽  
J. Hooker ◽  
K. Williams ◽  
A. Wiltshire ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Land Surface Model ◽  
Water Security ◽  
Climate Change Impacts ◽  
Heat Fluxes ◽  
Surface Model ◽  
Yield Model ◽  
Area Index ◽  
The Impact

Abstract. Studies of climate change impacts on the terrestrial biosphere have been completed without recognition of the integrated nature of the biosphere. Improved assessment of the impacts of climate change on food and water security requires the development and use of models not only representing each component but also their interactions. To meet this requirement the Joint UK Land Environment Simulator (JULES) land surface model has been modified to include a generic parametrisation of annual crops. The new model, JULES-crop, is described and evaluation at global and site levels for the four globally important crops; wheat, soy bean, maize and rice is presented. JULES-crop demonstrates skill in simulating the inter-annual variations of yield for maize and soy bean at the global level, and for wheat for major spring wheat producing countries. The impact of the new parametrisation, compared to the standard configuration, on the simulation of surface heat fluxes is largely an alteration of the partitioning between latent and sensible heat fluxes during the later part of the growing season. Further evaluation at the site level shows the model captures the seasonality of leaf area index and canopy height better than in standard JULES. However, this does not lead to an improvement in the simulation of sensible and latent heat fluxes. The performance of JULES-crop from both an earth system and crop yield model perspective is encouraging however, more effort is needed to develop the parameterisation of the model for specific applications. Key future model developments identified include the specification of the yield gap to enable better representation of the spatial variability in yield.

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

2019 ◽  
Author(s):  
Clément Albergel ◽  
Emanuel Dutra ◽  
Bertrand Bonan ◽  
Yongjun Zheng ◽  
Simon Munier ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Western Europe ◽  
Initial Conditions ◽  
Added Value ◽  
Area Index ◽  
Long Time ◽  
Land Data Assimilation System ◽  
The Impact

This study aims to assess the potential of the LDAS-Monde a land data assimilation system developed by Météo-France to monitor the impact of the 2018 summer heatwave over western Europe vegetation state. The LDAS-Monde is forced by the ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). Analysis of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of North Western Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the considered domain have never been observed in the LAI product over this 18-yr period. The LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25°x0.25° (January 2008 to October 2018) and 0.10°x0.10° (April 2016 to December 2018). Both configuration of the LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5 and IFS HRES driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g. associated to a better representation of the land cover, topography) and using HRES forcing still enhance the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedbacks in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model only initial conditions.

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