# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 3 Results and Discussions
### Monitoring the Heatwave Impact on Vegetation Lising LDAS-Monde
Lower panel, same as upper panel between modeled/analyzed soil moisture from the second layer of soil (1–4 cm) and ASCAT surface soil moisture estimates from the Copernicus Global Land Service project. similar LAI patterns. They both underestimate the amplitude and spatial extent of the drought in the open-loop, and for both the analysis effectively improves the particular LAI conditions associated to the 2018 heatwave. Furthermore, due to the large-scale nature of the drought event the spatial resolution differences between ERA5 and HRES do not affect significantly the simulations. Figure 7 represents maps of monthly anomalies from LDAS-ERA5 for July 2008, 2010, 2012, 2014, 2016 and 2018 for soil moisture in the fourth layer of soil (wg4, between 20 cm and 40 cm) as well as drainage, runoff and evapotranspiration over most of the UK. July 2018 presents the strongest negative anomalies. Figure 7 is complementary to Figures 4-6 illustrating how the data assimilation system impacts the model variables. Assimilating SSM and LAI within LDAS-Monde results in updates of the LSM variables in different ways. Control variables in the state vector (LAI and soil moisture from several layers of soil) are first updated through the Kalman gain computed by the SEKF. That gain especially provides sensitivity to observed variables (LAI and the second layer of soil, wg2, between 1 cm and 4 cm) for unobserved variables allowing their updates by the SEKF (e.g., soil moisture from the fourth layer of soil, wg4, top row of Figure 7). Then, other variables are indirectly modified by the analysis through biophysical processes and feedback in the model by updates of the control variables (e.g., drainage, runoff and evapotranspiration as seen on Figure 7). It is worth mentioning the positive anomaly values for July 2012, particularly in runoff and drainage responding to persistent rain during the first weekend of July that had led to flooding in many part of the UK ([[https://www.metoffice.gov.uk/learning/learn-about-the-weather/weather-phenomena/case-studies/july-2012-flooding](https://www.metoffice.gov.uk/learning/learn-about-the-weather/weather-phenomena/case-studies/july-2012-flooding)]([https://www.metoffice.gov.uk/learning/learn-about-the-weather/weather-phenomena/case-studies/july-2012-flooding](https://www.metoffice.gov.uk/learning/learn-about-the-weather/weather-phenomena/case-studies/july-2012-flooding)), last access January 2019). Figure 6: Upper panel, Leaf Area Index from (**a**) LDAS-ERA5 Open-loop, (**b**) the observations, (**c**) LDAS-ERA5 Analysis and (**d**) differences between LDAS-ERA5 Analysis and LDAS-ERA5 Open-loop for July 2018. Lower panel, (**e**–**h**) same as upper panel for LDAS-HRES. Spatial resolution of upper panel is 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\), spatial resolution of lower panel is 0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\). Both LDAS-Monde configurations forced by either ERA5 or HRES lead to an accurate representation of vegetation during the summer 2018 heatwave and in general. The HRES configuration presents slightly better results over the common period investigated. HRES being obtained from frequently updated versions of the IFS it is not a fixed system in time, while a reanalysis like ERA5 guarantees a higher level of consistency because of its frozen configuration. ERA5 has a coarser spatial resolution than the HRES. Its spatial resolution allows, however LDAS experiments to be long term and affordable at large scale. With ERA5 available back to 1950 and covering near real-time needs with the ERA5T ([[https://climate.copernicus.eu/climate-reanalysis](https://climate.copernicus.eu/climate-reanalysis)]([https://climate.copernicus.eu/climate-reanalysis](https://climate.copernicus.eu/climate-reanalysis)), last access March 2019), an LDAS-ERA5 would be able to provide a model climate as reference for anomalies of the land surface conditions.