The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS)

Unravelling a hidden synergy: How pathogen-climate interactions transform habitat hydrology and affect tree growth

Interactions between multiple global change stressors are a defining characteristic of the Anthropocene. Tree-associated pathogens are affecting forested ecosystems worldwide and occur in the context of increased frequency and intensity of extreme climate events such as heat waves, droughts, and floods. The effects of these events, along with subsequent changes in environmental conditions, on remaining and regenerating trees, are not well understood but crucial for the restoration and conservation of forested habitats. In this study, we investigate ash (Fraxinus excelsior) dieback in a temperate broadleaf woodland as a case study to explore the processes influencing non-infected trees during pathogen-induced mortality events. Utilising an experimental setup, we examine tree growth rates at different chronological stages of the disease, including naturally progressing ash dieback (4-5 years since disease outbreak), accelerated ash dieback where ash trees have been girdled (10-15 years), and negligible ash dieback (<20 % ash trees). During a year with typical climatic conditions (2021), soils in accelerated ash dieback plots remained saturated throughout the summer due to insufficient transpiration (57 % higher in the accelerated dieback plots), suggesting a significantly increased risk of summer run-off and floods. However, tree growth rates in these plots were not affected (t-test, t = -0.3 to 1.2, p > 0.05). Conversely, anomalously dry years, such as the 2022 summer drought, saw higher soil moisture in the accelerated ash dieback plots (t-test, t = 4.8, p < 0.01) acting as a buffer, resulting in normal tree growth during drought compared to greatly reduced growth in plots with weaker dieback. These findings emphasise the complex interactions between extreme climate events and pathogen outbreaks. Better understanding of the relationships between pathogens and hydrology on tree growth is imperative and detailed long-term studies on tree growth and hydrology will facilitate and improve mitigation strategies.

Improving Soil Water Content and Surface Flux Estimation Based on Data Assimilation Technique

Land surface model is a powerful tool for estimating continuous soil water content (SWC) and surface fluxes. However, simulation error tends to accumulate in the process of model simulation due to the inevitable uncertainties of forcing data and the intrinsic model errors. Data assimilation techniques consider the uncertainty of the model, update model states during the simulation period, and therefore improve the accuracy of SWC and surface fluxes estimation. In this study, an Ensemble Kalman Filter (EnKF) technique was coupled to a Hydrologically Enhanced Land Process (HELP) model to update model states, including SWC and surface temperature (Ts). The remotely sensed latent heat flux (LE) estimated by Surface Energy Balance System (SEBS) was used as the observation value in the data assimilation system to update the model states such as SWC and Ts, etc. The model was validated by the observation data in 2006 at the Weishan flux station, where the open-loop estimation without state updating was treated as the benchmark run. Results showed that the root mean square error (RMSE) of SWC was reduced by 30%~50% compared to the benchmark run. Meanwhile, the surface fluxes also had significant improvement to different extents, among which the RMSE of LE estimation from the wheat season and maize season reduced by 33% and 44%, respectively. The application of the data assimilation technique can substantially improve the estimation of surface fluxes and SWC states. It is suggested that the data assimilation system has great potential to be used in the application of land surface models in agriculture and water management.

ECLand: The ECMWF Land Surface Modelling System

The land-surface developments of the European Centre for Medium-range Weather Forecasts (ECMWF) are based on the Carbon-Hydrology Tiled Scheme for Surface Exchanges over Land (CHTESSEL) and form an integral part of the Integrated Forecasting System (IFS), supporting a wide range of global weather, climate and environmental applications. In order to structure, coordinate and focus future developments and benefit from international collaboration in new areas, a flexible system named ECLand, which would facilitate modular extensions to support numerical weather prediction (NWP) and society-relevant operational services, for example, Copernicus, is presented. This paper introduces recent examples of novel ECLand developments on (i) vegetation; (ii) snow; (iii) soil; (iv) open water/lake; (v) river/inundation; and (vi) urban areas. The developments are evaluated separately with long-range, atmosphere-forced surface offline simulations and coupled land-atmosphere-ocean experiments. This illustrates the benchmark criteria for assessing both process fidelity with regards to land surface fluxes and reservoirs of the water-energy-carbon exchange on the one hand, and on the other hand the requirements of ECMWF’s NWP, climate and atmospheric composition monitoring services using an Earth system assimilation and prediction framework.

Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets

In recent years, geospatial data (e.g. remote sensing imagery), and other relevant ancillary datasets (e.g. land use land cover, climate conditions) have been utilized through sophisticated algorithms to produce global population datasets. With a handful of such datasets, their performances and skill in flood exposure assessment have not been explored. This study proposes a comprehensive framework to understand the dynamics and differences in population flood exposure over Canada by employing four global population datasets alongside the census data from Statistics Canada as the reference. The flood exposure is quantified based on a set of floodplain maps (for 2015, 1 in 100-yr and 1 in 200-yr event) for Canada derived from the CaMa-Flood global flood model. To obtain further insights at the regional level, the methodology is implemented over six flood-prone River Basins in Canada. We find that about 9% (3.31 million) and 11% (3.90 million) of the Canadian population resides within 1 in 100-yr and 1 in 200-yr floodplains. We notice an excellent performance of WorldPop, and LandScan in most of the cases, which is unaffected by the representation of flood hazard, while Global Human Settlement and Gridded Population of the World showed large deviations. At last, we determined the long-term dynamics of population flood exposure and vulnerability from 2006 to 2019. Through this analysis, we also identify the regions that contain a significantly larger population exposed to floods. The relevant conclusions derived from the study highlight the need for careful selection of population datasets for preventing further amplification of uncertainties in flood risk. We recommend a detailed assessment of the severely exposed regions by including precise ground-level information. The results derived from this study may be useful not only for flood risk management but also contribute to understanding other disaster impacts on human-environment interrelationships.