Calibration of the Global Flood Awareness System (GloFAS) using daily streamflow data.

This paper presents the calibration and evaluation of the Global Flood Awareness System (GloFAS), an operational system that produces ensemble streamflow forecasts and threshold exceedance probabilities for large rivers worldwide. The system generates daily streamflow forecasts using a coupled H-TESSEL land surface scheme and the LISFLOOD model forced by ECMWF IFS meteorological forecasts. The hydrology model currently uses a priori parameter estimates with uniform values globally, which may limit the streamflow forecast skill. Here, the LISFLOOD routing and groundwater model parameters are calibrated with ECMWF reforecasts from 1995 to 2015 as forcing using daily streamflow data from 1287 stations worldwide. The calibration of LISFLOOD parameters is performed using an evolutionary optimization algorithm with the Kling-Gupta Efficiency (KGE) as objective function. The skill improvements are quantified by computing the skill scores as the change in KGE relative to the baseline simulation using a priori parameters. The results show that simulation skill has improved after calibration (KGE skill score > 0.08) for the large majority of stations during the calibration (67% globally and 77% outside of North America) and validation (60% globally and 69% outside of North America) periods compared to the baseline simulation. However, the skill gain was impacted by the bias in the baseline simulation (the lowest skill score was obtained in basins with negative bias) due to the limitation of the model in correcting the negative bias in streamflow. Hence, further skill improvements could be achieved by reducing the bias in the streamflow by improving the precipitation forecasts and the land surface model. The results of this work will have implications on improving the operational GloFAS flood forecasting (www.globalfloods.eu).

E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time.

In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48-72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved.

The number of people exposed to water stress in relation to how much water is reserved for the environment: a global modelling study.

BACKGROUND: Increasing human demand for water and changes in water availability due to climate change threatens water security worldwide. Additionally, exploitation of water resources induces stress on freshwater environments, leading to biodiversity loss and reduced ecosystem services. We aimed to conduct a spatially detailed assessment of global human water stress for low to high environmental flow (EF) protection. METHODS: In this modelling study, we used the LISFLOOD model to generate daily natural flows without anthropogenic water use for 1980-2018. On the basis of these flows, we selected three EF methods (EF with high ecological protection [EFPROT], EF with minimum flow requirements [EFMIN], and variable monthly flow [EFVMF]) to calculate monthly EFs. We assessed monthly consumptive water use for industry, agricultural crops, livestock, municipalities, and energy production for 2010. We then estimated the corresponding number of people under water stress per month on a global and national level using a spatially detailed population database for 2010. FINDINGS: We estimate that 3·2 billion (EFPROT), 2·4 billion (EFVMF), and 2·2 billion (EFMIN) people lived under water stress for at least 1 month per year, corresponding to 46%, 35%, and 32% of the world's population in 2010, respectively. Around 80% of people living under water stress lived in Asia; in particular, India, Pakistan, and northeast China. Compared with EFMIN, imposing EFPROT globally would have put between 710 million (March) to 1 billion (June) additional people under water stress on a monthly basis, whereas this would have been 72 million (August) to 218 million (April) additional people if EFVMF were imposed. INTERPRETATION: Ensuring high ecological protection would put nearly half of the world's population (3·2 billion people) under water stress for at least 1 month per year. Policy makers and water managers have to make an important trade-off when allocating limited water resources between direct human needs and the environment. A better understanding of local ecosystem needs is crucial to alleviating current and future human water stress, while sustaining healthy ecosystems. FUNDING: None.

A global streamflow reanalysis for 1980-2018.

Global and continental scale hydrological reanalysis datasets receive growing attention due to their increasing number of applications, ranging from water resources management, climate change studies, water related hazards and policy support. Until recently, their use was mostly limited to qualitative assessments, due to their coarse spatial and temporal resolution, large uncertainty and bias in the model output, and limited extent of the dataset in space and time. This research reports on the setup of a gridded hydrological model with quasi-global coverage, able to reproduce a seamless 39-year streamflow simulation in all world's medium to large river basins. The model was calibrated at 1226 river sections with a total drainage area of 51 million km2 within 66 countries, using ECMWF's latest atmospheric reanalysis ERA5. A performance assessment revealed large improvements in reproducing past discharge observations, in comparison to the calibration used in the current operational setup of the hydrological model as part of the Copernicus - Global Flood Awareness System (GloFAS, www.globalfloods.eu), with median scores of Kling-Gupta Efficiency KGE = 0.67 and correlation r = 0.8. The simulation bias was also dramatically reduced and narrowed around zero, with more than 60% of stations showing percent bias within ±20%. Pronounced regional differences in the simulation results remain, pointing out the need for detailed investigation of the hydrological processes in specific regions, including parts of Africa and South Asia. In addition, observed discharges with high data quality is key to achieving skillful model output. The new calibrated model will become part of the operational runs of GloFAS in the next system release foreseen for Spring 2020, together with a near real time extension of the streamflow reanalysis.