Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model.

We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

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).

A high-resolution global flood hazard model.

Floods are a natural hazard that affect communities worldwide, but to date the vast majority of flood hazard research and mapping has been undertaken by wealthy developed nations. As populations and economies have grown across the developing world, so too has demand from governments, businesses, and NGOs for modeled flood hazard data in these data-scarce regions. We identify six key challenges faced when developing a flood hazard model that can be applied globally and present a framework methodology that leverages recent cross-disciplinary advances to tackle each challenge. The model produces return period flood hazard maps at ∼90 m resolution for the whole terrestrial land surface between 56°S and 60°N, and results are validated against high-resolution government flood hazard data sets from the UK and Canada. The global model is shown to capture between two thirds and three quarters of the area determined to be at risk in the benchmark data without generating excessive false positive predictions. When aggregated to ∼1 km, mean absolute error in flooded fraction falls to ∼5%. The full complexity global model contains an automatically parameterized subgrid channel network, and comparison to both a simplified 2-D only variant and an independently developed pan-European model shows the explicit inclusion of channels to be a critical contributor to improved model performance. While careful processing of existing global terrain data sets enables reasonable model performance in urban areas, adoption of forthcoming next-generation global terrain data sets will offer the best prospect for a step-change improvement in model performance.

National water shortage for low to high environmental flow protection.

Global freshwater biodiversity has been decreasing rapidly, requiring the restoration and maintenance of environmental flows (EFs) in streams and rivers. EFs provide many ecosystem services that benefit humans. Reserving such EFs for aquatic ecosystems, implies less renewable water availability for direct human water use such as agriculture, industry, cities and energy. Here we show that, depending on the level of EF protection, global annual renewable water availability for humans decreases between 41 and 80% compared to when not reserving EFs. With low EF protection, currently 53 countries experience different levels of water shortage, which increases to 101 countries for high EF protection. Countries will carefully have to balance the amount of water allocated to humans and the environment.

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.

Biases in national and continental flood risk assessments by ignoring spatial dependence.

Recently, flood risk assessments have been extended to national and continental scales. Most of these assessments assume homogeneous scenarios, i.e. the regional risk estimate is obtained by summing up the local estimates, whereas each local damage value has the same probability of exceedance. This homogeneity assumption ignores the spatial variability in the flood generation processes. Here, we develop a multi-site, extreme value statistical model for 379 catchments across Europe, generate synthetic flood time series which consider the spatial correlation between flood peaks in all catchments, and compute corresponding economic damages. We find that the homogeneity assumption overestimates the 200-year flood damage, a benchmark indicator for the insurance industry, by 139%, 188% and 246% for the United Kingdom (UK), Germany and Europe, respectively. Our study demonstrates the importance of considering the spatial dependence patterns, particularly of extremes, in large-scale risk assessments.

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.

Range-dependent thresholds for global flood early warning.

Early warning systems (EWS) for river flooding are strategic tools for effective disaster risk management in many world regions. When driven by ensemble Numerical Weather Predictions (NWP), flood EWS can provide skillful streamflow forecasts beyond the monthly time scale in large river basins. Yet, effective flood detection is challenged by accurate estimation of warning thresholds that identify specific hazard levels along the entire river network and forecast horizon. This research describes a novel approach to estimate warning thresholds which retain statistical consistency with the operational forecasts at all lead times. The procedure is developed in the context of the Global Flood Awareness System (GloFAS). A 21-year forecast-consistent dataset is used to derive thresholds with global coverage and forecast range up to six weeks. These are compared with thresholds derived from ERA5, a state of the art atmospheric reanalysis used to run the baseline simulation for the years 1986-2017 and to give a best guess of the present hydrological states. Findings show that the use of constant thresholds for 30-day flood forecasting, as in the current operational GloFAS setup, is consistent throughout the entire forecast range in only 30% to 40% of the river network, depending on the flood return period. Findings show that range-dependent thresholds, of weekly duration, are a more suitable alternative to time-invariant thresholds, as they improve the model consistency as well as the skills in flood monitoring and early warning, particularly over longer forecasting range.