Papers based on analyses of ISIMIP Fast Track data were cited extensively in the IPCC AR5, published in 2014.
Chapter 3: Freshwater Resources
"The effects of hydrological model parameter uncertainty on simulated runoff changes are typically small when compared with the range from a large number of climate scenarios (Arnell, 2011; Cloke et al., 2010; Lawrence and Haddeland, 2011; Steele-Dunne et al., 2008; Vaze et al., 2010). However, the effects of hydrological model structural uncertainty on projected changes can be substantial (Dankers et al., 2013; Hagemann et al., 2013; Schewe et al., 2013), due to differences in the representation of evaporation and snowmelt processes. In some regions (e.g. high latitudes; Hagemann et al., 2013) with reductions in precipitation (Schewe et al., 2013)), hydrological model uncertainty can be greater than climate model uncertainty – although this is based on small numbers of climate models."
"Each degree of global warming (up to 2.7°C above pre-industrial levels; Schewe et al. 2013) is projected to decrease renewable water resources by at least 20% for an additional 7% of the world population."
"Fourth, estimates of future water availability are sensitive not only to climate and population projections and population assumptions, but also to the choice of hydrological impact model (Schewe et al., 2013) and to the adopted measure of stress or scarcity. As an indication of the potential magnitude of the impact of climate change, Schewe et al. (2013) estimated that approximately 8% of the global population would see a severe reduction in water resources (a reduction in runoff either greater than 20% or more than the standard deviation of current annual runoff) with a 1oC rise in global mean temperature (compared to the 1990s), rising to 14% at 2oC and 17% at 3oC; the spread across climate and hydrological models was, however, large."
"The SREX report (Seneviratne et al., 2012) recognized that projected increases in temperature and heavy precipitation imply regional-scale changes in flood frequency and intensity, but with low confidence because these projections were obtained from a single GCM. Global flood projections based on multiple CMIP5 GCM simulations coupled with global hydrology and land surface models (Dankers et al., PNAS, 2013; Hirabayashi et al., 2013) show flood hazards increasing over about half of the globe, but with great variability at the catchment scale."
In a global model intercomparison study (Davie et al., in press), two out of four models projected stronger increases and, respectively, weaker decreases in runoff when considering CO2 effects compared to simulations with constant CO2 concentration (consistent with above findings, though magnitudes differed between the models), but two other models showed the reverse."
"Changes in vegetation coverage and structure due to long-term climate change or shorter-term extreme events such as droughts (Anderegg et al., 2013) also affect the partitioning of precipitation into evapotranspiration and runoff, sometimes involving complex feedbacks with the atmosphere such as in the Amazon region (Port et al., 2012; Saatchi et al., 2013). One model in the study by Davie et al. (in press) showed regionally diverse climate change effects on vegetation distribution and structure, which had a much weaker effect on global runoff than the structural and physiological CO2 effects."
"The number of people with significantly decreased access to renewable groundwater resources is projected to be roughly 50% higher under RCP8.5 than under RCP2.6 (Portmann et al., 2013). The land area affected by decreases of groundwater resources increases linearly."
"The percentage of projected global population (SSP2 population scenario) that will suffer from a decrease of renewable groundwater resources of more than 10% between the 1980s and the 2080s was computed to range from 24% (mean based on five GCMs, range 11-39%) for RCP2.6 to 38% (range 27-50%) for RCP8.5 (Portmann et al., 2013; Table 3-2). The land area affected by decreases of groundwater resources increases linearly with global mean temperature rise between 0°C and 3°C. For each degree of global mean temperature rise, anadditional 4% of the global land area is projected to suffer a groundwater resources decrease of more than 30%, and an additional 1% to suffer a decrease of more than 70% (Portmann et al., 2013)."
"Using seven global hydrological models but a limited set of CMIP5 projections, Wada et al. (2013) suggested a global increase in irrigation demand by the 2080s (ensemble average 7–21% depending on emissions scenario), with a pronounced regional pattern, a large inter-model spread, and possible seasonal shifts in crop water demand and consumption."
"In general, future irrigation demand is projected to exceed local water availability in many places (Wada et al., 2013)."
Figure 3-4: Percentage change of mean annual streamflow for a global mean temperature rise of 2°C above 1980–2010 (2.7°C above pre-industrial). Color hues show the multi-model mean change across 4 GCMs and 11 global hydrological models (GHMs), and saturation shows the agreement on the sign of change across all 55 GHM-GCM combinations (percentage of model runs agreeing on the sign of change) (Schewe et al., 2013).]
"Each degree of global warming (up to 2.7°C above pre-industrial levels; Schewe et al., 2013) is projected to decrease renewable water resources by at least 20% for an additional 7% of the world population. The number of people with significantly decreased access to renewable groundwater resources is projected to be roughly 50% higher under RCP8.5 than under RCP2.6 (Portmann et al., 2013)."
The percentage of projected global population (SSP2 population scenario) that will suffer from a decrease of renewable groundwater resources of more than 10% between the 1980s and the 2080s was computed to range from 24% (mean based on five GCMs, range 11-39%) for RCP2.6 to 38% (range 27-50%) for RCP8.5 (Portmann et al., 2013; Table 3-2). The land area affected by decreases of groundwater resources increases linearly with global mean temperature rise between 0°C and 3°C. For each degree of global mean temperature rise, an additional 4% of the global land area is projected to suffer a groundwater resources decrease of more than 30%, and an additional 1% to suffer a decrease of more than 70% (Portmann et al., 2013).
Chapter 4. Terrestrial Ecosystems and Inland Water Systems
"CO2 effects are a first-order influence on model projections of ecosystem and hydrological responses to anthropogenic climate change (Sitch et al., 2008; Lapola et al., 2009; Friend et al., 2013)."
Chapter 5. Coastal Systems and Low-Lying Areas
"Upgrading coastal defenses and nourishing beaches would reduce these impacts roughly by three orders of magnitude. Hinkel et al. PNAS (2013) estimate the number of people flooded annually in 2100 to reach 170 Fto 260 million per year in 2100 without upgrading protection and two orders of magnitude smaller with dike (levee) upgrades, if GMSL rises 0.6 to 1.3 m by 2100."
Hinkel et al. (2013) report that stabilizing emissions at 450 ppm-CO2-eq reduces the average number of people flooded in 2100 by about 30% compared to a baseline where emissions increase to about 25 Gt C-eq in 2100.
The available global studies show that it is economically rational to protect large parts of the world's coastline during the 21st century against sea level rise impacts of increased coastal flood damage and land loss (Nicholls and Tol, 2006; Anthoff et al., 2010; Hinkel et al., 2013; high agreement, limited evidence).
For coastal flooding, an application of DIVA shows that for 21st century GMSL rise scenarios of 60-126 cm, the global costs of protection through dikes (levees) are much lower than the costs of damages avoided through adaptation (Hinkel et al., 2013).
For coastal flooding, annual damage and protection costs are projected to amount to several percentages of the national GDP for small island states such as Kiribati, the Solomon Islands, Vanuatu and Tuvalu under GMSL projections of 0.6-1.3 m by 2100 (Hinkel et al., 2013).
Chapter 7. Food Security and Food Production Systems
"A recent global crop model intercomparison for rice, wheat and maize shows similar results to those presented here, although with less impacts on temperate rice yields (Rosenzweig et al, 2013). That study also showed that crop models without explicit nitrogen stress fail to capture the expected response. Quantitative assessments of yield changes can be found in section 7.4. Across the globe, regional variability, which has not been summarised in meta-analyses except in contributing to the spread of data (Figure 7-4), will be important in determining how climate change affects particular agricultural systems."
"One lesson from recent model intercomparison experiments (Nelson et al., 2013) is that the choice of economic model matters at least as much as the climate or crop model for determining price response to climate change, indicating the critical role of economic uncertainties for projecting the magnitude of price impacts."
"Changes in the interannual variability of yields could potentially affect stability of food availability and access. Figure 7-6 shows projected changes in the coefficient of variation (CV) of yield from some of the few studies that publish this information. The data shown are consistent with reports of CV elsewhere: Müller et al. (2013) conducted gridded simulations across the globe and reported an increase of more than 5% in CV in 64% grid cells, and a decrease of more than 5% in 29% of cases. Increases in CV can be due to reductions in mean yields and/or increases in standard deviation of yields, and often simulated changes are a combination of the two. Overall, climate change will increase crop yield variability in many regions (medium evidence, medium agreement)."
Chapter 19. Emergent Risks and Key Vulnerabilities
"There are also efforts to coordinate impacts assessments adopting identical future climatic and/or socio-economic scenarios at various spatial scales (Parry et al., 2004; Piontek et al., 2013). Areas of compound risk identified by overlaying spatial data of impacts in multiple sectors can be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies (Piontek et al., 2013)."
