Explore the vast range of titles available.

We project climate change induced changes in fluvial flood risks for six global warming levels between 1.5 and 4 °C by 2100, focusing on the major river basins of six countries. Daily time series of precipitation, temperature and monthly potential evapotranspiration were generated by combining monthly observations, daily reanalysis data and projected changes in the five CMIP5 GCMs also selected in the ISI-MIP fast track project. These series were then used to drive the HBV hydrological model and the CaMa-Flood hydrodynamic model to simulate river discharge and flood inundation. Our results indicate that return periods of 1 in 100-year floods in the late twentieth century (Q100-20C) are likely to decrease with warming. At 1.5 °C warming, 47%, 66%, 27%, 65%, 62% and 92% of the major basin areas in Brazil, China, Egypt, Ethiopia, Ghana and India respectively experience a decrease in the return period of Q100-20C, increasing to 54%, 81%, 28%, 82%, 86% and 96% with 4 °C warming. The decrease in return periods leads to increased number of people exposed to flood risks, particularly with 4 °C warming, where exposure in the major river basin areas in the six countries increases significantly, ranging from a doubling (China) to more than 50-fold (Egypt). Limiting warming to 1.5 °C would avoid much of these increased risks, resulting in increases ranging from 12 to 1266% for the 6 countries.

Floods are among the most frequent and costliest natural hazards. Fluvial flood losses are expected to increase in the future, driven by population and economic growth in flood-prone areas, and exacerbated in many regions by effects of climate change on the hydrological cycle. Yet, studies assessing direct and indirect economic impacts of fluvial flooding in combination with climate change and socio-economic projections at a country level are rare. This study presents an integrated flood risk analysis framework to calculate total (direct and indirect) economic damages, with and without socio-economic development, under a range of warming levels from < 1.5 to 4 °C in Brazil, China, India, Egypt, Ethiopia, and Ghana. Direct damages are estimated by linking spatially explicit daily flood hazard data from the Catchment-based Macro-scale Floodplain (CaMa-Flood) model with country- and sector-specific depth-damage functions. These values input into an economic Input-Output model for the estimation of indirect losses. The study highlights that total fluvial flood losses are largest in China and India when expressed in absolute terms. When expressed as a share of national GDP, Egypt faces the largest total losses under both the climate change and climate change plus socio-economic development experiments. The magnitude of indirect losses also increased significantly when socio-economic development was modelled. The study highlights the importance of including socio-economic development when estimating direct and indirect flood losses, as well as the role of recovery dynamics, essential to provide a more comprehensive picture of potential losses that will be important for decision makers.

The potential impact of climate change on agriculture has been one of the most discussed topics in the literature on climate change. Although the possible impacts of climate change on crop yields have been widely studied, there remains little quantitative understanding of the heterogeneous economic responses to climate-induced crop yield changes in different economies, particularly at higher levels of warming. This study assesses the economic impacts of eight scenarios of warming, from 1.5 to 4 °C, on rice and wheat yields in China, India, Brazil, Egypt, Ghana and Ethiopia. The role of both natural and social factors in crop production is considered by coupling a statistical crop model (ClimaCrop) and a global economic model (GTAP). Changes in economic outputs, consumer and producer prices and national economic welfare are presented. The study shows marginal benefits of crop yield changes on GDP and welfare in China up to 3.5 and 3.0 °C, respectively. This is due to projected increases in rice yields which lower domestic consumer rice prices. Although at higher warming levels these trends begin to reverse. The other countries are negatively impacted due to declining crop yields, with increasing consumer prices of domestic and imported rice and wheat. GDP and welfare declines, with more severe reductions associated with the higher warming levels, particularly in India and Ethiopia. The method is beneficial as the economic outputs reflect a more in-depth picture of the response of global markets and ultimately regional consequences of agricultural impacts that will be of importance to decision makers.

We present the results from a new framework providing an assessment of how climate change risks to natural capital accrue with warming of 1.5–4 °C in six countries (China, Brazil, Egypt, Ethiopia, Ghana, and India). Unlike typical biodiversity and climate change studies, this assessment also considers landcover and population changes across a range of 17 ecosystem services. The potential impacts of climate change (alone) on natural capital at 1.5 °C is greatest in Brazil and least in Ghana. However, when population and landcover change are included, areas projected to be at high natural capital risk begin to accrue by 1.5 °C in all countries. By 2 °C, Ethiopia and Ghana show increasing areas at high risk, even though they are at low risk owing to climate alone. Thus, current impacts to biodiversity and ecosystem services and changes in potential demand coupled with warming exceed changes projected by climate alone. However, this also indicates that there is adaptation potential, especially with warming of < 2 °C, to reduce risk through restoring habitat. At lower levels of warming, targeted restoration of marginal agricultural habitats would increase the bank of natural capital for use by people and provide support for remaining agricultural lands. By 3 °C, the adaptation potential from restoration is substantially less: < 1% in Brazil, India and Egypt; 7–8% in China and Ethiopia; but still 26% in Ghana. This indicates that restoration as an adaptation option for biodiversity, and thus, natural capital, rapidly decreases with increasing temperatures. By 2100, factoring in population change (SSP2), current ecological footprint, and current landcover, even with only 1.5 °C warming, large parts of Brazil, eastern China, most of Egypt, much of Ethiopia, southwestern Ghana (except for protected areas), and most of India are at high to extreme natural capital risk with an adaptation deficit potentially equating to a soft adaptation limit.

Abstract We quantify the projected impacts of alternative levels of global warming upon the probability and length of severe drought in six countries (China, Brazil, Egypt, Ethiopia, Ghana and India). This includes an examination of different land cover classes, and a calculation of the proportion of population in 2100 (SSP2) at exposed to severe drought lasting longer than one year. Current pledges for climate change mitigation, which are projected to still result in global warming levels of 3 °C or more, would impact all of the countries in this study. For example, with 3 °C warming, more than 50% of the agricultural area in each country is projected to be exposed to severe droughts of longer than one year in a 30-year period. Using standard population projections, it is estimated that 80%-100% of the population in Brazil, China, Egypt, Ethiopia and Ghana (and nearly 50% of the population of India) are projected to be exposed to a severe drought lasting one year or longer in a 30-year period. In contrast, we find that meeting the long-term temperature goal of the Paris Agreement, that is limiting warming to 1.5 °C above pre-industrial levels, is projected to greatly benefit all of the countries in this study, greatly reducing exposure to severe drought for large percentages of the population and in all major land cover classes, with Egypt potentially benefiting the most.

Flooding represents the greatest natural threat to the UK, presenting severe risk to populations along coastlines and floodplains through extreme tidal surge and hydrometeorological events. Climate change is projected to significantly elevate flood risk through increased severity and frequency of occurrences, which will be exacerbated by external drivers of risk such as property development and population growth throughout floodplains. This investigation explores the entire flood hazard modelling chain, utilising the nonparametric bias correction of UKCP18 regional climate projections, the distributed HBV-TYN hydrological model and HEC-RAS hydraulic model to assess future manifestation of flood hazard within the Broadland Catchment, UK. When assessing the independent impact of extreme river discharge and storm surge events as well as the impact of a compound event of the two along a high emission scenario, exponential increases in hazard extent over time were observed. The flood extent increases from 197 km 2 in 1990 to 200 km 2 in 2030, and 208 km 2 in 2070. In parallel, exponential population exposure increases were found from 13,917 (1990) to 14,088 (2030) to 18,785 (2070). This methodology could see integration into policy-based flood risk management by use of the developed hazard modelling tool for future planning and suitability of existing infrastructure at a catchment scale.

The UKCP18 Convection-permitting model (CPM) provides the latest high-resolution climate projections for the UK. Compared with regional climate model projections, the CPM projections are more capable of simulating small-scale atmospheric convection particularly during extreme weather events such as intense rainfall and localised storms. However, systematic biases still exist in these projections. To improve the reliability of these projections, bias correction is crucial. In this study, we apply and evaluate a quantile mapping (QM) bias correction method for UKCP18-CPM hourly precipitation (with diurnal correction) and daily temperature over England. We quantify how closely the bias corrected simulations align with observations relative to the raw simulations. The raw UKCP18-CPM simulations exhibit wet precipitation biases, particularly in northern England, with annual mean biases ranging from 4.6 % to 18.3 %, and cool temperature biases, with annual mean biases from -0.87 to 0.02 °C. Bias correction substantially improved agreement with observational datasets, increasing R.sup.2 values for the 95th percentile of hourly precipitation from 0.80-0.88 to 0.98 and achieving near-perfect alignment (R.sup.2 = 1) for temperature extremes. Future projections for the 2070s indicate notable increases in annual maximum precipitation by 25.1 %-39.1 % and mean daily temperature by 3.1 to 4.5 °C, highlighting the potential for more intense climate-related events. Overall, the applied bias-correction method brings UKCP18-CPM simulations into closer agreement with observations for both mean behaviour and extremes, providing a more reliable basis for high-resolution impact modelling and assessments that require hourly precipitation forcing.

The Paris Agreement aims to constrain global warming to ‘well below 2 °C’ and to ‘pursue efforts’ to limit it to 1.5 °C above pre-industrial levels. We quantify global and regional risk-related metrics associated with these levels of warming that capture climate change–related changes in exposure to water scarcity and heat stress, vector-borne disease, coastal and fluvial flooding and projected impacts on agriculture and the economy, allowing for uncertainties in regional climate projection. Risk-related metrics associated with 2 °C warming, depending on sector, are reduced by 10–44% globally if warming is further reduced to 1.5 °C. Comparing with a baseline in which warming of 3.66 °C occurs by 2100, constraining warming to 1.5 °C reduces these risk indicators globally by 32–85%, and constraining warming to 2 °C reduces them by 26–74%. In percentage terms, avoided risk is highest for fluvial flooding, drought, and heat stress, but in absolute terms risk reduction is greatest for drought. Although water stress decreases in some regions, it is often accompanied by additional exposure to flooding. The magnitude of the percentage of damage avoided is similar to that calculated for avoided global economic risk associated with these same climate change scenarios. We also identify West Africa, India and North America as hotspots of climate change risk in the future.

Biodiversity loss is expected to escalate with every increment of global warming. Simultaneously, land-intensive climate change mitigation strategies, such as afforestation and bioenergy, may further compound biodiversity loss. So far, the magnitude of these two drivers has not been compared in the context of temperature overshoot, meaning the temporary exceedance of a targeted global warming limit. By combining spatial data on climate refugia (areas sheltering biodiversity from climate change), bioenergy cropland, and forestation for multiple cost-effective scenarios with varying levels of climate action and overshoot, we illustrate how both warming and mitigation affect today’s climate refugia across five integrated assessment models. Decisive climate action, compatible with limiting warming to 1.5 °C, reduces the combined loss of today’s climate refugia due to warming and mitigation-related land-use change by more than 50% compared to current climate policies, outweighing potentially negative implications of mitigation at the global level by limiting the magnitude and duration of warming above 1.5 °C. We observe notable differences across regions and the considered model frameworks. Overshoot implications strongly depend on the underlying biodiversity recovery assumptions.

The United Kingdom Climate Projections 2018 (UKCP18) regional climate model (RCM) 12 km regional perturbed physics ensemble (UKCP18-RCM-PPE) is one of the three strands of the latest set of UK national climate projections produced by the UK Met Office. It has been widely adopted in climate impact assessment. In this study, we report biases in the raw UKCP18-RCM simulations that are significant and are likely to deteriorate impact assessments if they are not adjusted. Two methods were used to bias-correct UKCP18-RCM: non-parametric quantile mapping using empirical quantiles and a variant developed for the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) designed to preserve the climate change signal. Specifically, daily temperature and precipitation simulations for 1981 to 2080 were adjusted for the 12 ensemble members. Potential evapotranspiration was also estimated over the same period using the Penman–Monteith formulation and then bias-corrected using the latter method. Both methods successfully corrected biases in a range of daily temperature, precipitation, and potential evapotranspiration metrics and reduced biases in multi-day precipitation metrics to a lesser degree. An exploratory analysis of the projected future changes confirms the expectation of wetter, warmer winters and hotter, drier summers and shows uneven changes in different parts of the distributions of both temperature and precipitation. Both bias-correction methods preserved the climate change signal almost equally well, as well as the spread among the projected changes. The change factor method was used as a benchmark for precipitation, and we show that it fails to capture changes in a range of variables, making it inadequate for most impact assessments. By comparing the differences between the two bias-correction methods and within the 12 ensemble members, we show that the uncertainty in future precipitation and temperature changes stemming from the climate model parameterization far outweighs the uncertainty introduced by selecting one of these two bias-correction methods. We conclude by providing guidance on the use of the bias-corrected datasets. The datasets bias-adjusted with ISIMIP3BA are publicly available in the following repositories: https://doi.org/10.5281/zenodo.6337381 for precipitation and temperature (Reyniers et al., 2022a) and https://doi.org/10.5281/zenodo.6320707 for potential evapotranspiration (Reyniers et al., 2022b). The datasets, bias-corrected using the quantile mapping method, are available at https://doi.org/10.5281/zenodo.8223024 (Zha et al., 2023).