A modelling framework to analyze climate change effects on radionuclide aquifer contamination

Predicting the joint effects of future climate and land use change on ecosystem health in the Middle Reaches of the Yangtze River Economic Belt, China

<p>Future environmental changes will strongly affect the occurrence of rainfall-induced landslides in mountainous regions. In our ongoing study, we focus on the effects of climate changes as well as land use and land cover (LULC) changes on shallow slope failures in the Pyrenees. For this reason, a physically-based susceptibility model was developed, which calculates the landslide susceptibility at regional scale. The model merges two different approaches for the calculation of pore fluid pressure and also includes the option of defining the values of input parameters stochastically.</p><p>The model was validated using landslide inventories from two different study areas located in the Central and Eastern Pyrenees. One is the inventory of historic shallow slides and debris flows in Andorra country. The other one is the inventory of the catastrophic landslide episode in Val d’Aran area in June 2013, which includes 393 landslide initiation points. The susceptibility modelling of these two validation cases produced acceptable results and showed that our physically-based model is producing consistent stability conditions.</p><p>In the next step, the future LULC and climate changes until the end of the 21th century were simulated for Val d’Aran study area. The LULC changes were determined with the IDRISI TerrSet software suite, while the climate changes were obtained from the ensemble of regional climate models using RCP 4.5 and 8.5 scenarios. The results of the susceptibility modelling showed that the impacts of future LULC changes increase the overall stability because of the larger area of forest and shrubs (and consequently higher cohesion due to root strength). In contrast, the impact of future climate changes, which was principally incorporated by higher rainfall intensity, reduced the overall slope stability. However, when we compared the impacts of both future changes, the results showed that the influence of the vegetation expansion is more important than the effect of higher rainfall intensity. Therefore, the overall stability conditions in the study area seem to slightly improve in the future.</p><p>As always in such studies, there are many uncertainties in the input data and additional simulations are necessary to confirm the observed trends. Nonetheless, the outcomes provide helpful information for researchers and practitioners that deal with the impacts of future changes on landslide susceptibility in mountainous regions.</p>

ABSTRACTProviding nutritious and environmentally sustainable food to all people at all times is one of the greatest challenges currently facing society. This problem is particularly acute in Africa where an estimated one in four people still lack adequate food to sustain an active and healthy life. In this study, we consider the potential impact of future population growth and climate change on food security in Africa, looking ahead to 2050. A modelling framework termed FEEDME (Food Estimation and Export for Diet and Malnutrition Evaluation) was used which was characterized to model the impacts of future climate changes (utilizing the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios projections) and projected population growth on food availability and subsequent undernourishment prevalence in 44 African countries. Our results indicate that projected rapid population growth will be the leading cause of food insecurity and widespread undernourishment across Africa. Very little to no difference in undernourishment projections were found when we examined future scenarios with and without the effects of climate change, suggesting population growth is the dominant driver of change. Various adaptation options are discussed, such as closing the yield gap via sustainable intensification and increasing imports through trade and aid agreements. These strategies are likely to be critical in preventing catastrophic future food insecurity.

In the face of escalating global warming and intensified human activities, it is crucial to quantitatively assess the combined impacts of future climate change (CC) and land use change (LUCC) on runoff. This study employed simulation results of future CC and LUCC in the Min-Tuo River Basin, utilizing the CMIP6 and CA-Markov models in conjunction with the SWAT model to project runoff changes under various scenarios. The findings indicate an anticipated increase in both precipitation and average temperature in the future. Projected LUCC involves a reduction in arable land and grassland, alongside expansion of other land cover types. Changes in basin runoff are predominantly influenced by precipitation, with a higher likelihood of extreme events as CO2 emissions increase. Across four emission scenarios, the impact of future CC on basin runoff varies from −5.21% to +6.09%, while future LUCC's contribution ranges from +0.05% to +0.07%. When both factors are considered, the overall trend indicates a decrease in future runoff changes, ranging from −0.27% to +0.17%. These findings underscore the greater influence of CC on runoff compared to LUCC, thereby providing a scientific foundation for ecological conservation and water resources management in the basin.

Introduction: Climate change is expected to increase the burden of waterborne acute gastrointestinal illness (AGI) due to the increased frequency and intensity of extreme precipitation events. Here we investigate the relationship between extreme precipitation and parasitic AGI and to project the impact of climate change on these illnesses. Methods: We included reported cryptosporidiosis and giardiasis cases served by a municipal surface drinking water system (DWS) in Canada from 2000-2009. The association between weekly cases and modeled extreme precipitation (>90th percentile) was assessed (up to 6 week lags), using distributed lag non-linear Poisson regression models adjusted for seasonality (in lieu of temperature), secular trend, preceding dry/wet period and holiday effects. Using the best fitting model, the mean annual case counts were predicted for 2010-2069 using downscaled precipitation projections from 10 global climate models under the representative concentration pathway 8.5. Results: Including 5738 cases, a significant increase in cryptosporidiosis and giardiasis 5-6 weeks after extreme precipitation was found during the study period 2000-2009. A greater effect was evident during the rainy season (RR, 95% CI: 1.17, 1.08-1.32 in lag 5; 1.34, 1.11-1.59 in lag 6) than the dry season (RR, 95% CI: 1.09, 1.02-1.26 in lag 5; 1.17, 1.01-1.39 in lag 6). By the 2060s, climate models indicate decrease in average weekly and extreme precipitation during dry seasons, and increase in rainy seasons compared to 2000-2009. This increases the annual disease burden by 10%-14% (ensemble mean 11%), mainly in the rainy season. Discussion: We present a modeling framework to study the impact of extreme weather on waterborne AGI and support the hypothesis that increases in extreme precipitation may increase the burden of these AGI in future. These results show the need for increasing the adaptive capacity of vulnerable DWS through standardized infrastructure.

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 77:241-248 (2019) - DOI: https://doi.org/10.3354/cr01556 Impacts of future climate change on species richness of land vertebrates and critical areas in South Korea Ju-Hyun Lee1, Hee-Jin Kang1, Ha-Cheol Sung2,* 1School of Biological Sciences and Biotechnology, and 2Research Center of Ecomimetics and Department of Biological Sciences, Chonnam National University, 61186 Gwangju, South Korea *Corresponding author: shcol2002@jnu.ac.kr ABSTRACT: Climate change has extensive impacts on abundance, distribution, and conservation of species and ecosystems. The purpose of this study was to investigate impacts of future climate change on the species richness of land vertebrates, and to assess critical areas that might be continuously threatened by climate change in order to maintain high species richness in South Korea. The Climate Change Severity Index (CCSI) was calculated based on representative concentration pathway (RCP) scenarios to determine the climatic space (also known as comfort zone) of species and ecosystems under historic and future climate conditions. Regions with high species richness were then identified using survey results of species abundance and distribution. Finally, we identified critical areas with high CCSI values and high levels of species richness. These are areas where species and ecosystems are threatened the most by climate change. The number of critical areas is predicted to increase towards the late 2000s, and this increase is greater under RCP 8.5 than under RCP 4.5. Furthermore, the number of critical areas will increase more in coastal and wide plain areas than in inland mountainous areas. This study provides information useful for the conservation of species and their habitats in South Korea. It also indicates opportunities for launching protection measures and actions in highly vulnerable areas. KEY WORDS: Climate Change Severity Index · Species richness · Critical area · RCP scenarios Full text in pdf format PreviousNextCite this article as: Lee JH, Kang HJ, Sung HC (2019) Impacts of future climate change on species richness of land vertebrates and critical areas in South Korea. Clim Res 77:241-248. https://doi.org/10.3354/cr01556 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 77, No. 3. Online publication date: April 11, 2019 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2019 Inter-Research.

Assessments of impacts of future climate change on widely grown sugarcane varieties can guide decision‐making and help ensure the economic stability of numerous rural households. This study assessed the potential impact of future climatic change on sugarcane grown under dryland conditions in Mexico and identified key climate factors influencing yield. The Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model was used to simulate sugarcane growth and yield under current and future climate conditions. Management, soil and climate data from farm sites in Jalisco (Pacific Mexico) and San Luis Potosi (Northeastern Mexico) were used to simulate baseline yields. Baseline climate was developed with 30‐year historical data from weather stations close to the sites. Future climate for three decadal periods (2021–2050) was constructed by adding forecasted climate values from downscaled outputs of global circulation models to baseline values. Climate change impacts were assessed by comparing baseline yields with those in future decades under the A2 scenario. Results indicate positive impacts of future climate change on sugarcane yields in the two regions, with increases of 1%–13% (0.6–8.0 Mg/ha). As seen in the multiple correlation analysis, evapotranspiration explains 77% of the future sugarcane yield in the Pacific Region, while evapotranspiration and number of water and temperature stress days account for 97% of the future yield in the Northeastern Region. The midsummer drought (canicula) in the Pacific Region is expected to be more intense and will reduce above‐ground biomass by 5%–13% (0.5–1.7 Mg/ha) in July–August. Harvest may be advanced by 1–2 months in the two regions to achieve increases in yield and avoid early flowering that could cause sucrose loss of 0.49 Mg ha−1 month−1. Integrating the simulation of pest and diseases under climate change in crop modelling may help fine‐tune yield forecasting.

International audience

Projected changes in extreme precipitation at sub-daily and daily time scales

Quantitatively projecting the impact of future climate change on the socio-economy and exploring its internal mechanism are of great practical significance to adapt to climate change and prevent climate risks. Based on the economy-climate (C-D-C) model, this paper introduces a yield impact of climate change (YICC) model that can quantitatively project the climate change impact. The model is based on the YICC as its core concept and uses the impact ratio of climate change (IRCC) indicator to assess the response of the economic system to climate change over a long period of time. The YICC is defined as the difference between the economic output under changing climate condition and that under assumed invariant climate condition. The IRCC not only reflects the sensitivity of economic output to climate change but also reveals the mechanism of the nonlinear interaction between climate change and non-climatic factors on the socio-economic system. Using the main grain-producing areas in China as a case study, we use the data of the ensemble average of 5 GCMs in CMIP6 to project the possible impact of climate change on grain production in the next 15–30 years under three future scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5). The results indicate that the long-term climate change in the future will have a restraining effect on production in North region and enhance production in South region. From 2021 to 2035, climate change will reduce production by 0.60–2.09% in North region, and increase production by 1.80–9.01% in South region under three future scenarios. From 2021 to 2050, compared with the climate change impact in 2021–2035, the negative impact of climate change on production in North region will weaken, and the positive impact on production in South region will enhance with the increase in emission concentration. Among them, climate change will reduce grain output in North region by 0.52–1.99%, and increase output in South region by 1.35–9.56% under the three future scenarios. The combination of economic results and climate change research is expected to provide scientific support for further revealing the economic mechanism of climate change impacts.

Communicating Uncertainty about Climate Change: The Scientists’ Dilemma

ABSTRACTNorth China faces increasing risks from extreme precipitation under climate change, yet projections integrating socio‐economic dynamics with high‐resolution climate models remain limited. Leveraging the latest version of the NEX‐GDDP‐CMIP6 (NASA Earth Exchange Global Daily Downscaled Projections) from NASA (National Aeronautics and Space Administration) and CMIP6 (Coupled Model Intercomparison Project Phase 6) datasets across SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, this study quantifies future extreme precipitation impacts on urban populations and cropland in two critical periods: 2031–2050 (mid‐century) and 2081–2100 (end‐century). Through the Multivariable Integrated Evaluation Tool (MVIETool), we demonstrate that NEX‐GDDP‐CMIP6 reduces regional precipitation biases by 79% compared to CMIP6 (from +133.16 mm/day to −27.00 mm/day), despite persistent uncertainties in extreme intensity indices. Projections reveal a pronounced intensification of extreme precipitation, with R99p (extremely wet day precipitation) increasing by 127%–131% and CDD (consecutive dry days) decreasing by 12%–17% in 2081–2100 under SSP5‐8.5, signalling a transition toward wetter conditions. Exposure analyses indicate that 38.24 million citizens (26.32% of the urban population) and 49,900 km2 cropland (5.87% of the area) in North China may face record‐breaking precipitation events by the end of the century under SSP5‐8.5, primarily concentrated in coastal megacities and the North China Plain. These findings underscore the urgency of scenario‐specific adaptation strategies, including ‘sponge city’ retrofitting in high‐exposure zones and precision agriculture tailored to precipitation regime shifts. Our integrated framework advances regional climate risk assessments by reconciling dynamical downscaling limitations with SSP‐driven socio‐economic uncertainties.

Prolonged impacts of extreme precipitation events weakened annual ecosystem CO2 sink strength in a coastal wetland

Economics and risk of adaptation options in the Australian cotton industry

The intensification of climate change and the implementation of territorial spatial planning policies have jointly increased the complexity of future carbon storage changes. However, the impact of territorial spatial planning on carbon storage under future climate change remains unclear. Therefore, this study aims to reveal the potential impacts of future climate change and territorial spatial planning on carbon storage and sequestration, providing decision support for addressing climate change and optimizing territorial spatial planning. We employed the FLUS model, the InVEST model, and the variance partitioning analysis (VPA) method to simulate carbon storage under 15 different scenarios that combine climate change scenarios and territorial spatial planning for Xiamen in 2035, and to quantify the individual and combined impacts of territorial spatial planning and climate change on ecosystem carbon sequestration. The results showed that (1) by 2035, Xiamen’s carbon storage capacity is expected to range from 32.66 × 106 Mg to 33.00 × 106 Mg under various scenarios, reflecting a decrease from 2020 levels; (2) the implementation of territorial spatial planning is conducive to preserving Xiamen’s carbon storage, with the urban development boundary proving to be the most effective; (3) carbon storage is greatly affected by climate change, with RCP 4.5 more effective than RCP 8.5 in maintaining higher levels of carbon storage; and (4) the influence of territorial spatial planning on carbon sequestration consistently exceeds that of climate change, particularly under high-emission scenarios, where the regulatory effect of planning is especially significant.