Impact Of Future Climate Change Research Articles

Risk-based versus storyline approaches for global warming impact assessment on basin-averaged extreme rainfall: a case study for Typhoon Hagibis in eastern Japan

Two methods exist to address the degree to which past extreme events and associated disasters will be intensified due to climate change: storyline approaches and risk-based approaches. However, the risk-based approach applied to weather similar to the target event (typhoons, a stationary weather front,…etc) becomes theoretically similar to the storyline approach. We examine this theory for the climate change impact of a real event, Typhoon Hagibis, which caused devastating flood damage to eastern Japan in 2019, while focusing on basin-averaged accumulated rainfall (BAAR) in major eastern river basins. A risk-based approach was conducted to determine the future change of BAAR by calculating the quantile change corresponding to Hagibis from the probability distribution of typhoon-induced events in a large ensemble climate simulation dataset database for Policy Decision-making for Future climate change (past, +2K and +4K future climates). A storyline approach for Typhoon Hagibis was realized using a pseudo global warming (PGW) experiment with a 5 km non-hydrostatic model. The projected BAAR in the two approaches were consistent for all target basins, supporting the robustness of the calculated changes in extreme catchment precipitation. This presents an important practical benefit: one can assess future climate change impact on a past symbolic event using either PGW experiments or large ensemble climate projections for the target weather.

Trivial gain of downscaling in future projections of higher trophic levels in the Nordic and Barents Seas

AbstractDownscaling physical forcing from global climate models is both time consuming and labor demanding and can delay or limit the physical forcing available for regional marine ecosystem modelers. Earlier studies have shown that downscaled physics is necessary for capturing the dynamics of primary production and lower trophic levels; however, it is not clear how higher trophic levels respond to the coarse resolution physics of global models. Here, we apply the Nordic and Barents Seas Atlantis ecosystem model (NoBa) to study the consequences of using physical forcing from global climate models versus using that from regional models. The study is therefore (i) a comparison between a regional model and its driving global model to investigate the extent to which a global climate model can be used for regional ecosystem predictions and (ii) a study of the impact of future climate change in the Nordic and Barents Seas. We found that few higher trophic level species were affected by using forcing from a global versus a regional model, and there was a general agreement in future biomass trends and distribution patterns. However, the slight difference in temperature between the models dramatically impacted Northeast Arctic cod (Gadus morhua), which highlights how species projection uncertainty could arise from poor physical representation of the physical forcing, in addition to uncertainty in the ecosystem model parameterization.

Revealing the long-term trend of the global-scale Ginkgo biloba distribution and the impact of future climate change based on the ensemble modeling

Revealing the long-term trend of the global-scale Ginkgo biloba distribution and the impact of future climate change based on the ensemble modeling

Predicting the potential distribution of wheatear birds using stacked generalization-based ensembles

Habitat suitability models, usually referred to as species distribution models (SDMs), are widely applied in ecology for many purposes, including species conservation, habitat discovery, and gain evolutionary insights by estimating the distribution of species. Machine learning algorithms as well as statistical models have been recently used to predict the distribution of species. However, they seemed to have some limitations due to the data and the models used. Therefore, this study proposes a novel approach for assessing habitat suitability based on ensemble learning techniques. Three heterogeneous ensembles were built using the stacked generalization method to model the distribution of four wheatear species (Oenanthe deserti, Oenanthe leucopyga, Oenanthe leucura, and Oenanthe oenanthe) located in Morocco. Initially, a set of base-learners were constructed by virtue of training for each specie's dataset six machine learning algorithms (Multi-Layer Perceptron (MLP), Support Vector Classifier (SVC), K-nearest neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GB), and Random Forest (RF)). Then, the predictions of these base learners were fed as training data to train three meta-learners (Logistic Regression (LR), SVC, and MLP). To evaluate and assess the performance of the proposed approaches, we used: (1) six performance criteria (accuracy, recall, precision, F1-score, AUC, and TSS), (2) Borda Count (BC) ranking method based on multiple criteria to rank the best-performing models, and (3) Scott Knott (SK) test to statistically compare the performance of the presented models. The results based on the six-evaluation metrics showed that stacked ensembles outperformed their singles in all species datasets, and the stacked model with SVC as a meta-learner outperformed the other two ensembles. The results showed the potential of using ensemble learning techniques to model species distribution and recommend the use of the stacked generalization technique as a combination strategy since it gave better results compared to single models in four wheatear species datasets. Moreover, to assess the impact of future climate changes on the distribution of the four wheatear species, the best-performing distribution model was selected and projected into the current and future climatic conditions. The distributions of the Moroccan wheatear birds were found to be slightly affected by future climate changes.

Adaptation Strategies Strongly Reduce the Future Impacts of Climate Change on Simulated Crop Yields

AbstractSimulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields.

Impact of Spatial Soil Variability on Rainfed Maize Yield in Kansas under a Changing Climate

As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten maize-producing counties. A cropping system model, CERES-Maize of Decision Support System for Agrotechnology Transfer (DSSAT) was calibrated using observed maize yield. To account for the spatial variability of soils, the gSSURGO soil database was used. The model was run for a baseline and future climate change scenarios under two Representative Concentration Pathways (RCP4.5 and RCP8.5) to assess the impact of future climate change on rainfed maize yield. The simulation results showed that maize yield was impacted by spatial soil variability, and that using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county. The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss. Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively. The results indicate the uncertainties of growing maize in our study region under the changing climate, emphasizing the need for developing strategies to sustain maize production in the region.

Assessing the Implication of Climate Change to Forecast Future Flood Using SWAT and HEC-RAS Model under CMIP5 Climate Projection in Upper Nan Watershed, Thailand

Climate change will affect Southeast Asian countries, particularly Thailand. There are still insufficient studies on rainfall, streamflow, and future floods in the Upper Nan Watershed, northern Thailand. This study examined how future climate change will affect the rainfall, streamflow, and flooding in the Upper Nan Watershed. SWAT and HEC-RAS models were utilized to assess the future streamflow and flooding in this area. The models used data from 1980–2020, which were taken from seven Upper Nan meteorological stations and two discharge stations. In this study, the impact of future climate change was predicted using three GCMs, under RCP4.5 and RCP8.5 scenarios. The historical data analyzed in this study indicated that rainfall in the study area has a positive trend. Climate change will increase further, from 18% to 19%, which will cause more fluctuations and lead to wetter conditions, both in the wet and dry seasons. Climate change delayed the hydrograph peak and the SWAT-modelled streamflow in the N1 and N64 stations by between 0.3% and 5.1%. RCP8.5 inundated all of the stations more than RCP4.5. Our models showed that in the medium future (2041–2060), the inundated area will be similar to that during the 100-year flood probability. Thus, monitoring and preparation are necessary to avoid repeating the considerable 2011 flood losses in Thailand.

Responses of Runoff and Its Extremes to Climate Change in the Upper Catchment of the Heihe River Basin, China

Understanding the impact of climate change on runoff and its extremes is of great significance for water resource assessment and adaptation strategies, especially in water-scarce regions. This study aims to analyze the impact of future climate change on runoff and its extremes in the upper reaches of the Heihe River basin in northwest China. The projected runoff was derived using the Soil Water Assessment Tool with climate data from the CSIRO-MK-3-6-0 model under the scenario of RCP4.5, and a frequency analysis of runoff was performed by generalized extreme value distribution. The results indicate that, compared with the baseline period of 1961 to 2000, the minimum and maximum temperatures in the period 2031 to 2070 were predicted to increase by 2.5 °C on average. The precipitation in most months was also predicted to increase, with an average rise of 16.5%. The multi-year average runoff was projected to increase by 8%. The annual mean and extreme flows were also expected to rise under future climate change at different return periods, and the low flow was expected to increase the most.

Assessing Future Climate Change Impacts on Potato Yields — A Case Study for Prince Edward Island, Canada

Crop yields are adversely affected by climate change; therefore, it is crucial to develop climate adaptation strategies to mitigate the impacts of increasing climate variability on the agriculture system to ensure food security. As one of the largest potato-producing provinces in Canada, Prince Edward Island (PEI) has recently experienced significant instability in potato production. PEI's local farmers and stakeholders are extremely concerned about the prospects for the future of potato farming industries in the context of climate change. This study aims to use the Decision Support System for Agrotechnology Transfer (DSSAT) potato model to simulate future potato yields under the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios (including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The study evaluates the combined effects of changing climatic conditions at local scales (i.e., warming temperature and changing precipitation patterns) and increasing carbon dioxide (CO2) concentration in the atmosphere. The results indicate future significant declines in potato yield in PEI under the current farming practices. In particular, under the high-emission scenarios (e.g., SSP3-7.0 and SSP5-8.5), the potato yield in PEI would decline by 48% and 60% in the 2070s and by 63% and 80% by 2090s; even under the low-emission scenarios (i.e., SSP1-1.9 and SSP1-2.6), the potato yield in PEI would still decline by 6-10%. This implies that it is important to develop effective climate adaptation measures (e.g., adjusting farming practices and introducing supplemental irrigation plans) to ensure the long-term sustainability of potato production in PEI.

The Role of Crop Management Practices and Adaptation Options to Minimize the Impact of Climate Change on Maize (Zea mays L.) Production for Ethiopia

Climate change impact assessment along with adaptation measures are key for reducing the impact of climate change on crop production. The impact of current and future climate change on maize production was investigated, and the adaptation role of shifting planting dates, different levels of nitrogen fertilizer rates, and choice of maize cultivar as possible climate change adaptation strategies were assessed. The study was conducted in three environmentally contrasting sites in Ethiopia, namely: Ambo, Bako, and Melkassa. Future climate data were obtained from seven general circulation models (GCMs), namely: CanESM2, CNRM-CM5, CSIRO-MK3-6-0, EC-EARTH, HadGEM2-ES, IPSL-CM5A-MR, and MIROC5 for the highest representative concentration pathway (RCP 8.5). GCMs were bias-corrected at site level using a quantile-quantile mapping method. APSIM, AquaCrop, and DSSAT crop models were used to simulate the baseline (1995–2017) and 2030s (2021–2050) maize yields. The result indicated that the average monthly maximum air temperature in the 2030s could increase by 0.3–1.7 °C, 0.7–2.2 °C, and 0.8–1.8 °C in Ambo, Bako, and Melkassa, respectively. For the same sites, the projected increase in average monthly minimum air temperature was 0.6–1.7 °C, 0.8–2.3 °C, and 0.6–2.7 °C in that order. While monthly total precipitation for the Kiremt season (June to September) is projected to increase by up to 55% (365 mm) for Ambo and 75% (241 mm) for Bako respectively, whereas a significant decrease in monthly total precipitation is projected for Melkassa by 2030. Climate change would reduce maize yield by an average of 4% and 16% for Ambo and Melkassa respectively, while it would increase by 2% for Bako in 2030 if current maize cultivars were grown with the same crop management practice as the baseline under the future climate. At higher altitudes, early planting of maize cultivars between 15 May and 1 June would result in improved relative yields in the future climate. Fertilizer levels increment between 23 and 150 kg ha−1 would result in progressive improvement of yields for all maize cultivars when combined with early planting for Ambo. For a mid-altitude, planting after 15 May has either no or negative effect on maize yield. Early planting combined with a nitrogen fertilizer level of 23–100 kg ha−1 provided higher relative yields under the future climate. Delayed planting has a negative influence on maize production for Bako under the future climate. For lower altitudes, late planting would have lower relative yields compared to early planting. Higher fertilizer levels (100–150 kg ha−1) would reduce yield reductions under the future climate, but this varied among maize cultivars studied. Generally, the future climate is expected to have a negative impact on maize yield and changes in crop management practices can alleviate the impacts on yield.

The Trees of the Pisa Botanic Garden under Climate Change Scenarios: What Are We Walking into?

Botanical gardens are dynamic systems of high scientific, ecological, cultural, and historical value. They are irreplaceable places to cultivate, manage, study, and preserve tree diversity. One of the ongoing biggest challenges worldwide in managing and preserving trees is climate change, which exposes specimens to more stressful and severe environmental conditions, causing an increase in mortality. In our research, we aimed to assess the future climate change impact on the tree collection of Botanic Garden of Pisa in order to outline the expected consequences about tree cultivation and management. Under the most pessimistic scenario, in 2090 more than 60% of the tree species cultivated in the Botanic Garden of Pisa are expected to fall outside of their climatic niche. Future chances and pitfalls in managing urban green areas, focusing on the botanical garden community, are also explored.

Water-energy-food nexus of local bioeconomy hub and future climate change impact implication

Policies on bioeconomy are some of the key emerging ones around the world to support sustainability in terms of using renewable resources. However, these may lead to significant demand for biomass feedstock which relies on limited resources, viz., water, land, and energy. The impact of climate change on water availability makes it more challenging. This paper demonstrates the analysis of the water, energy, and food resource nexus of a bioeconomy-based industrial complex and future climate change impact. The model structure consists of cultivation, crop processing, and urban sectors. Sugarcane is the major crop in the case study area and is the main bioeconomy crop.The results indicate that the average sugarcane yield will be lower than the minimum industrial goal for the 2020s and 2030s decades. Consequently, sugarcane feedstock shortage may often occur. The drought situation tends to be increased, so groundwater resources can be the main reserve source of water. In all improvement scenarios on water supply management, nexus energy used for water acquisition was mainly for groundwater acquisition. Direct energy use in the cultivation process is high and fossil fuels based, while most of the energy used in processing came from biomass waste. This implies that technologies for water acquisition will be more important in the future and should be developed in terms of energy efficiency and the aspect of promoting the usage of renewable energy to reduce greenhouse gases and fossil fuels use. The impact of climate change will cause significant fluctuations in the annual rainfall characteristics of the study area. An additional irrigation water supply of about 250 mm and expansion to cover more than 90% of the cultivation area should be considered to secure the minimum supply of sugarcane for the local biorefinery complex in the following decades.

Habitat suitability mapping for a high-value non-timber forest product: A case study of <i>Rauvolfia serpentina</i>

Non-timber forest products (NTFPs) are essential for rural livelihoods but are often threatened by deforestation and overexploitation. Rauvolfia serpentina is a critical NTFP species; however, there have been no studies on the impact of future climate change on its potential habitat distribution. We used the Maxent modeling approach to address this knowledge gap and determine this species’ current and future suitability under climate change scenarios. The areas more suitable to its occurrence were mainly located near the southwestern coastal regions of Myanmar and India. Based on our predictions, these ‘very high’ suitability areas are likely to decrease in 2070 under both low and high CO2 emission scenarios, indicating the future vulnerability of this species’ habitat to the impact of climate change. This species is a highly valued NTFP species in the study region; therefore, such a reduction in habitat suitability may result in reductions in NTFP production and economic benefits for the dependent populations. Our findings can help define and conserve areas containing the natural habitats of this high-valued NTFP species.

Possible Future Climate Change Impacts on the Meteorological and Hydrological Drought Characteristics in the Jinghe River Basin, China

Revealing the impact of future climate change on the characteristics and evolutionary patterns of meteorological and hydrological droughts and exploring the joint distribution characteristics of their drought characteristics are essential for drought early warning in the basin. In this study, we considered the Jinghe River Basin in the Loess Plateau as the research object. The standardized precipitation index (SPI) and standardized runoff index (SRI) series were used to represent meteorological drought and hydrological drought with monthly runoff generated by the SWAT model. In addition, the evolution laws of the JRB in the future based on Copula functions are discussed. The results showed that: (1) the meteorological drought and hydrological drought of the JRB displayed complex periodic change trends of drought and flood succession, and the evolution laws of meteorological drought and hydrological drought under different spatiotemporal scales and different scenario differ significantly. (2) In terms of the spatial range, the JRB meteorological and hydrological drought duration and severity gradually increased along with the increase in the time scale. (3) Based on the joint distribution model of the Copula function, the future meteorological drought situation in the JRB will be alleviated when compared with the historical period on the seasonal scale, but the hydrological drought situation is more serious. The findings can help policy-makers explore the correlation between meteorological drought and hydrological drought in the background of future climate change, as well as the early warning of hydrological drought.

Climate Change Impacts on the Availability of Anti-malarial Plants in Kenya

In many rural East African areas, anti-malarial plants are commonly used as first-line treatment against malaria. However, spatially explicit information about the future availability of anti-malarial plant species and its relation to future suitable habitat for malaria vectors is limited. In this study we 1) model the distribution of anti-malarial plant and malaria vector species and assess the drivers of their distributions taking the example of the Samburu dryland in Kenya, 2) map the modeled overlap in this area, 3) assess the impact of future climate change on anti-malarial plant and malaria vector species and 4) report their future overlaps. Our results show that mean temperature of warmest quarter, precipitation of wettest quarter and mean temperature of coldest quarter were the most important environmental variables that affected the distribution of anti-malarial species. The effects of climate change will be detrimental, since most areas will witness huge losses in anti-malarial species habitat while only a few gained or remained stable under both SSP2-4.5 and SSP5-8.5 climate change scenarios by 2050s and 2070s. According to most of our scenarios, more than half of the anti-malarial species will become threatened by 2050s and 2070s. A comparison between distribution patterns of future anti-malarial species richness and malaria vector species suitable habitat suggests that the former will decrease considerably while the later will increase. Because the availability of anti-malarial species will decrease in the areas affected by malaria vectors, geographically targeted conservation strategies and further control measures against malaria vectors are all the more important.

Study on the Impact of Future Climate Change on Extreme Meteorological and Hydrological Elements in the Upper Reaches of the Minjiang River

Global warming increases global average precipitation and evaporation, causing extreme climate and hydrological events to occur frequently. Future changes in temperature, precipitation, and runoff from 2021 to 2050 in the upper reaches of the Minjiang River were analyzed using a distributed hydrological model, the SWAT (Soil and Water Assessment Tool), under a future climate scenario. Simultaneously, future variation characteristics of extreme climate hydrological elements in the upper reaches of the Minjiang River were analyzed using extreme climate and runoff indicators. The research shows that the frequency and intensity of the extreme temperature warming index will increase, while those of the extreme temperature cooling index will increase and then weaken in the upper reaches of the Minjiang River under a future climate scenario. The duration of precipitation, the intensity of continuous heavy precipitation, and the frequency of heavy precipitation will increase, whereas the intensity of short-term heavy precipitation and the frequency of heavy precipitation will decrease. However, spatial distribution of flood in the upper reaches is different, and thus flood risk in the upstream source area will still tend to increase. Particular attention should be given to the increase in autumn flood risk in the upper reaches of the Minjiang River.

Infectious disease ecology and evolution in a changing world.

Infectious disease ecology and evolution in a changing world.

Adaptive change of land use to nature and society in China’s agro-pastoral ecotone

Land use and cover change (LUCC), as an adaptation and confrontation of landowners to environmental changes, plays an essential role in supporting multiple goals of sustainable development. However, the driving process and prospects of LUCC remain unclear at the regional scale. Using remote sensing methods, trend analysis, binary logistic regression models and open-scenario-based forecasting, this study explored the spatio-temporal pattern and driving factors of LUCC in the agro-pastoral ecotone of northeast China (APENE) from 2001 to 2018, revealed potential risks in land-use patterns and key issues in current policies, and proposed targeted suggestions. Results showed that over the past two decades, the areas of forest and farmland increased by 67.55 % and by 31.64 % and that of grassland decreased by 14.58 % via the transition from farmland to grassland, grassland to farmland, and grassland to forest in the mode of edge expansion in the APENE, with 60.76 % areas were greening, while 1.04 % browning. The driving force of LUCC varied greatly with space and land use type, and natural factors play dominant roles in LUCC in the APENE. Over 70 % of farmland might be abandoned under the impact of future climate change, and serious conflict between afforestation investment and forest adaptation. The lack of scientific regulation of farmland quality and ecosystem adaptability were major problems in current policies. More positive economic and regulatory policies to benefit farmers and foresters should be adopted to further improve the talent and financial attractiveness of the agricultural production and desertification-combating practices and to fundamentally solve the current dilemma of food security and ecological construction.

A history of the 1.5°C target

AbstractThe 1.5°C target is now widely considered as the maximum acceptable limit for global warming. However, it is at once recent and, as it appears increasingly unreachable, already almost obsolete. Adopted as an aspirational target in the Paris Agreement in 2015, the 1.5°C objective originated with a political impetus within UNFCCC negotiations. The Intergovernmental Panel on Climate Change (IPCC) endorsed this policy‐driven target when it produced the Special Report on 1.5°C. This article highlights the continuity of the history of the 1.5°C target with that of the 2°C target, but also the differences between the two. Because the 1.5°C target considerably raises the bar on mitigation efforts, it exacerbates political tensions and ambiguities that were already latent in the 2°C target. This article retraces the emergence of the 1.5°C in diplomatic negotiations, the preparation of the IPCC Special report on 1.5°C, and the new kinds of debates they provoked among climate scientists and experts. To explain how an unreachable target became the reference for climate action, we analyze the “political calibration” of climate science and politics, which can also be described as a codependency between climate science and politics.This article is categorized under: Integrated Assessment of Climate Change > Integrated Assessment Modeling Climate, History, Society, Culture > World Historical Perspectives Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change

Future Climate Change Impact on the Streamflow of Mahi River Basin Under Different General Circulation Model Scenarios

Future Climate Change Impact on the Streamflow of Mahi River Basin Under Different General Circulation Model Scenarios