scholarly journals Climate Change Impacts on the Potential Distribution and Range Shift of Dendroctonus ponderosae (Coleoptera: Scolytidae)

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 860
Author(s):  
Zhou ◽  
Ge ◽  
Zou ◽  
Guo ◽  
Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Bark Beetles ◽  
Dendroctonus Ponderosae ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Range Shift ◽  
Pest Species ◽  
Climate Data ◽  
Climate Conditions ◽  
The Impact

Dendroctonus ponderosae Hopkins (Coleoptera: Scolytidae) is one of the most important bark beetles in North America and causes considerable economic and ecological losses during outbreaks. The distribution of this pest species is likely to be altered by climate change, which may threaten currently unaffected areas. In this study, we used CLIMEX to project the potential global distribution of D. ponderosae according to both historical climate data (1987–2016) and future climate warming estimates (2021–2100) to evaluate the impact of climate change on this species. Regions with suitable climate for D. ponderosae are distributed in all continents except Antarctica under both historical and future climate conditions, and these are predicted to change continuously with climate change. Overall, climate suitability will increase in middle- and high-latitude regions and decrease in low-latitude regions, and regions most sensitive to climate change are located in the mid-latitude zone. Moreover, the shift directions and ranges of climate-suitable regions under future conditions will differ among continents, and the shift distances in the north–south direction are larger than these in the east–west direction for Africa, Asia, Europe, South America, and Oceania, indicating that shift direction is possibly mainly affected by temperature. These projected distributions may provide theoretical guidance for early-warning intervention and risk assessment.

scholarly journals Climate change impacts on potential maize yields in Gambella Region, Ethiopia

2021 ◽  
Vol 21 (2) ◽  
Author(s):  
Azeb W. Degife ◽  
Florian Zabel ◽  
Wolfram Mauser
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Total Yield ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Yield Potential ◽  
Limiting Factor ◽  
Climate Conditions ◽  
Rcp 8.5 ◽  
The Impact

AbstractChanging climate conditions are supposed to have particularly strong impacts on agricultural production in the tropics with strong implications on food security. Ethiopia’s economy is profoundly dominated by agriculture, contributing to around 40% of the gross domestic product. Thereby, Ethiopia is one of the most vulnerable countries to the impact of climate change and has a wide gap in regional climate change impact studies. In this study, we systematically investigate climate change impacts on yields for the Gambella region in Ethiopia, exemplarily for maize. Here, we show how yields change until 2100 for RCPs 2.6, 4.5, and 8.5 from a climate model ensemble under rainfed and irrigated conditions. While rainfed yields decrease by 15% and 14% respectively for RCPs 2.6 and 4.5, yields decrease by up to 32% under RCP 8.5. Except for RCP 8.5, yields are not further decreasing after 2040–2069. We found that temperature increase, changing soil water availability, and atmospheric CO2 concentration have different effects on the simulated yield potential. Our results demonstrate the dominance of heat response under future climate conditions in the tropical Gambella region, contributing to 85% of total yield changes. Accordingly, irrigation will lose effectiveness for increasing yield when temperature becomes the limiting factor. CO2, on the other hand, contributes positively to yield changes by 8.9% for RCP 8.5. For all scenarios, the growing period is shorted due to increasing temperature by up to 29 days for RCP 8.5. Our results suggest that new varieties with higher growing degree days are primarily required to the region for adapting to future climate conditions.

Regional biogeographical model of vegetation zones in doctoral programme Regional Biography in Olomouc (Case study for Norway spruce)

2020 ◽  
Author(s):  
Ivo Machar ◽  
Marián Halás ◽  
Zdeněk Opršal
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Climate Change Impacts ◽  
Climate Data ◽  
Ecological Data ◽  
The Czech Republic ◽  
Climate Conditions ◽  
Vegetation Zones ◽  
Landscape Research

Regional climate changes impacts induce vegetation zones shift to higher altitudes in temperate landscape. This paper deals with applying of regional biogeography model of climate conditions for vegetation zones in Czechia to doctoral programme Regional Geography in Palacky University Olomouc. The model is based on general knowledge of landscape vegetation zonation. Climate data for model come from predicted validated climate database under RCP8.5 scenario since 2100. Ecological data are included in the Biogeography Register database (geobiocoenological data related to landscape for cadastral areas of the Czech Republic). Mathematical principles of modelling are based on set of software solutions with GIS. Students use the model in the frame of the course “Special Approaches to Landscape Research” not only for regional scenarios climate change impacts in landscape scale, but also for assessment of climate conditions for growing capability of agricultural crops or forest trees under climate change on regional level.

Bias correction of extreme values of high resolution climate simulations for risk analysis.

2021 ◽  
Author(s):  
luis Augusto sanabria ◽  
Xuerong Qin ◽  
Jin Li ◽  
Robert Peter Cechet
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Extreme Values ◽  
The Body ◽  
Future Climate ◽  
Return Periods ◽  
Climate Conditions ◽  
Mitigation And Adaptation ◽  
Climate Simulations ◽  
The Impact

Abstract Most climatic models show that climate change affects natural perils' frequency and severity. Quantifying the impact of future climate conditions on natural hazard is essential for mitigation and adaptation planning. One crucial factor to consider when using climate simulations projections is the inherent systematic differences (bias) of the modelled data compared with observations. This bias can originate from the modelling process, the techniques used for downscaling of results, and the ensembles' intrinsic variability. Analysis of climate simulations has shown that the biases associated with these data types can be significant. Hence, it is often necessary to correct the bias before the data can be reliably used for further analysis. Natural perils are often associated with extreme climatic conditions. Analysing trends in the tail end of distributions are already complicated because noise is much more prominent than that in the mean climate. The bias of the simulations can introduce significant errors in practical applications. In this paper, we present a methodology for bias correction of climate simulated data. The technique corrects the bias in both the body and the tail of the distribution (extreme values). As an illustration, maps of the 50 and 100-year Return Period of climate simulated Forest Fire Danger Index (FFDI) in Australia are presented and compared against the corresponding observation-based maps. The results show that the algorithm can substantially improve the calculation of simulation-based Return Periods. Forthcoming work will focus on the impact of climate change on these Return Periods considering future climate conditions.

scholarly journals Simulation of future climate scenarios and the impact on the water availability in southern Brazil

2021 ◽  
Vol 43 ◽  
pp. e56026
Author(s):  
Gabriela Leite Neves ◽  
Jorim Sousa das Virgens Filho ◽  
Maysa de Lima Leite ◽  
Frederico Fabio Mauad
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Water Availability ◽  
Meteorological Data ◽  
Future Climate ◽  
Climate Scenarios ◽  
Southern Brazil ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
The Impact

Water is an essential natural resource that is being impacted by climate change. Thus, knowledge of future water availability conditions around the globe becomes necessary. Based on that, this study aimed to simulate future climate scenarios and evaluate the impact on water balance in southern Brazil. Daily data of rainfall and air temperature (maximum and minimum) were used. The meteorological data were collected in 28 locations over 30 years (1980-2009). For the data simulation, we used the climate data stochastic generator PGECLIMA_R. It was considered two scenarios of the fifth report of the Intergovernmental Panel on Climate Change (IPCC) and a scenario with the historical data trend. The water balance estimates were performed for the current data and the simulated data, through the methodology of Thornthwaite and Mather (1955). The moisture indexes were spatialized by the kriging method. These indexes were chosen as the parameters to represent the water conditions in different situations. The region assessed presented a high variability in water availability among locations; however, it did not present high water deficiency values, even with climate change. Overall, it was observed a reduction of moisture index in most sites and in all scenarios assessed, especially in the northern region when compared to the other regions. The second scenario of the IPCC (the worst situation) promoting higher reductions and dry conditions for the 2099 year. The impacts of climate change on water availability, identified in this study, can affect the general society, therefore, they must be considered in the planning and management of water resources, especially in the regional context

Projecting future vegetation change for northeast China using CMIP6

2020 ◽  
Author(s):  
Wei Yuan ◽  
Shuang-ye Wu ◽  
Shugui Hou
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Management Strategies ◽  
Future Climate ◽  
Future Climate Change ◽  
Vegetation Changes ◽  
Emission Scenarios ◽  
Climate Data ◽  
Climate Conditions ◽  
Temperature And Precipitation

<p>This study aims to establish future vegetation changes in the east and central of northern China (ECNC), an ecologically sensitive region in the transition zonal from humid monsoonal to arid continental climate. The region has experienced significant greening in the past several decades. However, few studies exist on how vegetation will change with future climate change, and great uncertainties exist due to complex, and often spatially non-stationary, relationships between vegetation and climate. In this study, we first used historical NDVI and climate data to model this spatially variable relationship with Geographically Weighted Logit Regression. We found that temperature and precipitation could explain, on average, 43% of NDVI variance, and they could be used to model NDVI fairly well. We then establish future climate change using the output of 11 CMIP6 models for the medium (SSP245) and high (SSP585) emission scenarios for the mid-century (2041-2070) and late-century (2071-2100). The results show that for this region, both temperature and precipitation will increase under both scenarios. By late-century under SSP585, precipitation is projected to increase by 25.12% and temperature is projected to increase 5.87<sup>o</sup>C in ECNC. Finally, we used future climate conditions as input for the regression models to project future vegetation (indicated by NDVI). We found that NDVI will increase under climate change. By mid-century, the average NDVI in ECNC will increase by 0.024 and 0.021 under SSP245 and SSP585. By late-century, it will increase by 0.016 and 0.006 under SSP245 and SSP585 respectively. Although NDVI is projected to increase, the magnitude of increase is likely to diminish with higher emission scenarios, possibly due to the benefit of precipitation increase being gradually encroached by the detrimental effects of temperature increase. Moreover, despite the overall NDVI increase, the area likely to suffer vegetation degradation will also expands, particularly in the western part of ECNC. With higher emissions and later into the century, region with low NDVI is likely to shift and/or expand north-forward. Our results could provide important information on possible vegetation changes, which could help to develop effective management strategies to ensure ecological and economic sustainability in the future.</p>

scholarly journals Assessment of Impacts of Climate Change and Adaptation Measures for Maize Production in East Sikkim, India

2016 ◽  
Vol 9 (1) ◽  
pp. 15-27
Author(s):  
Proloy Deb ◽  
S. Babel
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Global Climate Model ◽  
Maize Yield ◽  
Future Climate ◽  
Yield Response ◽  
Climate Data ◽  
North East ◽  
The Impact ◽  
Impacts Of Climate Change

An investigation was carried out to assess the impacts of climate change on rainfed maize yield using a yield response to water stress model (AquaCrop) and to identify suitable adaptation options to minimize the negative impacts on maize yield in East Sikkim, North East India. Crop management and yield data was collected from the field experimental plots for calibration and validation of the model for the study area. The future climate data was developed for two IPCC emission scenarios A2 and B2 based on the global climate model HadCM3 with downscaling of climate to finer spatial resolution using the statistical downscaling model, SDSM. The impact study revealed that there is an expected reduction in maize yield of 12.8, 28.3 and 33.9% for the A2 scenario and 7.5, 19.9 and 29.9% for the B2 scenario during 2012-40, 2041-70 and 2071-99 respectively compared to the average yield simulated during the period of 1961-1990 with observed climate data. The maize yield of same variety under future climate can be maintained or improved from current level by changing planting dates, providing supplement irrigation and managing optimum nutrient.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.15-27

scholarly journals Formulating and testing a method for perturbing precipitation time series to reflect anticipated climatic changes

2017 ◽  
Vol 21 (1) ◽  
pp. 345-355 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Stylianos Georgiadis ◽  
Ida Bülow Gregersen ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Future Climate ◽  
High Temporal Resolution ◽  
Urban Water ◽  
Precipitation Time Series ◽  
Event Scale ◽  
The Impact

Abstract. Urban water infrastructure has very long planning horizons, and planning is thus very dependent on reliable estimates of the impacts of climate change. Many urban water systems are designed using time series with a high temporal resolution. To assess the impact of climate change on these systems, similarly high-resolution precipitation time series for future climate are necessary. Climate models cannot at their current resolutions provide these time series at the relevant scales. Known methods for stochastic downscaling of climate change to urban hydrological scales have known shortcomings in constructing realistic climate-changed precipitation time series at the sub-hourly scale. In the present study we present a deterministic methodology to perturb historical precipitation time series at the minute scale to reflect non-linear expectations to climate change. The methodology shows good skill in meeting the expectations to climate change in extremes at the event scale when evaluated at different timescales from the minute to the daily scale. The methodology also shows good skill with respect to representing expected changes of seasonal precipitation. The methodology is very robust against the actual magnitude of the expected changes as well as the direction of the changes (increase or decrease), even for situations where the extremes are increasing for seasons that in general should have a decreasing trend in precipitation. The methodology can provide planners with valuable time series representing future climate that can be used as input to urban hydrological models and give better estimates of climate change impacts on these systems.

scholarly journals Expected Changes to Alpine Pastures in Extent and Composition under Future Climate Conditions

Agronomy ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 926
Author(s):  
Camilla Dibari ◽  
Sergi Costafreda-Aumedes ◽  
Giovanni Argenti ◽  
Marco Bindi ◽  
Federico Carotenuto ◽  
...  
Keyword(s):  
Climate Change ◽  
Biodiversity Loss ◽  
Future Climate ◽  
Pastoral Systems ◽  
Pastoral System ◽  
Climate Conditions ◽  
Nardus Stricta ◽  
Livestock Feeding ◽  
The Alps ◽  
The Impact

As the basis of livestock feeding and related performances, pastures evolution and dynamics need to be carefully monitored and assessed, particularly in the Alps where the effects of land abandonment are further amplified by climate change. As such, increases in temperature associated with changes in precipitation patterns and quantity are leading to modifications of grassland extent and composition with consequences on the pastoral systems. This study applied a machine learning approach (Random Forest) and GIS techniques to map the suitability of seven pasture macro types most representative of the Italian Alps and simulated the impact of climate change on their dynamics according to two future scenarios (RCP4.5, 8.5), two time-slices (2011–2040, 2041–2070), and three RCMs (Aladin, CMCC, ICTP). Results indicated that (i) the methodology was robust to map the current suitability of pasture macro types (mean accuracy classification = 98.7%), so as to predict the expected alterations due to climate change; (ii) future climate will likely reduce current extend of suitable pasture (−30% on average) and composition, especially for most niche ecosystems (i.e., pastures dominated by Carex firma and Festuca gr. Rubra); (iii) areas suited to hardier but less palatable pastures (i.e., dominated by Nardus stricta and xeric species) will expand over the Alps in the near future. These impacts will likely determine risks for biodiversity loss and decreases of pastoral values for livestock feeding, both pivotal aspects for maintaining the viability and profitability of the Alpine pastoral system as a whole.

scholarly journals Evaluating Nature-Based Solution for Flood Reduction in Spercheios River Basin under Current and Future Climate Conditions

2021 ◽  
Vol 13 (7) ◽  
pp. 3885
Author(s):  
Christos Spyrou ◽  
Michael Loupis ◽  
Νikos Charizopoulos ◽  
Ilektra Apostolidou ◽  
Angeliki Mentzafou ◽  
...  
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Maximum Velocity ◽  
Weather Conditions ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Climate Conditions ◽  
The Impact

Nature-based solutions (NBS) are being deployed around the world in order to address hydrometeorological hazards, including flooding, droughts, landslides and many others. The term refers to techniques inspired, supported and copied from nature, avoiding large constructions and other harmful interventions. In this work the development and evaluation of an NBS applied to the Spercheios river basin in Central Greece is presented. The river is susceptible to heavy rainfall and bank overflow, therefore the intervention selected is a natural water retention measure that aims to moderate the impact of flooding and drought in the area. After the deployment of the NBS, we examine the benefits under current and future climate conditions, using various climate change scenarios. Even though the NBS deployed is small compared to the rest of the river, its presence leads to a decrease in the maximum depth of flooding, maximum velocity and smaller flooded areas. Regarding the subsurface/groundwater storage under current and future climate change and weather conditions, the NBS construction seems to favor long-term groundwater recharge.

scholarly journals Using 3PG to assess climate change impacts on management plan optimization of Eucalyptus plantations. A case study in Southern Brazil

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
João HN Palma ◽  
Rodrigo Hakamada ◽  
Gabriela Gonçalves Moreira ◽  
Silvana Nobre ◽  
Luiz Carlos Estraviz Rodriguez
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Management Plan ◽  
Empirical Models ◽  
Forest Growth ◽  
Future Climate ◽  
Empirical Equations ◽  
Complete Replacement ◽  
Physiological Models ◽  
The Impact

AbstractEucalyptus plantations around the world have been largely used by the paper industry. Optimizing the management of resources is a common practice in this highly competitive industry and new forest growth models may help to understand the impact of climate change on the decisions of the optimization processes. Current optimized management plans use empirical equations to predict future forest stands growth, and it is currently impractical to replace these empirical equations with physiological models due to data input requirements. In this paper, we present a different approach, by first carrying out a preliminary assessment with the process-based physiological model 3PG to evaluate the growth of Eucalyptus stands under climate change predictions. The information supplied by 3PG was then injected as a modifier in the projected yield that feeds the management plan optimizer allowing the interpretation of climate change impacts on the management plan. Modelling results show that although a general increase of rain with climate change is predicted, the distribution throughout the year will not favor the tree growth. On the contrary, rain will increase when it is less needed (summer) and decrease when it is most needed (winter), decreasing forest stand productivity between 3 and 5%, depending on the region and soil. Evaluation of the current optimized plan that kept constant the relation between wood price/cellulose ton shows a variation in different strategic management options and an overall increase of costs in owned areas between 2 and 4%, and a decrease of cumulated net present value, initially at 15% with later stabilization at 6–8%. This is a basic comparison to observe climate change effects; nevertheless, it provides insights into how the entire decision-making process may change due to a reduction in biomass production under future climate scenarios. This work demonstrates the use of physiological models to extract information that could be merged with existing and already implemented empirical models. The methodology may also be considered a preliminary alternative to the complete replacement of empirical models by physiological models. Our approach allows some insight into forest responses to different future climate conditions, something which empirical models are not designed for.

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