scholarly journals Snow Vulnerability Analysis in the Korean Peninsula using RCP Climate Change Scenarios

2020 ◽  
Vol 20 (4) ◽  
pp. 251-259
Author(s):  
Joonhyeok Ha ◽  
Heeseong Park ◽  
Gunhui Chung
Keyword(s):  
Climate Change ◽  
Vulnerability Analysis ◽  
Entropy Method ◽  
Weather Data ◽  
Climate Change Scenarios ◽  
State Response ◽  
Thiessen Polygon ◽  
The Future ◽  
Polygon Method

Snow vulnerability analysis was implemented using 400 years of controlled RCP 2.6, 4.5, 6.0, and 8.5 scenarios in the following divided periods: the former period (2011-2040), middle period (2041-2070), and later period (2071-2100). Data from a total of 74 meteorological stations were used and the Thiessen polygon method was applied in the areas without stations. The indicators were classified into the Pressure-State-Response (PSR) structure, and the weight for vulnerability analysis was calculated using the entropy method. As snow vulnerability analysis was implemented for the future scenarios, it was difficult to determine social and economic factors as indicators; thus, only predicted weather data and population trends were considered. As a result, the rankings for snow vulnerable areas were determined for each period and scenario. Overall, snow vulnerability would decrease due to the decrease in long-term heavy snowfall in climate change scenarios. However, increased snow vulnerability is also expected in Sejong-si and the western coastal area due to a rise in population and snow depth in the future. Based on this, disaster prevention projects considering the characteristics of the region in the future could be implemented.

scholarly journals A Hydrological Modeling Approach for Assessing the Impacts of Climate Change on Runoff Regimes in Slovakia

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3358
Author(s):  
Patrik Sleziak ◽  
Roman Výleta ◽  
Kamila Hlavčová ◽  
Michaela Danáčová ◽  
Milica Aleksić ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Hydrological Modeling ◽  
Performance Metrics ◽  
Climate Change Scenarios ◽  
Reference Period ◽  
Changing Climate ◽  
Time Horizons ◽  
The Future

The changing climate is a concern with regard to sustainable water resources. Projections of the runoff in future climate conditions are needed for long-term planning of water resources and flood protection. In this study, we evaluate the possible climate change impacts on the runoff regime in eight selected basins located in the whole territory of Slovakia. The projected runoff in the basins studied for the reference period (1981–2010) and three future time horizons (2011–2040, 2041–2070, and 2071–2100) was simulated using the HBV (Hydrologiska Byråns Vattenbalansavdelning) bucket-type model (the TUW (Technische Universität Wien) model). A calibration strategy based on the selection of the most suitable decade in the observation period for the parameterization of the model was applied. The model was first calibrated using observations, and then was driven by the precipitation and air temperatures projected by the KNMI (Koninklijk Nederlands Meteorologisch Instituut) and MPI (Max Planck Institute) regional climate models (RCM) under the A1B emission scenario. The model’s performance metrics and a visual inspection showed that the simulated runoff using downscaled inputs from both RCM models for the reference period represents the simulated hydrological regimes well. An evaluation of the future, which was performed by considering the representative climate change scenarios, indicated that changes in the long-term runoff’s seasonality and extremality could be expected in the future. In the winter months, the runoff should increase, and decrease in the summer months compared to the reference period. The maximum annual daily runoff could be more extreme for the later time horizons (according to the KNMI scenario for 2071–2100). The results from this study could be useful for policymakers and river basin authorities for the optimum planning and management of water resources under a changing climate.

scholarly journals Downscaling climate change scenarios for apple pest and disease modeling in Switzerland

2011 ◽  
Vol 2 (2) ◽  
pp. 493-529 ◽  
Author(s):  
M. Hirschi ◽  
S. Stoeckli ◽  
M. Dubrovsky ◽  
C. Spirig ◽  
P. Calanca ◽  
...  
Keyword(s):  
Climate Change ◽  
Fire Blight ◽  
Codling Moth ◽  
Weather Data ◽  
Climate Change Scenarios ◽  
Climate Conditions ◽  
Pests And Diseases ◽  
Daily Weather Data ◽  
The Future ◽  
The Impact

Abstract. As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously not affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology depending on actual weather conditions and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980–2009 and 2045–2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the ENSEMBLES multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (Cydia pomonella) and fire blight (Erwinia amylovora) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045–2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1 % on average today to over 60 % in the future for the median climate change signal of the multi-model projections), the actual risk will critically depend on the pace of the adaptation of the codling moth with respect to the critical photoperiod. To control this additional generation, an intensification and prolongation of control measures (e.g., insecticides) will be required, implying an increasing risk of pesticide resistances. For fire blight, the projected changes in infection days are less certain due to uncertainties in the leaf wetness approximation and the simulation of the blooming period. Two compensating effects are projected, warmer temperatures favoring infections are balanced by a temperature-induced advancement of the blooming period, leading to no significant change in the number of infection days under future climate conditions for most stations.

scholarly journals Downscaling climate change scenarios for apple pest and disease modeling in Switzerland

2012 ◽  
Vol 3 (1) ◽  
pp. 33-47 ◽  
Author(s):  
M. Hirschi ◽  
S. Stoeckli ◽  
M. Dubrovsky ◽  
C. Spirig ◽  
P. Calanca ◽  
...  
Keyword(s):  
Climate Change ◽  
Fire Blight ◽  
Codling Moth ◽  
Weather Data ◽  
Climate Change Scenarios ◽  
Climate Conditions ◽  
Pests And Diseases ◽  
Daily Weather Data ◽  
The Future ◽  
The Impact

Abstract. As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously non-affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology, depending on actual weather conditions, and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980–2009 and 2045–2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the ENSEMBLES multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (Cydia pomonella) and fire blight (Erwinia amylovora) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045–2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1% on average today to over 60% in the future for the median climate change signal of the multi-model projections), the actual risk will critically depend on the pace of the adaptation of the codling moth with respect to the critical photoperiod. To control this additional generation, an intensification and prolongation of control measures (e.g. insecticides) will be required, implying an increasing risk of pesticide resistances. For fire blight, the projected changes in infection days are less certain due to uncertainties in the leaf wetness approximation and the simulation of the blooming period. Two compensating effects are projected, warmer temperatures favoring infections are balanced by a temperature-induced advancement of the blooming period, leading to no significant change in the number of infection days under future climate conditions for most stations.

Downscaling of climate change scenarios for a high resolution, site–specific assessment of drought stress risk for two viticultural regions with heterogeneous landscapes

2021 ◽  
Author(s):  
Marco Hofmann ◽  
Claudia Volosciuk ◽  
Martin Dubrovský ◽  
Douglas Maraun ◽  
Hans R. Schultz
Keyword(s):  
Climate Change ◽  
Drought Stress ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Weather Data ◽  
Climate Change Scenarios ◽  
Research Knowledge ◽  
Wine Production ◽  
The Future

Abstract. Extended periods without precipitation observed for example in Central Europe including Germany during the seasons from 2018 to 2020, can lead to water deficit and yield and quality losses for grape and wine production. However, irrigation infrastructure is largely non–existent. Regional climate models project changes of precipitation amounts and patterns, indicating an increase in frequency of occurrence of comparable situations in the future. In order to assess possible impacts of climate change on the water budget of grapevines, a water balance model was developed, which accounts for the large heterogeneity of vineyards with respect to their soil water storage capacity, evapotranspiration as a function of slope and aspect, and viticultural management practices. The model was fed with data from soil maps (soil type and plant available water capacity), a digital elevation model, the European Union (EU) vineyard–register, observed weather data and future weather data provided by regional climate models and a stochastic weather generator. This allowed conducting a risk assessment of the drought stress occurrence for the wine–producing regions Rheingau and Hessische Bergstraße in Germany on the scale of individual vineyard plots. The simulations showed that the risk for drought stress varies substantially between vineyard sites but might increase for steep–slope regions in the future. Possible adaptation measures depend highly on local conditions and to make targeted use of the resource water, an intense interplay of different wine-industry stakeholders, research, knowledge transfer, and local authorities will be required.

scholarly journals Generation of Synthetic Daily Weather for Climate Change Scenarios and Extreme Storm Intensification

2019 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Jurgen Garbrecht ◽  
X. C. Zhang ◽  
David Brown ◽  
Phillip Busteed
Keyword(s):  
Climate Change ◽  
Weather Data ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
Generation Model ◽  
Traditional Role ◽  
Climate Conditions ◽  
Daily Weather ◽  
Daily Weather Data

Long-term simulations in watershed hydrology, soil and nutrient transport, and sustainability of agricultural production systems require long-term weather records that are often not available at the location of interest. Generation of synthetic daily weather data is a common approach to augment limited weather observations. Here a synthetic daily weather generation model (called SYNTOR) is described. SYNTOR fulfills the traditional role of generating alternative weather realizations that have statistical properties similar to those of the parent historical weather it is intended to simulate. In addition, it has the capability to simulate daily weather records for climate change scenarios and storm intensification due to climate change. The various model components are briefly summarized and an application is presented for semi-arid climate conditions in west-central Oklahoma. SYNTOR generated daily weather compared well with observed weather values. Climate change is simulated by adjusting weather generation parameters to reflect the changed mean monthly weather values of climate projections. Storm intensification is approximated by increasing the top 10 percentile of storm distribution by a predefined amount based on previous studies of trends in United States precipitation. Further evaluation of published storm intensification values and associated uncertainties and spatial variability is recommended.

scholarly journals Assessing the long-term effectiveness of Nature-Based Solutions under different climate change scenarios

2021 ◽  
pp. 148515
Author(s):  
Eulalia Gómez Martín ◽  
María Máñez Costa ◽  
Sabine Egerer ◽  
Uwe Schneider
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios

Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan

2012 ◽  
Vol 92 (3) ◽  
pp. 421-425 ◽  
Author(s):  
Hong Wang ◽  
Yong He ◽  
Budong Qian ◽  
Brian McConkey ◽  
Herb Cutforth ◽  
...  
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Short Communication ◽  
Weather Data ◽  
Yield Reduction ◽  
Climate Change Scenarios ◽  
Communication Climate ◽  
Stochastic Weather Generator ◽  
Ipcc Sres

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (>32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.

scholarly journals A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

2015 ◽  
Vol 127 (3-4) ◽  
pp. 573-585 ◽  
Author(s):  
G. Duveiller ◽  
M. Donatelli ◽  
D. Fumagalli ◽  
A. Zucchini ◽  
R. Nelson ◽  
...  
Keyword(s):  
Climate Change ◽  
Weather Data ◽  
Climate Change Scenarios ◽  
Crop Modelling ◽  
Daily Weather ◽  
Daily Weather Data

Projecting conflict risk following the Shared Socioeconomic pathways: what role for water stress and climate?

2021 ◽  
Author(s):  
Sophie de Bruin ◽  
Jannis Hoch ◽  
Nina von Uexkull ◽  
Halvard Buhaug ◽  
Nico Wanders
Keyword(s):  
Climate Change ◽  
At Risk ◽  
Machine Learning Techniques ◽  
Mitigation Measures ◽  
Related Factors ◽  
Socioeconomic Impacts ◽  
Learning Techniques ◽  
The Future ◽  
Conflict Risk

<p>The socioeconomic impacts of changes in climate-related and hydrology-related factors are increasingly acknowledged to affect the on-set of violent conflict. Full consensus upon the general mechanisms linking these factors with conflict is, however, still limited. The absence of full understanding of the non-linearities between all components and the lack of sufficient data make it therefore hard to address violent conflict risk on the long-term. </p><p>Although it is neither desirable nor feasible to make exact predictions, projections are a viable means to provide insights into potential future conflict risks and uncertainties thereof. Hence, making different projections is a legitimate way to deal with and understand these uncertainties, since the construction of diverse scenarios delivers insights into possible realizations of the future.  </p><p>Through machine learning techniques, we (re)assess the major drivers of conflict for the current situation in Africa, which are then applied to project the regions-at-risk following different scenarios. The model shows to accurately reproduce observed historic patterns leading to a high ROC score of 0.91. We show that socio-economic factors are most dominant when projecting conflicts over the African continent. The projections show that there is an overall reduction in conflict risk as a result of increased economic welfare that offsets the adverse impacts of climate change and hydrologic variables. It must be noted, however, that these projections are based on current relations. In case the relations of drivers and conflict change in the future, the resulting regions-at-risk may change too.   By identifying the most prominent drivers, conflict risk mitigation measures can be tuned more accurately to reduce the direct and indirect consequences of climate change on the population in Africa. As new and improved data becomes available, the model can be updated for more robust projections of conflict risk in Africa under climate change.</p>

Climate, ticks and disease

2021 ◽  
Keyword(s):  
Climate Change ◽  
Spatial Distribution ◽  
Disease Ecology ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Expert Opinions ◽  
The World ◽  
Host Pathogen ◽  
The Future ◽  
Impacts Of Climate Change

Abstract This book is a collection of 77 expert opinions arranged in three sections. Section 1 on "Climate" sets the scene, including predictions of future climate change, how climate change affects ecosystems, and how to model projections of the spatial distribution of ticks and tick-borne infections under different climate change scenarios. Section 2 on "Ticks" focuses on ticks (although tick-borne pathogens creep in) and whether or not changes in climate affect the tick biosphere, from physiology to ecology. Section 3 on "Disease" focuses on the tick-host-pathogen biosphere, ranging from the triangle of tick-host-pathogen molecular interactions to disease ecology in various regions and ecosystems of the world. Each of these three sections ends with a synopsis that aims to give a brief overview of all the expert opinions within the section. The book concludes with Section 4 (Final Synopsis and Future Predictions). This synopsis attempts to summarize evidence provided by the experts of tangible impacts of climate change on ticks and tick-borne infections. In constructing their expert opinions, contributors give their views on what the future might hold. The final synopsis provides a snapshot of their expert thoughts on the future.

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