Ensemble-based analysis of regional climate change effects on the cabbage stem weevil (Ceutorhynchus pallidactylus (Mrsh.)) in winter oilseed rape (Brassica napus L.)

2011 ◽  
Vol 150 (2) ◽  
pp. 191-202 ◽  
Author(s):  
J. JUNK ◽  
M. EICKERMANN ◽  
K. GÖRGEN ◽  
M. BEYER ◽  
L. HOFFMANN
Keyword(s):  
Climate Change ◽  
Oilseed Rape ◽  
Stem Elongation ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Future Climate ◽  
Brassica Napus L ◽  
Climate Conditions ◽  
Ceutorhynchus Pallidactylus ◽  
The Impact

SUMMARYThe impact of projected regional climate change on the migration of cabbage stem weevil (Ceutorhynchus pallidactylus) to oilseed rape crops in the Grand Duchy of Luxembourg is evaluated for past and future time spans. Several threshold-based statistical models for the emergence and the main migration of C. pallidactylus were chosen from the literature and combined with selected regional climate change projections of the EU ENSEMBLES project. Additionally, a simple degree-day based model was used to assess the plant development under expected climate change conditions. An earlier onset as well as a prolongation of the possible emergence times and the main migration periods was detected. The onset of stem elongation of oilseed rape was predicted to occur 3·0 days earlier per decade, while emergence of C. pallidactylus was expected to occur between 3·0 and 3·3 days earlier per decade. The main migration period of the weevil to the field may start 2·0 days earlier per decade under future climate conditions. Additionally, the time span of possible migration is prolonged for about 30 days under projected future climate conditions.

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 Climate Change Controls Phosphorus Transport from the Watershed into Lake Kinneret (Israel)

2021 ◽  
Author(s):  
Moshe Gophen
Keyword(s):  
Climate Change ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Lake Kinneret ◽  
Phosphorus Transport ◽  
Efficient Management ◽  
Hula Valley ◽  
Reclamation Project ◽  
The Impact

AbstractPart of the Kinneret watershed, the Hula Valley, was modified from wetlands – shallow lake for agricultural cultivation. Enhancement of nutrient fluxes into Lake Kinneret was predicted. Therefore, a reclamation project was implemented and eco-tourism partly replaced agriculture. Since the mid-1980s, regional climate change has been documented. Statistical evaluation of long-term records of TP (Total Phosphorus) concentrations in headwaters and potential resources in the Hula Valley was carried out to identify efficient management design targets. Significant correlation between major headwater river discharge and TP concentration was indicated, whilst the impact of external fertilizer loads and 50,000 winter migratory cranes was probably negligible. Nevertheless, confirmed severe bdamage to agricultural crops carried out by cranes led to their maximal deportation and optimization of their feeding policy. Consequently, the continuation of the present management is recommended.

scholarly journals AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change

2020 ◽  
Vol 12 (8) ◽  
pp. 3223 ◽  
Author(s):  
Soheil Fathi ◽  
Ravi S. Srinivasan ◽  
Charles J. Kibert ◽  
Ruth L. Steiner ◽  
Emre Demirezen
Keyword(s):  
Climate Change ◽  
Energy Use ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Developed Countries ◽  
Energy Performance ◽  
Energy Needs ◽  
Energy Use Prediction ◽  
Campus Energy ◽  
The Impact

In developed countries, buildings are involved in almost 50% of total energy use and 30% of global annual greenhouse gas emissions. The operational energy needs of buildings are highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based energy modeling of buildings, Artificial Intelligence (AI) has the capability to provide faster and higher accuracy estimates, given buildings’ historic energy consumption data. Looking beyond individual building levels, forecasting building energy performance can help city and community managers have a better understanding of their future energy needs, and to plan for satisfying them more efficiently. Focusing at an urban scale, this research develops a campus energy use prediction tool for predicting the effects of long-term climate change on the energy performance of buildings using AI techniques. The tool comprises four steps: Data Collection, AI Development, Model Validation, and Model Implementation, and can predict the energy use of campus buildings with 90% accuracy. We have relied on energy use data of buildings situated in the University of Florida, Gainesville, Florida (FL). To study the impact of climate change, we have used climate properties of three future weather files of Gainesville, FL, developed by the North American Regional Climate Change Assessment Program (NARCCAP), represented based on their impact: median (year 2063), hottest (2057), and coldest (2041).

The Impact of Regional Climate Change on the Marine Ecosystem of the Western Antarctic Peninsula

2012 ◽  
pp. 91-120 ◽  
Author(s):  
Andrew Clarke ◽  
David K. A. Barnes ◽  
Thomas J. Bracegirdle ◽  
Hugh W. Ducklow ◽  
John C. King ◽  
...  
Keyword(s):  
Climate Change ◽  
Antarctic Peninsula ◽  
Regional Climate ◽  
Marine Ecosystem ◽  
Regional Climate Change ◽  
Western Antarctic Peninsula ◽  
The Impact

scholarly journals Evaluation of three different regional climate change scenarios for the application of a water balance model in a mesoscale catchment in Northeast Germany

2010 ◽  
Vol 27 ◽  
pp. 57-64 ◽  
Author(s):  
M. Wegehenkel ◽  
U. Heinrich ◽  
H. Jochheim ◽  
K. C. Kersebaum ◽  
B. Röber
Keyword(s):  
Climate Change ◽  
Water Balance ◽  
Input Data ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Water Balance Model ◽  
Balance Model ◽  
Data Sets ◽  
Climate Change Scenarios ◽  
The Impact

Abstract. Future climate changes might have some impacts on catchment hydrology. An assessment of such impacts on e.g. ground water recharge is required to derive adaptation strategies for future water resources management. The main objective of our study was an analysis of three different regional climate change scenarios for a catchment with an area of 2415 km2 located in the Northeastern German lowlands. These data sets consist of the STAR-scenario with a time period 1951–2055, the WettReg-scenario covering the period 1961–2100 and the grid based REMO-scenario for the time span 1950–2100. All three data sets are based on the SRES scenario A1B of the IPCC. In our analysis, we compared the meteorological data for the control period obtained from the regional climate change scenarios with corresponding data measured at meteorological stations in the catchment. The results of this analysis indicated, that there are high differences between the different regional climate change scenarios regarding the temporal dynamics and the amount of precipitation. In addition, we applied a water balance model using input data obtained from the different climate change scenarios and analyzed the impact of these different input data on the model output groundwater recharge. The results of our study indicated, that these regional climate change scenarios due to the uncertainties in the projections of precipitation show only a limited suitability for hydrologic impact analysis used for the establishment of future concrete water management procedures in their present state.

The Impact of Climate Change on the River Jordan-Lake Kinneret (Israel) Ecosystem

2020 ◽  
pp. 1-12
Author(s):  
Moshe Gophen
Keyword(s):  
Climate Change ◽  
Lake Water ◽  
Regional Climate ◽  
Nitrogen Deficiency ◽  
Regional Climate Change ◽  
Lake Kinneret ◽  
Ecosystem Structure ◽  
Nutrient Inputs ◽  
Lake Water Level ◽  
The Impact

The long-term record of River Jordan-Lake Kinneret ecosystem indicates some significant climate condition changes: water temperature increase, decline in rainfall, and diminishing river discharges and lake water inflows accompanied by a reduction in nitrogen and a slight increase in phosphorus in the Lake upper layers (Epilimnion). Lake Water level decreased, Prolongation of Residence Time was documented, nutrient inputs and dynamics modifications resulting water quality deterioration. As a result of temperature elevation and nitrogen deficiency, the biomass of Peridinium spp significantly reduced and was replaced by Cyanobacterial biomass enhancement. Dryness trend expressed as enhanced frequency of drought seasons initiated an elevation of lake water salinity. It has been suggested that these changes in the phytoplankton community structure are caused by regional climate change. This study evaluates a multi-annual respective approach although the summer is the most critical. The objective of this research is evaluate the background of the ecosystem structure modification aimed at define future potential management design.

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 Storyline approach to the construction of regional climate change information

2019 ◽  
Vol 475 (2225) ◽  
pp. 20190013 ◽  
Author(s):  
Theodore G. Shepherd
Keyword(s):  
Climate Change ◽  
Atmospheric Circulation ◽  
Regional Climate ◽  
Regional Scale ◽  
Regional Climate Change ◽  
Climate Impacts ◽  
Climate Projections ◽  
Ethical Implications ◽  
Climate Change Information ◽  
The Impact

Climate science seeks to make statements of confidence about what has happened, and what will happen (conditional on scenario). The approach is effective for the global, thermodynamic aspects of climate change, but is ineffective when it comes to aspects of climate change related to atmospheric circulation, which are highly uncertain. Yet, atmospheric circulation strongly mediates climate impacts at the regional scale. In this way, the confidence framework, which focuses on avoiding type 1 errors (false alarms), raises the prospect of committing type 2 errors (missed warnings). This has ethical implications. At the regional scale, however, where information on climate change has to be combined with many other factors affecting vulnerability and exposure—most of which are highly uncertain—the societally relevant question is not ‘What will happen?’ but rather ‘What is the impact of particular actions under an uncertain regional climate change?’ This reframing of the question can cut the Gordian knot of regional climate change information, provided one distinguishes between epistemic and aleatoric uncertainties—something that is generally not done in climate projections. It is argued that the storyline approach to climate change—the identification of physically self-consistent, plausible pathways—has the potential to accomplish precisely this.

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