Incorporating climate change scenarios into new operating rules for large reservoirs

2011 ◽  
pp. 469-477
Author(s):  
B Dewals ◽  
S Detrembleur ◽  
P Archambeau ◽  
S Erpicum ◽  
M Pirotton ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Scenarios ◽  
Operating Rules

Modelling impacts of different climate change scenarios on soil water regime of a Mollisol

2005 ◽  
Vol 33 (1) ◽  
pp. 185-188 ◽  
Author(s):  
Csilla Farkas ◽  
Roger Randriamampianina ◽  
Juraj Majerčak
Keyword(s):  
Climate Change ◽  
Soil Water ◽  
Water Regime ◽  
Climate Change Scenarios ◽  
Soil Water Regime

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

scholarly journals Mapping current and potential future distributions of the oak tree (Quercus aegilops) in the Kurdistan Region, Iraq

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Nabaz R. Khwarahm
Keyword(s):  
Climate Change ◽  
Habitat Suitability ◽  
Environmental Variables ◽  
Climate Changes ◽  
Future Climate ◽  
Annual Precipitation ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Kurdistan Region ◽  
Oak Tree

Abstract Background The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species, Q. aegilops forest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI, Q. aegilops has been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions of Q. aegilops is minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records of Q. aegilops, and environmental variables. Results The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges of Q. aegilops would be reduced by 3.6% (1849.7 km2) and 3.16% (1627.1 km2), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km2) and 1.7% (848.0 km2), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes. Conclusions The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.

Impact assessment of common bean availability in Brazil under climate change scenarios

2021 ◽  
Vol 191 ◽  
pp. 103174
Author(s):  
Luís A.S. Antolin ◽  
Alexandre B. Heinemann ◽  
Fábio R. Marin
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Common Bean ◽  
Climate Change Scenarios

scholarly journals Elevated temperature and browning increase dietary methylmercury, but decrease essential fatty acids at the base of lake food webs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pianpian Wu ◽  
Martin J. Kainz ◽  
Fernando Valdés ◽  
Siwen Zheng ◽  
Katharina Winter ◽  
...  
Keyword(s):  
Climate Change ◽  
Fatty Acids ◽  
Organic Matter ◽  
Food Webs ◽  
Essential Fatty Acids ◽  
Trophic Levels ◽  
Climate Change Scenarios ◽  
Essential Nutrients ◽  
Aquatic Food Webs ◽  
Aquatic Food

AbstractClimate change scenarios predict increases in temperature and organic matter supply from land to water, which affect trophic transfer of nutrients and contaminants in aquatic food webs. How essential nutrients, such as polyunsaturated fatty acids (PUFA), and potentially toxic contaminants, such as methylmercury (MeHg), at the base of aquatic food webs will be affected under climate change scenarios, remains unclear. The objective of this outdoor mesocosm study was to examine how increased water temperature and terrestrially-derived dissolved organic matter supply (tDOM; i.e., lake browning), and the interaction of both, will influence MeHg and PUFA in organisms at the base of food webs (i.e. seston; the most edible plankton size for zooplankton) in subalpine lake ecosystems. The interaction of higher temperature and tDOM increased the burden of MeHg in seston (< 40 μm) and larger sized plankton (microplankton; 40–200 μm), while the MeHg content per unit biomass remained stable. However, PUFA decreased in seston, but increased in microplankton, consisting mainly of filamentous algae, which are less readily bioavailable to zooplankton. We revealed elevated dietary exposure to MeHg, yet decreased supply of dietary PUFA to aquatic consumers with increasing temperature and tDOM supply. This experimental study provides evidence that the overall food quality at the base of aquatic food webs deteriorates during ongoing climate change scenarios by increasing the supply of toxic MeHg and lowering the dietary access to essential nutrients of consumers at higher trophic levels.

scholarly journals Spatial Prediction of Future Flood Risk: An Approach to the Effects of Climate Change

Geosciences ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 25
Author(s):  
Mohammadtaghi Avand ◽  
Hamid Reza Moradi ◽  
Mehdi Ramazanzadeh Lasboyee
Keyword(s):  
Climate Change ◽  
Spatial Prediction ◽  
Flood Management ◽  
Management Program ◽  
Climate Change Scenarios ◽  
Ann Model ◽  
Probability Map ◽  
Flood Susceptibility ◽  
Flood Probability ◽  
Model Training

Preparation of a flood probability map serves as the first step in a flood management program. This research develops a probability flood map for floods resulting from climate change in the future. Two models of Flexible Discrimination Analysis (FDA) and Artificial Neural Network (ANN) were used. Two optimistic (RCP2.6) and pessimistic (RCP8.5) climate change scenarios were considered for mapping future rainfall. Moreover, to produce probability flood occurrence maps, 263 locations of past flood events were used as dependent variables. The number of 13 factors conditioning floods was taken as independent variables in modeling. Of the total 263 flood locations, 80% (210 locations) and 20% (53 locations) were considered model training and validation. The Receiver Operating Characteristic (ROC) curve and other statistical criteria were used to validate the models. Based on assessments of the validated models, FDA, with a ROC-AUC = 0.918, standard error (SE = 0.038), and an accuracy of 0.86% compared to the ANN model with a ROC-AUC = 0.897, has the highest accuracy in preparing the flood probability map in the study area. The modeling results also showed that the factors of distance from the River, altitude, slope, and rainfall have the greatest impact on floods in the study area. Both models’ future flood susceptibility maps showed that the highest area is related to the very low class. The lowest area is related to the high class.

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