In-depth understanding of the potential implications of climate change is required to guide decision- and policy-makers when developing adaptation strategies and designing infrastructure suitable for future conditions. Impact models that translate potential future climate conditions into variables of interest are needed to create the causal connection between a changing climate and its impact for different sectors. Recent surveys suggest that the primary strategy for validating such models (and hence for justifying their use) heavily relies on assessing the accuracy of model simulations by comparing them against historical observations. We argue that such a comparison is necessary and valuable, but not sufficient to achieve a comprehensive evaluation of climate change impact models. We believe that a complementary, largely observation-independent, step of model evaluation is needed to ensure more transparency of model behavior and greater robustness of scenario-based analyses. This step should address the following four questions: (1) Do modeled dominant process controls match our system perception? (2) Is my model's sensitivity to changing forcing as expected? (3) Do modeled decision levers show adequate influence? (4) Can we attribute uncertainty sources throughout the projection horizon? We believe that global sensitivity analysis, with its ability to investigate a model's response to joint variations of multiple inputs in a structured way, offers a coherent approach to address all four questions comprehensively. Such additional model evaluation would strengthen stakeholder confidence in model projections and, therefore, into the adaptation strategies derived with the help of impact models. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Assessing Impacts of Climate Change > Evaluating Future Impacts of Climate Change
The Strengths and Difficulties Questionnaire (SDQ) is a frequently used screening instrument for behavioral problems in children and adolescents. There is an ongoing controversy—not only in educational research—regarding the factor structure of the SDQ. Research results speak for a 3-factor as well as a 5-factor structure. The narrowband scales (5-factor structure) can be combined into broadband scales (3-factor structure). The question remains: Which factors (narrowband vs. broadband) are better predictors? With the prediction of child and adolescent outcomes (academic grades, well-being, and self-belief), we evaluated whether the broadband scales of internalizing and externalizing behavior (3-factor structure) or narrowband scales of behavior (5-factor structure) are better suited for predictive purposes in a cross-sectional study setting. The sample includes students in grades 5 to 9 (N = 4642) from the representative German Health Interview and Examination Survey for Children and Adolescents (KiGGS study). The results of model comparisons (broadband scale vs. narrowband scales) did not support the superiority of the broadband scales with regard to the prediction of child and adolescent outcomes. There is no benefit from subsuming narrowband scales (5-factor structure) into broadband scales (3-factor structure). The application of narrowband scales, providing a more differentiated picture of students’ academic and social situation, was more appropriate for predictive purposes. For the purpose of identifying students at risk of struggling in educational contexts, using the set of narrowband dimensions of behavior seems to be more suitable.
The Strengths and Difficulties Questionnaire (SDQ) is a frequently used screening instrument for behavioral problems in children and adolescents. There is an ongoing controversy—not only in educational research—regarding the factor structure of the SDQ. Research results speak for a 3-factor as well as a 5-factor structure. The narrowband scales (5-factor structure) can be combined into broadband scales (3-factor structure). The question remains: Which factors (narrowband vs. broadband) are better predictors? With the prediction of child and adolescent outcomes (academic grades, well-being, and self-belief), we evaluated whether the broadband scales of internalizing and externalizing behavior (3-factor structure) or narrowband scales of behavior (5-factor structure) are better suited for predictive purposes in a cross-sectional study setting. The sample includes students in grades 5 to 9 (N = 4642) from the representative German Health Interview and Examination Survey for Children and Adolescents (KiGGS study). The results of model comparisons (broadband scale vs. narrowband scales) did not support the superiority of the broadband scales with regard to the prediction of child and adolescent outcomes. There is no benefit from subsuming narrowband scales (5-factor structure) into broadband scales (3-factor structure). The application of narrowband scales, providing a more differentiated picture of students’ academic and social situation, was more appropriate for predictive purposes. For the purpose of identifying students at risk of struggling in educational contexts, using the set of narrowband dimensions of behavior seems to be more suitable.
Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.
Previous research offers equivocal results regarding the effect of
social networking site use on individuals’ self-esteem. We con-
duct a systematic literature review to examine the existing litera-
ture and develop a theoretical framework in order to classify the
results. The framework proposes that self-esteem is affected by
three distinct processes that incorporate self-evaluative informa-
tion: social comparison processes, social feedback processing,
and self-reflective processes. Due to particularities of the social
networking site environment, the accessibility and quality of self-
evaluative information is altered, which leads to online-specific
effects on users’ self-esteem. Results of the reviewed studies
suggest that when a social networking site is used to compare
oneself with others, it mostly results in decreases in users’ self-
esteem. On the other hand, receiving positive social feedback
from others or using these platforms to reflect on one’s own self is
mainly associated with benefits for users’ self-esteem.
Nevertheless, inter-individual differences and the specific activ-
ities performed by users on these platforms should be considered
when predicting individual effects.