National indicators of climate changes, impacts, and vulnerability

Kenney, Melissa A.; Janetos, Anthony C.

doi:10.1007/s10584-020-02939-4

Cited by 9 publications

(4 citation statements)

References 25 publications

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“…This may also be possible through husband migration, where women have the opportunity to increase their representation and participation. This provides opportunities and space to gain new knowledge, experience, skills, and responsibilities in newly defined areas, as well as more opportunities for social networking and greater bargaining power [53]. The role and responsibilities of women in nuclear families are different and motivate them to change their position and take more responsibility for additional tasks when men are not at home and possibly influence decision making within the existing socio-cultural structure.…”

Section: Discussionmentioning

confidence: 99%

Differential Impact Analysis for Climate Change Adaptation: A Case Study from Nepal

et al. 2022

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Following a case study, community adaptation plans are a bottom-up approach that focus on increasing climate-vulnerable communities’ engagement in local adaptation planning and policy design, prioritization, and implementation in Nepal. This paper explains how Community-Based Adaptation Action Plan (CAPA) groups are being studied to assess the climate vulnerability of the local socio-ecosystem and to develop community-level adaptation measures. However, there is insufficient research to differentiate local vulnerabilities caused by climate change. This paper, therefore, examines climate change vulnerability with respect to community vulnerability and potential adaptation measures to increase community resilience and adaptive capacity through CAPAs. The study compares differences by gender, caste/ethnicity, and wealth in relation to specific climate-related hazards (exposure, sensitivity, and adaptive capacity) of communities. The study draws on secondary sources of information along with field observations, 73 household interviews, 13 key-informant interviews, consultations, and 9 interactive meetings in 3 districts of Nepal. Differential impact analysis refers to the exposure, sensitivity, and adaptive capacity of local socio-ecological systems. In addition, multivariate analysis was conducted using the Canoco program to analyze the role of actors with respect to climate vulnerability. The results conclude that the degree of vulnerability varies widely at the household level and is strongly influenced by socio-economic characteristics such as gender, caste/ethnicity, and wealth. Immediate and focused attention is needed to improve access to government resources for vulnerable households, requiring positive support from decision makers. Equally important is improving the chain of communication, which includes information, skills, knowledge, capacity, and institutional arrangements. Analysis of the differential vulnerability and the adaptive capacity of a vulnerable community is more appropriate for the design of local adaptation plans. Therefore, the study suggests that engagement of local partners, including local authorities, in addressing vulnerability and adaptation is required to confront the social process, new institutional arrangements, local adaptation, and capacity-building with technical solutions.

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Section: Discussionmentioning

confidence: 99%

Differential Impact Analysis for Climate Change Adaptation: A Case Study from Nepal

et al. 2022

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“…ej., pérdidas evitadas). Por lo general, las evaluaciones de la efectividad de las medidas de adaptación se han limitado al rendimiento de proyectos específicos frente a un conjunto limitado de eventos extremos o condiciones climáticas 32,151,152,153,154 . Investigadores y profesionales de la adaptación han empezado a hacer seguimiento del número de acciones que se han llevado a cabo en los EE.…”

Section: Mensaje Clave 313 Una Adaptación Transformadora Será Necesar...unclassified

“…• utilizar estas herramientas para seguir los avances en los esfuerzos y resultados de adaptación 33,153 ;…”

Section: ¿Los Servicios Se Mantienen E Integran?unclassified

Cap. 31. Adaptación. En: La Quinta Evaluación Nacional del Clima

2023

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Cambio institucional; cambios en la dieta y el desperdicio de alimentos; diversificación de los medios de subsistencia y las fuentes de ingresos; adaptación de las prácticas agrícolas, pesqueras o ganaderas (Recuadro 29.5); mejora de las variedades de cultivos; almacenamiento, distribución y seguridad de los alimentos; reducción del riesgo de desastres; migración permanente, retirada planificada o reubicación (KM 21.3, 24.4); movilidad estacional o temporal; redes de seguridad social y cohesión; protección social adaptativa; uso y demanda de agua; técnicas de gestión del suelo o de la tierra; almacenamiento posterior a la cosecha; recolección de agua de lluvia; ampliación de la gestión integrada de plagas; retirada estratégica de la costa (KM 9.3); protección de la tierra; cambios en los hábitos de transporte; mejora de la salud pública (KM 23.5). TecnologíaDesarrollo de tecnologías nuevas o ampliación de las existentes para permitir y avanzar en la adaptación; tecnologías de mejora del uso del agua o del acceso al agua; capacidad de energía solar; energía eólica; almacenamiento de energía; biogás; purificación del agua; producción de sal solar; microrredes; inteligencia artificial y aprendizaje automático. Número de actividades de adaptación documentadas públicamente (2018-2022)El nivel de actividad de adaptación documentada de los sectores público y privado varía mucho entre los estados y territorios estadounidenses.

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“…In recent years, under the background of global warming and urbanization (Kenney & Janetos, 2020; Masson et al, 2020; Xu et al, 2023), urban flooding and waterlogging events have occurred frequently worldwide and have become a serious challenge faced by many large cities worldwide (Guan et al, 2023; Moghadas et al, 2019; Sharifi, 2020; Thanvisitthpon et al, 2020; Wang, Hu, et al, 2020). For example, in July 2019, heavy rains caused at least 18 deaths in Mumbai, India, and caused large‐scale traffic interruptions (Ke et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Real‐time prediction and ponding process early warning method at urban flood points based on different deep learning methods

Zhou,

Wu,

Jiang

et al. 2023

J Flood Risk Management

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Accurate prediction of urban floods is regarded as one of the critical means to prevent urban floods and reduce the losses caused by floods. In this study, a refined prediction and early warning method system for urban flood and waterlogging processes based on deep learning methods is proposed. The spatial autocorrelation of rain and ponding points is analyzed by Moran's I (a common used statistic for spatial autocorrelation). For each ponding point, the relationship model between the rainfall process and ponding process is constructed based on different deep learning methods, and the results are analyzed and verified by mean absolute error (MAE), root mean square error (RMSE), Nash efficiency coefficient (NSE) and correlation coefficient (CC). The results show that the gradient boosting decision tree algorithm has the highest accuracy and efficiency (with a 0.001 m RMSE of the predicted and measured ponding depth) for ponding process prediction and is regarded as the most suitable method for ponding process prediction. Finally, the real‐time prediction and early warning of urban floods and waterlogging processes driven by rainfall forecast data are realized, and the results are verified by the measured data. The research results can provide theoretical support for urban flood prevention and control.

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National indicators of climate changes, impacts, and vulnerability

Cited by 9 publications

References 25 publications

Differential Impact Analysis for Climate Change Adaptation: A Case Study from Nepal

Differential Impact Analysis for Climate Change Adaptation: A Case Study from Nepal

Cap. 31. Adaptación. En: La Quinta Evaluación Nacional del Clima

Real‐time prediction and ponding process early warning method at urban flood points based on different deep learning methods

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