Climate data for the European forestry sector: From end-user needs to opportunities for climate resilience

Fraccaroli, Cecilia; Govigli, Valentino Marini; Briers, Siebe; Peña, Nieves; Paz, Jorge; Romero, María Alejandra; Lindner, Marcus; Arano, Inazio Martínez de

doi:10.1016/j.cliser.2021.100247

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Forestry end-users widely request the integration of climate with non-climate data. Thus, CSs suppliers should aim at evolving CSs (e.g., by creating foreseen species distribution maps, platforms on emergency risk management, and projections on bioenergy consumption) to support policy-makers in defining practice-oriented and functional adaptation strategies [83].…”

Section: Natural Areas Conservationmentioning

confidence: 99%

A Pathway towards Climate Services for the Agricultural Sector

Charalampopoulos,

Droulia

2024

Climate

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Climate change is already having a negative impact on many areas of human activity, affecting life globally. It is more urgent than ever to increase our adaptive capacity to respond to current and future climate change risks. Climate services refer to a specialized sector that encompasses both research and operational activities. This sector is primarily focused on interpreting and communicating knowledge and information about climate risks in a manner that is tailored to meet the specific needs of diverse user communities. Climate services offer a range of specialized outputs, including forecasts, assessments, and advisories, which enable users to make decisions that are based on an understanding of the potential impacts of climate change. The outputs of climate services are designed to help diverse user communities effectively manage risks and capitalize on opportunities arising from climate variability and change. An attempt is made to outline the fundamental elements of climate services and point out their contribution to various aspects of human activity, focusing on their essential role in the adaptability of the priority for action agricultural sector, which appears as considerably vulnerable to the change of considerably susceptible to climate conditions. This article is structured to answer basic questions about climate services in general and to show the specificities of climate services in the agricultural sector.

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Section: Natural Areas Conservationmentioning

confidence: 99%

A Pathway towards Climate Services for the Agricultural Sector

Charalampopoulos,

Droulia

2024

Climate

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“…For example, GMOD are inevitably used in the process of weather forecasting [1,2] or climate change analysis [3][4][5]. Additionally, they are an important reference for societal production and life and can be used for disaster warning [6][7][8], agriculture [9,10], forestry [11][12][13], tourism [14,15], marine fisheries [16,17], water conservancy [18,19], transportation [20,21], and other fields. Effective use of GMOD may help stakeholders, including the government, to avoid environmental problems and damages, providing a wide range of social benefits.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data

Li,

Ren,

Zhao

2023

Algorithms

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Ground meteorological observation data (GMOD) are the core of research on earth-related disciplines and an important reference for societal production and life. Unfortunately, due to operational issues or equipment failures, missing values may occur in GMOD. Hence, the imputation of missing data is a prevalent issue during the pre-processing of GMOD. Although a large number of machine-learning methods have been applied to the field of meteorological missing value imputation and have achieved good results, they are usually aimed at specific meteorological elements, and few studies discuss imputation when multiple elements are randomly missing in the dataset. This paper designed a machine-learning-based multidimensional meteorological data imputation framework (MMDIF), which can use the predictions of machine-learning methods to impute the GMOD with random missing values in multiple attributes, and tested the effectiveness of 20 machine-learning methods on imputing missing values within 124 meteorological stations across six different climatic regions based on the MMDIF. The results show that MMDIF-RF was the most effective missing value imputation method; it is better than other methods for imputing 11 types of hourly meteorological elements. Although this paper applied MMDIF to the imputation of missing values in meteorological data, the method can also provide guidance for dataset reconstruction in other industries.

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Archival and Dissemination of Knowledge on Global Warming and Protection of the Arctic

Mamudu

2024

Arctic Marine Ecotoxicology

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Climate data for the European forestry sector: From end-user needs to opportunities for climate resilience

Cited by 4 publications

References 46 publications

A Pathway towards Climate Services for the Agricultural Sector

A Pathway towards Climate Services for the Agricultural Sector

Machine-Learning-Based Imputation Method for Filling Missing Values in Ground Meteorological Observation Data

Archival and Dissemination of Knowledge on Global Warming and Protection of the Arctic

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