SIE‐Climate: A methodological and technological tool for predicting local climate variability in managing socio‐ecological systems

SIE‐Climate: A methodological and technological tool for predicting local climate variability in managing socio‐ecological systems

Climate variability, as an element of uncertainty in water management, affects community, sectoral, and individual decision‐making. Long‐range prediction models are tools that offer the potential for integration and joint analysis with the hydrological, hydrodynamic, and management response of the s...

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Bibliographic Details
Published in:International journal of climatology Vol. 42; no. 2; pp. 868 - 888
Main Authors: Sierra‐Cárdenas, Erika, Usaquén‐Perilla, Olga, Fonseca‐Molano, Mauricio, Ochoa‐Echeverría, Mauricio, Díaz‐Gómez, Jaime, Jesus, Manuel
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 01-02-2022
Wiley Subscription Services, Inc
Subjects:
Algorithms
Anomalies
Climate
Climate models
Climate prediction
Climate variability
Climatic indexes
climatic variability
Computer applications
Decision making
Ecology
Hydrologic analysis
Hydrology
K‐nearest neighbours (KNN)
Lakes
Local climates
Prediction models
Rain
Rainfall
SIE‐climate software
Social-ecological systems
Variability
Water management
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Summary:Climate variability, as an element of uncertainty in water management, affects community, sectoral, and individual decision‐making. Long‐range prediction models are tools that offer the potential for integration and joint analysis with the hydrological, hydrodynamic, and management response of the socio‐ecological systems to which they are linked. The main objective of this article is to present a seasonal climate prediction model, the open‐source algorithm SIE‐Climate, whose application consists of three phases (exploration, development, and evaluation), and to describe its application to the Lake Sochagota socio‐ecological system (Paipa, Boyacá, Colombia). The K‐nearest neighbours method is used when defining a target matrix that represents and integrates macro‐ and micro‐climatic phenomena (Oceanic Niño Index, local temperature, and local rainfall) to identify periods of similar climatic behaviour. Considering a 1‐year horizon and management purposes the tool is calibrated and validated in periods with and without climatic anomalies (2000–2018), giving reliable adjustment results (RSME:4.86; R2: 0.95; PBIAS: −8.89%; EFF: 0.85). SIE‐Climate can be adapted to various contexts, variables of interest, and temporal and spatial scales, with an appropriate technological and computational cost for regional water management. Long‐range prediction models are tools that offer the potential for integration and joint analysis with the hydrological, hydrodynamic, and management response of the socio‐ecological systems to which they are linked. The K‐nearest neighbours method is used when defining a target matrix that represents and integrates macro‐ and micro‐climatic phenomena. The open‐source algorithm SIE‐Climate can be adapted to various contexts, variables of interest, and temporal and spatial scales, with an appropriate technological and computational cost for regional water management.
Bibliography:Funding information
Science, Technology and Innovation Fund (Fondo de Ciencia, Tecnología e Innovación ‐ FCTel), Grant/Award Number: FP44842‐293‐2018
ISSN:0899-8418
1097-0088
DOI:10.1002/joc.7277