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SIE‐Climate : A methodological and technological tool for predicting local climate variability in managing socio‐ecological systems
Author(s) -
SierraCárdenas Erika,
UsaquénPerilla Olga,
FonsecaMolano Mauricio,
OchoaEcheverría Mauricio,
DíazGómez Jaime,
Jesus Manuel
Publication year - 2022
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.7277
Subject(s) - environmental science , macro , climatology , range (aeronautics) , time horizon , climate change , climate model , computer science , environmental resource management , ecology , geology , mathematics , mathematical optimization , composite material , biology , programming language , materials science
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; R 2 : 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.