Mapping Environmental and Socioeconomic impacts of hydrometeorological hazards across Central America. Study case: Honduras

Alfaro, Eric J.; Hidalgo, Hugo G.; Briceño, Paula Marcela Pérez

doi:10.15359/peds.3-1.2

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Respecto a los impactos asociados a la sequía, se utiliza la base de datos empleada por Alfaro et al (2018b;c) y Pérez-Briceño et al (2016). Estas contienen información de tres distintas fuentes: DesInventar (http://www.desinventar.or), EM-DAT (http://www.emdat.be) y el Instituto Meteorológico Nacional (http://imn.ac.cr/).…”

Section: Datosunclassified

Asociación entre algunos índices de sequía e impactos socio-productivos en el Pacífico Norte de Costa Rica

2020

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[Introducción]: Las sequías en Costa Rica son un evento hidrometeorológico extremo que afecta a muchos productores agrícolas y a la sociedad en general. [Objetivo]: Identificar cuál índice de sequía logra una relación más fuerte con los impactos asociados a este tipo de acontecimientos extremos en sectores sociales y productivos para tres cantones localizados en Guanacaste, provincia de Costa Rica, ubicada dentro del Corredor Seco Centroamericano (CSC). [Metodología]: Para ello se construyó (e integró) una base de datos de impactos producidos por sequías para el periodo 1970-1999, a partir de diferentes fuentes de información, que incluye Desinventar, EM-DAT, el Instituto Meteorológico Nacional de Costa Rica, así como reportes periodísticos de impactos de otras fuentes. Estos datos se compararon con los siguientes 6 índices mediante una regresión logística: el Índice Normalizado de Precipitación a escalas de 6 y 12 meses (conocidos como SPI 6 y SPI 12 respectivamente), el Índice de Severidad de Sequía y el Índice Hidrológico de Palmer (conocidos como PDSI y PHDI, respectivamente), el Índice de Reconocimiento de Sequía (conocido como RDI) y el Índice Modificado de Anomalía de Lluvia (conocido como mRAI), todos previamente aplicados en América Central. [Resultados]: En general, el índice más apropiado para estudiar la sequía asociada con los impactos sociales y productivos es el SPI para 6 y 12 meses. [Conclusiones]: En todos los cantones estudiados este índice presenta un alta (baja) probabilidad de muchos (pocos) impactos cuando se encuentra en condiciones moderada y severamente secas (húmedas).

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Section: Datosunclassified

Asociación entre algunos índices de sequía e impactos socio-productivos en el Pacífico Norte de Costa Rica

2020

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“…These events are unlikely to abate (Hidalgo et al ., 2013; Hannah et al ., 2017; Imbach et al ., 2018; Almazroui et al ., 2021), which makes extreme weather preparedness an important challenge going forward. Drought and flooding impacts are modulated by many factors, including social and economic conditions (e.g., Perez‐Briceno et al ., 2016; Alfaro, Martínez et al ., 2018). Early warnings based on forecasting do not provide a comprehensive solution to mitigate these impacts but can be one factor to reduce vulnerability when effectively communicated (Braman et al ., 2013; de Perez et al ., 2016; Kreibich et al ., 2017; Golding et al ., 2019; Goddard et al ., 2020; Domeisen et al ., 2022; White et al ., 2022).…”

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confidence: 99%

A comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble

Kowal

Slater

López

et al. 2023

Intl Journal of Climatology

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Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting.

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SEAS5 skilfully predicts late wet‐season precipitation in Central American Dry Corridor excelling in Costa Rica and Nicaragua

Kowal

Slater

Loon

et al. 2022

Intl Journal of Climatology

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Better drought preparedness is critically needed in the Central American Dry Corridor (CADC). Seasonal forecasts can be used to build this preparedness but need localized evaluations to ensure they are relevant and useful. This study provides a CADC-focused assessment of the SEAS5 seasonal forecasting system produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). We evaluate SEAS5 predictions of the mean, variability, and extremes of precipitation across the CADC at 1-7-month lead times. We assess differences in regional forecast quality across seasons and lead times by evaluating spatial and temporal associations with El Niño-Southern Oscillation (ENSO) phase, topography, and continentality. Results show that SEAS5 precipitation forecasts often have better skill primarily during the mid to late wet season (July-October). In these months, low/normal precipitation forecasts outperform the climatological mean up to 5-or 6-month lead times in some subregions. Forecast skill is often worse, however, for predicting precipitation during the early wet season, primarily in June. Forecast skill varies spatially across the region, with higher skill concentrated in the southeast (Costa Rica and Nicaragua). Forecast skill is significantly related to continentality and topography, and together these factors account for at least a quarter of the spatial variance in annual skill at all lead times. Forecast accuracy varies depending on ENSO phase: predictions are often worse in El Niño (warm ENSO) periods during the early wet season when ENSO also has a weaker association with cumulative precipitation relative to the later wet season.SEAS5 could be a particularly useful tool during the second half of the wet season in the southeast CADC, skilfully alerting of upcoming precipitation variability with over 3-month lead times.

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Mapping Environmental and Socioeconomic impacts of hydrometeorological hazards across Central America. Study case: Honduras

Cited by 4 publications

References 23 publications

Asociación entre algunos índices de sequía e impactos socio-productivos en el Pacífico Norte de Costa Rica

Asociación entre algunos índices de sequía e impactos socio-productivos en el Pacífico Norte de Costa Rica

A comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble

SEAS5 skilfully predicts late wet‐season precipitation in Central American Dry Corridor excelling in Costa Rica and Nicaragua

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