Improving Stochastic Modelling of Daily Rainfall Using the ENSO Index: Model Development and Application in Chile

Abstract

Stochastic weather simulation, or weather generators (WGs), have gained a wide acceptance and been used for a variety of purposes, including climate change studies and the evaluation of climate variability and uncertainty effects. The two major challenges in WGs are improving the estimation of interannual variability and reducing overdispersion in the synthetic series of simulated weather. The objective of this work is to develop a WG model of daily rainfall, incorporating a covariable that accounts for interannual variability, and apply it in three climate regions (arid, Mediterranean, and temperate) of Chile. Precipitation occurrence was modeled using a two-stage, first-order Markov chain, whose parameters are fitted with a generalized lineal model (GLM) using a logistic function. This function considers monthly values of the observed Sea Surface Temperature Anomalies of the Region 3.4 of El Niño-Southern Oscillation (ENSO index) as a covariable. Precipitation intensity was simulated with a mixed exponential distribution, fitted using a maximum likelihood approach. The stochastic simulation shows that the application of the approach to Mediterranean and arid climates largely eliminates the overdispersion problem, resulting in a much improved interannual variability in the simulated values.

Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

“…Highest correlation coefficients are found between the ENSO and SPEI for both accumulation periods in March (r = 0.57 for SPEI-6 and r = 0.4 for SPEI-12). These results are in agreement with previous studies showing the high influence of ENSO on the basin [57]. On the other hand, r-values between ENSO and SSI differ only marginally between March and September for both accumulation periods.…”

“…To identify and assess the potential influence of El Niño Southern Oscillation (ENSO) phenomenon on the indexes, we computed the Pearson correlations coefficient between sea surface temperature anomaly values in region Niño 3.4 (ENSO 3.4), obtained from the National Oceanic and Atmospheric Administration (NOAA), and the corresponding SPEI, SSMI, and SSI values. Influences of ENSO on precipitation patterns in several parts in the world are well documented [57]. The influence itself does not describe the propagation process itself, but it can provide useful information on the development of a drought event (e.g., severity during La Nina phase), and further support drought management decisions.…”

Observed trends and relationships between ENSO and standardized hydrometeorological drought indices in central Chile

“…Precipitation is strongly influenced by the Pacific Subtropical High, whose intensity and proximity explains rainfall seasonality (Barrett & Hameed, ; Quintana & Aceituno, ). In addition, the region shows a strong interannual variability, particularly in its Mediterranean and semiarid areas, that has been extensively documented elsewhere (see Aceituno, ; Montecinos & Aceituno, ; Pittock, ; Urdiales, Meza, Gironás, & Gilabert, ).…”

“…In particular, climate change is likely to affect the frequency and magnitude of droughts in the Mediterranean and semiarid regions of the country by the end of the 21 st century (Chadwick, Gironás, Vicuña, Meza, & McPhee, ; Dai, ). In fact, changing patterns in the Chilean climate and the corresponding related impacts were already detected, as temperatures are increasing during spring and summer, overall precipitation is decreasing, and more frequent extreme drought events are occurring (Burger, Brock, & Montecinos, ; Núñez et al, ; Urdiales et al, ; Vicuña et al, ;). Droughts are a recurrent phenomenon causing severe economic losses in agriculture and tourism (Aldunce et al, ).…”

Pronóstico de la ocurrencia de precipitación diaria en un gradiente altitudinal en el Sur del Ecuador mediante un generador climático

“…Los GCs pueden representar efectivamente la variabilidad intrínseca natural (estocástico) de los procesos de precipitación, con prominente enfoque en el análisis de proyecciones de incertidumbres. Además, las simulaciones de GCs son menos sesgadas en climas templados húmedos que en climas secos (Breinl et al, 2017;Urdiales et al, 2018). Por tal motivo, los GCs son prometedores para el mejoramiento del pronóstico de la precipitación en Los Andes tropicales y además su implementación tiene un bajo costo computacional.…”