ENSO‐conditioned rainfall drought frequency analysis in northwest Baja California, Mexico

Abstract

Located in northwest Baja California, Mexico, the Guadalupe River Basin is situated in a semiarid region where periods of drought pose serious economic, social and environmental concerns. The study area has highly variable climate, with mean annual precipitation ranging from less than 12 mm to over 750 mm a year across the basin, and with most of the annual precipitation occurring during the autumn and winter seasons. To quantify the frequency and severity of meteorological droughts at the local (watershed) scale, this study investigates seasonal and annual precipitation data from 34 sites in northwest Baja California. Along with the analysis of precipitation climatology and interannual variability, El Niño/Southern Oscillation (ENSO) and related Pacific Ocean sea surface temperature (SST) patterns are shown to be potential predictors of seasonal precipitation. Analysis of precipitation variability at seasonal and annual time scales is performed using the standardized precipitation index (SPI) methodology, and annual, seasonal, and ENSOconditioned precipitation frequency analyses are executed using the regional L-moment algorithm. The SPI and regional rainfall drought frequency estimates developed in this study may be useful for monitoring meteorological droughts and could serve as critical components of a comprehensive drought management plan. Furthermore, the methods applied in this study may be transferred to other semi-arid locations to mitigate drought risk and, potentially, impacts of drier conditions in the 21st century.

Variability and Factors of Influence of Extreme Wet and Dry Events in Northern Mexico

“…The presence of +SST phase anomalies in Figure 7b is an indicator of extreme dry events in northern Mexico. These results are consistent with those of [27,49,55], who point out that the +SST anomalies in eastern tropical Pacific near the equator are indicators of extreme dry events.…”

“…In the results shown in Figure 7a, it can be seen that the presence of −SST phase anomalies in the equatorial and eastern Pacific is an important factor for the occurrence of extreme precipitation in the north of Mexico [27,53,54]. The presence of +SST phase anomalies in Figure 7b is an indicator of extreme dry events in northern Mexico.…”

“…Northern Mexico is no exception, due to the fact that PDO and ONI are associated with precipitation variability in this region [9,[23][24][25][26]. Extreme precipitation events in northern Mexico are also related to anomalies of the sea surface temperature (SST) in the equatorial and eastern Pacific Ocean [27]. Given the association between PDO and ONI, it can be inferred that sea surface temperature has a strong influence on the existence of dry and wet events in northern Mexico, and the goal of the present study was therefore to generate a method to examine seasonal variability by climatic classification and seasonal factors of the Pacific for extreme wet and dry events in northern Mexico for the period 1952-2013.…”

Variability of soil moisture proxies and hot days across the climate regimes of Australia

“…SPI can be calculated for any period but is typically provided at 1, 3, 6, and 12 months. A 3 and 6 month SPI corresponds best to changes in SM due to memory or retention being limited within this period [ Seneviratne et al , ; Hirschi et al , ; Mueller and Seneviratne , ], with 3 month SPI best correlating with SM [ Hallack‐Alegria et al , ; Jung and Chang , ]. Mueller and Seneviratne [] use the 3 month SPI for comparison with Tx90 and heat wave days in which it was found that heat wave days were almost linearly proportional to a decrease in 3 month SPI.…”

Uncertainty of stationary and nonstationary models for rainfall frequency analysis

“…In general, it has been observed that the GEV fits best the annual maximum rainfall data (Adamowski et al ., ; Lee and Maeng, ; Endreny and Pashiardis, ; Eslamian and Feizi, ; Abolverdi and Khalili, ; Cheng and AghaKouchak, ). For the total annual rainfalls, the G, GEV, Log‐Pearson 3 (LP3) and Pearson 3 (P3) are the most frequently used distributions (Öztürk, ; Ben‐Gai et al ., ; Small and Islam, ; Yue and Hashino, ; Gonzalez and Valdes, ; Hallack‐Alegria et al ., ). Figure presents the L‐moment ratio diagrams with the sample moments for each station for the annual maxima and the annual totals.…”