Next Article in Journal
An Integrated Framework to Assess the Environmental and Economic Impact of Fertilizer Restrictions in a Nitrate-Contaminated Aquifer
Previous Article in Journal
Assessing the Performance of a Citizen Science Based Water Quality Monitoring Program for Nitrates Using Test Strips Implemented in the Medjerda Hydrosystem in Northern Tunisia
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Hydrology
Volume 11
Issue 1
10.3390/hydrology11010007
hydrology-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analysis of Anomalies Due to the ENSO and Long-Term Changes in Extreme Precipitation Indices Using Data from Ground Stations

by
Luis Alberto Vargas-León
and
Juan Diego Giraldo-Osorio
*
Research Group Ciencia e Ingeniería del Agua y el Ambiente, Civil Enginnering Deparment, School of Enginnering, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
*
Author to whom correspondence should be addressed.
Hydrology 2024, 11(1), 7; https://doi.org/10.3390/hydrology11010007
Submission received: 12 July 2023 / Revised: 31 October 2023 / Accepted: 1 November 2023 / Published: 1 January 2024
Browse Figures
Review Reports Versions Notes

Abstract

:
In this work, the influence of the El Niño Southern Oscillation (ENSO) on the Extreme Precipitation Indices (EPIs) was analyzed, and these ENSO-forced anomalies were compared with the long-term change in the EPIs. The annual time series of the EPIs were built from 880 precipitation stations that contained daily records between 1979 and 2022. These daily time series were filled, then the eleven (11) annual time series of the EPIs were built. To calculate ENSO-driven anomalies, the several phases of the phenomenon were considered (i.e., warm phase or El Niño years, cold phase or La Niña years, and normal or neutral years). For a particular EPI, the values calculated for the extreme phases of the ENSO were grouped, and these groups were compared with the group made up of the EPI values for the neutral years. To calculate the long-term change, two periods (1979–1996 and 2004–2021) were considered to group the EPI values. Maps showing the magnitude and significance of the assessed change/anomaly were constructed. The results allowed us to identify that the EPIs are generally “wetter” (i.e., higher extreme precipitation, longer wet periods, shorter dry periods, etc.) during La Niña hydrological years, while the opposite changes are observed during El Niño years. Furthermore, ENSO-induced anomalies are more important than the long-term changes.

1. Introduction

In South America, climate variability results from the superposition of several phenomena with different time scales, such as the Atlantic Multidecadal Variability (AMV), the Pacific Multidecadal Variability (PMV), the Intertropical Convergence Zone (ITCZ), the El Niño Southern Oscillation (ENSO), and the South Atlantic Convergence Zone (SACZ), among others [1,2,3,4,5]. One of the ways in which these phenomena manifest is high variations in temperature and precipitation. These events are the result of the high spatiotemporal variability of precipitation and temperature, and are displayed through extreme precipitation events or prolonged periods of water deficit that cause droughts [6,7,8,9].
The sixth assessment report of the Intergovernmental Panel on Climate Change (AR6-IPCC) concluded that the frequency of extreme precipitation events has increased in many regions of the world, even in regions where total annual precipitation has decreased [8]. Extreme precipitation events cause an increase in the frequency and magnitude of flooding, crop loss and/or major socioeconomic impacts [10,11], soil erosion, and landslides [12,13,14,15]. One of the causes of the change in the magnitude and frequency of these events is associated with human-induced climate change, especially global warming, despite the fact that these changes vary depending on each location [8,16]. Due to the importance of accurately understanding and modeling extreme events, the IPCC expert team considered a basic set of 27 climate change indices for the study of extreme temperature and precipitation events [17], which have been applied in different studies. For example, in India, Kumar et al. [18] analyzed the spatiotemporal variation in precipitation and temperature using eight indices of climate extremes computed from high-resolution data from the Meteorological Department and products of global gridded reanalysis; in Zacatecas, Mexico, Pita-Diaz et al. [19] carried out an analysis of anomalies and trends of the indices, finding increases in the maximum temperature and lower values in the minimum temperature, as well as strong variability in precipitation, which they supposed could be associated with the Pacific Decadal Oscillation and the El Niño Southern Oscillation. In addition, fifteen extreme temperature indices and ten extreme precipitation indices were analyzed for thirteen countries in Western Africa [20]; a significant trend towards wetter conditions was found in the 1981–2010 period, but this period followed a decade with extreme dry conditions, so it is unclear whether these trends represent a long-term response to a warming or a recovery from the dry period [20]. There have been other studies on extreme indices carried out in recent decades in different parts of the world [21,22,23,24,25,26,27,28].
Global climate is strongly influenced by the El Niño Southern Oscillation (ENSO) [29,30,31,32], a quasi-periodic phenomenon that presents two extreme phases: a warm phase (El Niño) and a cold phase (La Niña). El Niño and La Niña are related, respectively, to positive and negative anomalies in the sea surface temperature (SST) of the Tropical Pacific Ocean (TPO; [33,34,35]). In its warm phase, the ENSO causes droughts in the western margin of Central America, Mexico, the Amazon Basin and northern South America (i.e., Colombia, Venezuela, and northeastern Brazil), and at the same time causes excessive rainfall in the eastern region of Central America and an increase in rainfall in the Panamanian Basin and the Andes of Peru, Bolivia, and Chile [36]. On the other hand, during the cold phase (La Niña), the effects are inverted in those areas. Studies have mentioned that the ENSO could modulate the regional climate variability in the tropics, causing abnormal atmospheric circulation and exerting significant impacts on the climate [37], and consequently causing extreme events [16].
Even so, ENSO events are not just limited to El Niño and La Niña. In recent decades, different types of the ENSO (called by some scientists “the ENSO flavors” [38,39,40]) have been distinguished: the Eastern Pacific ENSO (EP-ENSO; also called canonical ENSO or the “traditional” ENSO that we described in the previous paragraph) is the ENSO that presents SST anomalies that are the strongest in the far eastern TPO. However, the Central Pacific ENSO (CP-ENSO; also called the Modoki ENSO, the date line ENSO, etc.) is characterized by the fact that these SST anomalies are confined to the central TPO [39,41,42,43]. For example, the CP-El Niño occurs when positive SST anomalies are confined to the central TPO, and these warm waters are flanked by colder waters to the East and West. Associated with these different patterns in SST anomalies, the CP-ENSO teleconnections are usually different from those of the EP-ENSO, so the ENSO events vary in intensity and duration and produce different impacts around the planet [39,41]. Some attempts have been made to relate the precipitation of the DEF quarter in the Colombian territory to the different ENSO flavors [44].
In turn, we found in the bibliographic review that although progress has been made in detecting ENSO Modoki events (either El Niño Modoki or La Niña Modoki), their identification and discrimination with respect to the canonical ENSO is still challenging [39,43,45]. However, the same bibliographic review led us to find works that maintain that this challenge (of forecasting ENSO flavor, amplitude, and impacts) can be overcome if we “immerse” under the waters of the Tropical Pacific Ocean. The work of Pinault (2018; [46]) indicates that it is possible to anticipate the amplitude and date of occurrence of ENSO events, but they indicate that sub-superficial sea temperature (SubST) should be monitored in the central TPO, instead of monitoring the ENSO using the SSTs of vast regions of the ocean (which, as they claim, is much more complex). The paper indicates that, by observing the interaction between the baroclinic quasi-stationary waves (QSWs) in the TPO, it is possible to determine whether an upcoming El Niño event will be a CP-El Niño or an EP-El Niño. As [46] said, ENSO events can be classified according to the time lag of their occurrence, based on the observation of the quadrennial QSW. Meaning, the expected date of the ENSO event’s occurrence, compared to a regular four-year cycle. According to their results and conclusions, almost all CP-ENSO events occur exclusively during the first semester every two years, and no EP-ENSO events can occur at the same time. So, according to [46], the ENSO start date could be forecasted between 7 and 8 months in advance, together with its potential impacts. In the same way, the work of Chen and Magda (2016; [39]) simulated the behavior of the TOP through a simple dynamical model that captured the main characteristics of the CP-El Niño. However, these simple approaches to ENSO prediction or modeling are met with complex behaviors: for example, Freund et al. (2019; [41]) found that CP-El Niño events have become more frequent in recent decades, which departs from their historical behavior. The authors raise hypotheses about the causes of this behavior of ENSO events in recent centuries, and climate change appears among them. Finally, the usefulness of data-driven approaches for forecasting ENSO events has been demonstrated, for example, by the work of Liang et al. (2021; [45]) and Pal et al. (2020; [43]). Furthermore, careful work with real-time data and historical observations of SST, atmospheric pressure and wind speed, among other variables, has made it possible to identify the early signals of ENSO and prevent its potential impacts by understanding the ENSO teleconnections. Regardless of the current advances in ENSO forecasting, the recent work of Maher et al. (2023; [47]) shows high uncertainty in relation to the ENSO’s future behavior. In this paper, the authors managed to isolate the time-dependent response of ENSO variability to the increase in greenhouse gases. However, some models used in the ensemble presented behaviors that did not coincide with the consensus trend observed in the other models. The non-linear time-dependent behavior of ENSO SST variability is highlighted, together with changes in the Tropical Pacific SST gradient that intensifies towards the end of the 21st century. These changes in the Tropical Pacific Ocean SST behavior would have important implications for teleconnections and ENSO impacts [47].
Specifically, northern South America is characterized by high rainfall, high humidity, and high temperatures that prevail in the equatorial region [29]. Colombia has been strongly affected by ENSO on several occasions, generating great socioeconomic impacts during the occurrence of extreme phases of ENSO [48], particularly in the years 2010–2011 (La Niña) and 2014–2016 (El Niño). La Niña 2010–2011 affected approximately 4 million people, and caused losses of around USD 7.8 billion, associated with the destruction of infrastructure, flooding of agricultural land, and payment of government subsidies [11]. However, during the El Niño event that occurred between 2014 and 2016, the highest temperature records since the beginning of the measurements in 1950 were observed, generating the occurrence of forest fires and low levels of flows of the principal rivers of the Andean and Caribbean regions [49]. According to UNGRD (Unidad Nacional para la Gestión del Riesgo de Desastres—a national administrative office for disaster risk management), 358 public calamities were reported, 187 due to partial shortages and water rationing, 71 due to fires, and 100 between the agricultural sector and others. Likewise, during the contingency plan for the dry season during the El Niño event, around COP 1.6 billion (about USD 500M) were invested [49].
These events have been the strongest on record in Colombia. However, the nature of the disasters is closely related to the geographical characteristics of each region [50,51,52]. For example, the Colombian Andean zone is highly vulnerable to landslides due to the combination of different factors, including prolonged and intense rains in the region [12], which have led to large mass movements, floods and/or torrential floods, which have caused many deaths [13,53]. Between 1920 and 2017, the Andean region was the most affected by torrential floods, with 72% of the records and 80% of the fatalities [54]. There were intense floods in the Caribbean region, specifically in Riohacha city (the principal city of the La Guajira region, the northernmost of Colombia), in 2010–2011, despite being one of the driest regions in the country [55,56]. In Villavicencio city, in the Orinoco region, extreme floods affected the urban and rural populations with heavy economic losses [57]. This is the same condition as has been seen the Colombian Pacific region, characterized by high precipitation levels. In this context, the study of climate variability in Colombia becomes relevant due to the increase in the frequency and intensity of extreme hydroclimatic events in recent decades [33,35,58,59,60,61,62,63,64]. The ENSO is the dominant phenomenon that affects the climatic variability of the Colombian territory in its two extreme phases, El Niño and La Niña [33,65,66], which could modulate the circulation patterns and therefore affect extreme precipitation [67].
Understanding the spatiotemporal changes in extreme precipitation on a regional scale is of great importance for policymakers and the general public [68], because, in the current research, there is still uncertainty about the influence of ENSO [37,48,69]. On the other hand, for the Colombian territory, different studies have been carried out on the long-term trends of the average precipitation values and temperature, finding changes in precipitation between −4% and 6% per decade, although the areas with decreasing/increasing trends are different [58,59,61,64,70,71]. For example, precipitation reduction in areas of the Andean region (valleys of the Magdalena and Cauca rivers) and the Caribbean have been observed. Conversely, positive trends were observed on the foothills of the eastern side of the Eastern Cordillera, and in the north of the Pacific region.
Given the need to advance the study and knowledge of the influence that the ENSO has on extreme precipitation events, this study wants to answer the question: Which changes are more relevant to the extreme precipitation indices (EPIs), the climate variability anomalies driven by the ENSO or the long-term changes? So, the main objective of this study is to analyze the eleven extreme precipitation indices over Colombia from two perspectives: first, the long-term change of the EPIs, and second, the inter-annual variability of the EPIs, taking into account the ENSO phases.

2. Materials and Methods

2.1. Study Zone

Colombia is located in the northwest corner of South America, between the coordinates 4° S–12° N and 79–66° W, so the country has coasts on both the Atlantic and the Pacific oceans. The country can be divided into five hydrographic areas, which are cataloged by the IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales; the Colombian national institute devoted to research on hydrology, meteorology, and environmental studies), such as the Caribbean, Pacific, Andean, Orinoco and Amazonas [70]. Colombia has a mountainous topography (Figure 1) with strong climatic variability, derived from its tropical environment and the fact that the Andes Mountains cross the territory in a SW–NE direction, divided into three branches (i.e., western, central, and eastern mountain ranges of Colombia) [63]. The annual rainfall regime in the country is divided into four cycles: bimodal, unimodal, mixed, and seasonal [72]. The bimodal regime predominates in the Andean region due to the mountainous areas and the double passage of the ITCZ (Intertropical Convergence Zone), while the unimodal regime predominates on the eastern plains (the Orinoco region, plus the Amazon region), and the Caribbean region in the north. On the other hand, in some regions of the Pacific and Caribbean, there are areas with both unimodal and bimodal (mixed) regimes, and seasonal regimes, due to the alternating supply of moisture [72,73].

2.2. Data

Daily precipitation data from ground stations were obtained from the IDEAM. Initially, the information from all the stations with daily precipitation records in the country was obtained from the Information System for the Management of Hydrological and Meteorological Data (DHIME), building a database of daily precipitation series of 3868 stations spread throughout the country between 1920 and 2022. However, considering that the daily data continuity in the station is not guaranteed (i.e., data gaps in the daily records were widespread), a first stage was carried out, which consisted of selecting stations with more than thirty years of continuous data, with at least 80% of the daily information from 1979 to 2022. In this way, 880 stations were selected between 1979 and 2022. This availability can be seen in Figure 2, and the location of the chosen stations can be seen in Figure 1a.
The variable total precipitation, derived from the ERA5 global climate reanalysis, was used to fill the missing data, or gaps, in the daily time series given by the ground stations. This variable has a spatial resolution of 0.25°, and an hourly temporal resolution [74]. Then, the daily precipitation given by ERA5 was calculated to match the temporal resolution of the ground stations.

2.3. Methodology

To address the objective of the study, the methodology followed is summarized in Figure 3.
The main tasks outlined in the flowchart are:
  • Construction of the time series of extreme precipitation indices (EPI) in the study area. The resulting EPI time series will have a yearly time resolution;
  • Interannual variability analysis—The Mann–Whitney–Wilcoxon test was used to analyze the anomalies during El Niño and La Niña years against normal (or neutral) years;
  • Analysis of the long-term behavior of EPI—The Mann–Whitney–Wilcoxon test was used to assess whether two samples from different periods taken from the EPI time series belonged to the same population.

2.3.1. Missing Data Filling Using Bias Correction: Quantile Mapping

Quantile mapping is used to correct the probability distribution function (pdf) of the values simulated by the climate model being similar to the distribution of the observed values. The previous procedure can be undertaken by creating a transfer function that performs the transformation [75]. The quantile mapping approach develops a statistical relationship between the observed values’ distribution and the model values’ distribution, so the model-corrected values have the same pdf as the observed values. In this way, the gaps in the observed data record can be filled with the model-corrected values, which are supposed to preserve the observed data’s statistical parameters. This methodology has been used in several studies to correct the simulated data in climate models [76,77,78,79,80].
For each time series observed daily at the gauge stations (which have missing data that must be filled), the corresponding ERA5 daily time series of precipitation (which is supposed to be free of data gaps) was selected. Because of the strong precipitation seasonality in Colombia, the cumulative distribution functions (CDF) for both time series were built for each day of the year. Then, the observed precipitation CDFs ( F O B S − D A Y . 1 , F O B S − D A Y . 2 , ⋯ , F O B S − D A Y . N , ⋯ , F O B S − D A Y . 365 ), and the corresponding ERA5 precipitation CDFs ( F E R A 5 − D A Y . 1 , F E R A 5 − D A Y . 2 , ⋯ , F E R A 5 − D A Y . N , ⋯ , F E R A 5 − D A Y . 365 ), were obtained. Finally, if a particular date of the observed time series contained a gap (i.e., P O B S − D A Y . N = N A ; where D A Y . N corresponds with any day of year N ), it would be filled with the corresponding pair of CDFs (i.e., F O B S − D A Y . N , and F E R A 5 − D A Y . N ), in the following way:
P O B S − D A Y . N * = F O B S − D A Y . N − 1 F E R A 5 − D A Y . N P E R A 5 − D A Y . N
where P E R A 5 − D A Y . N is the ERA5 precipitation value for a day of year N (when the corresponding P O B S − D A Y . N = N A is a missing data point), and P O B S − D A Y . N * is the precipitation value estimated to fill the gap.

2.3.2. Extreme Precipitation Indices (EPI)

Once the observed daily precipitation time series were filled, the eleven (11) indices associated with extreme precipitation events were calculated. The EPI were proposed by the WMO (World Meteorological Organization) and the ETCCDI (Expert Team on Climate Change Detection and Indices) [17]. In this work, the EPI were classified into three categories according to the calculation algorithm [81] (see Table 1).
For the calculation of the EPI, daily precipitation data are required. The EPIs were calculated with a yearly time resolution. However, the hydrological year used for the EPIs calculation generally matched the calendar year (i.e., from 1 January to 31 December). Nonetheless, in this research, the hydrological year was modified for the EPI calculation; that is, the hydrological year was considered from June 1 of a particular year to 31 May of the following year. In this way, the hydrological year matches up with the development of the ENSO extreme phases. The results of this procedure allowed the construction of the annual time series of the EPI between 1979 and 2022, beginning on 1 June 1979 and ending on 31 May 2022. For analysis purposes, they are named with the beginning year; for example, the hydrological year 1 June 1979–31 May 1980 will be 1979, and 1 June 2021–31 May 2022 will be 2021. The indices were calculated using the R package “climdex.pcic” [82].

2.4. Mann–Whitney Wilcoxon Statistical Test

The Mann–Whitney Wilcoxon (MWW) is a non-parametric statistical test. The MWW’s null hypothesis establishes that, for randomly selected values X i and Y j from two samples X = X 1 , X 2 , ⋯ , X i , ⋯ , X n x and Y = Y 1 , Y 2 , ⋯ , Y i , ⋯ , Y n y , the probability that X i is greater than Y j equals the probability that Y j is greater than X i [83]. This work used the MWW test to evaluate whether two independent samples, X and Y , were drawn from populations with the same distribution. The data can be both ordinal and continuous variables. This characteristic of the MWW test makes it very suitable for our analyses because the EPIs can be a continuous variable (for example, R x 1 d a y ), or an ordinal variable (for example C W D ). The MWW test hypotheses are that the two populations are identical (null hypothesis H0), or that the two populations are not the same (alternative hypothesis H1).
The MWW test involves the calculation of the U statistic, whose distribution is known under the null hypothesis (H0). The first step of the test is to gather both samples, X and Y , into a single data set, and then order the values in that new set from the smallest (assigned to the rank 1) to the largest (given to the rank N , where N = n x + n y is the sample size; n x and n y are, respectively, the sizes of the samples X and Y ). Then, the ranks are added for each sample (i.e., R X is the sum of the ranks of sample X , while R Y is for the sample Y ). Finally, expressions in Equations (2) to (4) are used to calculate the U statistic.
U X = R X − n x . n x + 1 2
U y = R y − n y . ( n y + 1 ) 2
U = m i n ( U X , U y )
This test has been used to analyze anomalies in the EPIs computed from the CHIRPS satellite precipitation estimate [48]. Running the MWW test requires that the EPI time series be divided into different sets that must show specific characteristics. For example, to assess the climatic variability of the EPI, the Oceanic El Niño Index (ONI) was used to classify hydrological years according to the ENSO phases: El Niño years (NOY), or warm phase; La Niña years (NAY), or cold phase; and normal or neutral years (NOR). Those groups were the sets used to perform the MWW test.

2.4.1. Climatic Variability of EPI

The ONI is taken as a basis for analyzing anomalies due to climate variability given by the ENSO. The ONI is the main indicator of the NOAA (National Oceanic and Atmospheric Administration) used to classify the ENSO [84]. ONI classifies the ENSO events (El Niño years (NOY) and La Niña years (NAY)) as weak, moderate, strong and very strong events. In this work, the ENSO events’ classification is set according to that used in [48], where El Niño/La Niña events must be moderate, strong or very strong (the weak events are considered as normal years (NOR)). Then, the different sets used to analyze the variability of the EPI are the following:
  • NAY—1988, 1995, 1998, 1999, 2007, 2010, 2011, 2020, 2021;
  • NOY—1982, 1986, 1987, 1991, 1994, 1997, 2002, 2009, 2015;
  • NOR—1979, 1980, 1981, 1983, 1984, 1985, 1989, 1990, 1992, 1993, 1996, 2000, 2001, 2003, 2004, 2005, 2006, 2008, 2012, 2013, 2014, 2016, 2017, 2018, 2019.

2.4.2. Long-Term Change of EPI

To evaluate the long-term changes (LTC), we obtained two samples from different periods taken from the EPI time series [85]. The first period is between 1979 and 1996, and the second is 2004–2021.
The R package “Wilcox.test” was used [86] to carry out the MWW test. Maps of p-values were obtained from the MWW test. In these maps, the smaller the p-values, the more different the distributions are between the compared samples. Nevertheless, the test does not say anything about the sign of the anomaly/change, so anomaly/change maps were computed using the average values in those selected years:
-
EPI anomalies in NAY: ∆ E P I N A Y = E P I ¯ N A Y − E P I ¯ N O R ;
-
EPI anomalies in NOY: ∆ E P I N O Y = E P I ¯ N O Y − E P I ¯ N O R ;
-
EPI long term change: ∆ E P I L T C = E P I ¯ 2004 − 2021 − E P I ¯ 1979 − 1996 .
Finally, the absolute maximum value between NAY anomaly, NOY anomaly, and long-term change was identified for each EPI. Following that, a fourth map was drawn to recognize which was the most important among inter-annual variability and long-term change.

3. Results

3.1. Filling of Missing Data with ERA5

Bias correction using quantile mapping was applied. In this way, the pdf of the ERA5-simulated values and the pdf of the observed values at each station were built to assess the transfer function in each day of the year. Figure 4 shows both precipitation time series for a station (i.e., observed values and ERA5-simulated values) and the outcome of the bias correction on a specific day. This method was applied to fill in the empty data of the 880 selected stations.

3.2. Extreme Precipitation Indices (EPI)

Once the daily time series of precipitation for the 880 selected gauge stations were filled, the EPI yearly time series were built between 1979 and 2022, starting on 1 June 1979 and ending on 31 May 2022 (the time series length is 42 years). In Figure 5, an example of these constructed series is shown, in which ENSO phases are also identified.

3.3. Inter-Annua Variability or Long-Term Change: Which Is More Important?

Once the EPI time series had been built, the Mann-Whitney Wilcoxon (MWW) test was performed as described in Section 2.4. The maps representing the anomalies in each of the EPI can be seen in Figure 6, and in the Figure A1, Figure A2, Figure A3, Figure A4, Figure A5, Figure A6, Figure A7, Figure A8, Figure A9 and Figure A10 (Appendix A). Each figure has four sections: (a) anomalies in La Niña years; (b) anomalies in El Niño years; (c) long-term change; and (d) maximum absolute difference between anomalies, or long-term change. The direction of change was estimated using the mean value of each sample (i.e., ∆ E P I N A Y = E P I ¯ N A Y − E P I ¯ N O R ; ∆ E P I N O Y = E P I ¯ N O Y − E P I ¯ N O R ; and ∆ E P I L T C = E P I ¯ 2004 − 2021 − E P I ¯ 1979 − 1996 ). The dispersion of the anomalous values in NAY ( ∆ E P I N A Y ) is presented in Table 2; the distributions of anomalies in NOY ( ∆ E P I N O Y ) are shown in Table 3; finally, the dispersion of long-term change ( ∆ E P I L T C ) is shown in Table 4.
The stations whereat the frequency-related EPI values (i.e., R10mm, R20mm, and R50mm) show the main anomalies in NAY are in the Andean region. Significant negative anomalies in these EPIs are not common. Nonetheless, during NAY, 61% of the stations showed significant positive anomalies for R10mm, 70% for R20 mm, and 69% for R50mm. However, during NOY, R10mm, R20mm and R50mm showed significant negative anomalies in 36%, 23%, and 8% of the stations, respectively.
The intensity-related EPI values (i.e., Rx1day, Rx5day, SDII, RP95pTOT, R99pTOT, and PRCPTOT) show significant positive anomalies in NAY at between 57 and 72%. During NOY, these EPIs are only present at between 2 and 7% of stations with significant negative anomalies (but 39% of the stations show significant negative anomalies for PRCPTOT during NOY, which almost reflects the situation in NAY).
In NAY, the EPI CDD shows significant negative anomalies in 342 stations (39%), while in NOY, 227 stations (26%) present significant positive changes. Regarding CWD, during NAY, 642 stations (73%) showed significant positive anomalies, while only 78 stations (9%) presented negative anomalies during NOY.
In the long term, all EPIs show their preferred direction of change to be negative (except for CDD, where the preferred change is positive). However, the long-term changes of all EPI values mean the same thing: in the long term, most precipitation stations present “drier” extreme indices. The stations that showed significant negative changes are between 7% (Rx1day) and 20% (SDII).
Frequency-related EPI maps can be seen in Figure 6 (R10mm), Figure A1 (R20mm), and Figure A2 (R50mm). These maps show significant positive anomalies during NAY, and significant negative anomalies during NOY. Nevertheless, no specific direction of the change is observed for LTC. For the three cases, the change in the EPI R10mm is the most considerable, and it is more noticeable over the mountain ranges and in some stations of the Caribbean region. Figure 6d, Figure A1d, and Figure A2d show that the inter-annual variability driven by ENSO is more important than the long-term change; for R10mm, 515 stations present the most important anomalies in NAY, and 206 stations in NOY. For R20mm, 472 stations show the greatest anomalies in NAY, and 195 stations in NOY; for R50mm, 400 stations show the most important anomalies in NAY, and 210 stations in NOY. However, this result for R50mm must be analyzed carefully because days with precipitation greater than 50mm are uncommon for all stations.
The intensity-related EPI maps can be seen in Figure A3 (Rx1day), Figure A4 (Rx5day), Figure A5 (SDII), Figure A6 (R95pTOT), Figure A7 (R99pTOT), and Figure A8 (PRCPTOT).
Concerning Rx1day (Figure A3) and Rx5day (Figure A4), positive anomalies can be seen during La Niña years in a large portion of the Andean, Caribbean, and Pacific region. However, there is no spatial coherence throughout these regions. On the other hand, the anomalies are not spatially coherent during NOY in the Andean region. However, the south of the Caribbean region shows a significant negative anomaly in NOY. In the long term, significant positive and negative changes are scattered in the mountainous area of the Andean region (i.e., there is no spatial coherence in the change), and no statistical significance is shown.
In the long term, the EPI SDII (Figure A5) presents positive and negative significant changes; however, regardless of the significance of the change, the change is not spatially coherent (i.e., the stations with positive changes are surrounded by stations with negative changes). For NAY, there are mainly positive anomalies, despite a few points presenting significant anomalies (positive or negative). In NOY, there is no good spatial coherence of the anomalies. Finally, for this EPI, the long-term change looks to be more important than the inter-annual variability; however, the results are shared out among the precipitation stations used.
The EPI R95pTOT (Figure A6) shows non-significant positive anomalies during La Niña years, and during El Niño years the opposite happens. In the long term, negative significant changes predominate. The EPI R99pTOT (Figure A7) does not show spatial coherence in the anomalies in any of the ENSO extreme phases; over the same scale, there are significant negative and positive changes in the western and eastern mountain ranges of Colombia.
For the EPI PRCPTOT, Figure A8 shows significant positive changes during La Niña and significant negative changes during El Niño. In the long term, there are significant negative and positive changes, without spatial coherence. The influence of the inter-annual variability driven by ENSO is notorious, with 533 stations showing the greatest anomalies in NAY, and 198 stations in NOY.
For the EPI CDD (Figure A9), a significant negative change in NAY is evident along the Andean mountain range, and a significant positive change is observed in NOY. For the LTC, there are some stations in the Andean, Pacific and Orinoquia regions showing significant positive and negative changes. In general, during NOY, dry periods tend to be longer, while during NAY, dry periods tend to be shorter.
For the EPI CWD (Figure A10), it is evident that in the Andean region, there are significant positive anomalies for NAY, and for NOY there are negative anomalies (but fewer stations show significant negative anomalies). For the LTC, there are several stations with significant positive or negative changes, but they do not show spatial coherence.
For the duration-related EPI, the greater influence of the inter-annual variability driven by ENSO is clearly shown, especially for the EPI CDD, with 377 stations showing the greatest anomalies in NAY, and 384 stations in NOY.

4. Summary and Discussion

The result of each EPI depends on whether the calculation is evaluated through the inter-annual variability driven by ENSO, in each of its phases (El Niño years (NOY), or warm phase; La Niña years (NAY), or cold phase; and normal or neutral years (NOR)), versus long-term changes. In order to have an idea of the behavior of the EPIS anomalies, and also to be able to answer the research question (which changes are more important in the extreme precipitation indices—the anomalies due to inter-annual variability driven by ENSO, or the long-term changes?), in Figure 7 the distribution of changes for each EPI is shown, from which we can conclude:
  • Extreme precipitation indices tend to be “wetter” during the cold ENSO phase (La Niña years), while they are “drier” during the warm phase (El Niño years);
  • The long-term change presents smaller magnitudes than the anomalies driven by ENSO;
  • In conclusion, for all EPI, the inter-annual variability driven by ENSO is more important than the long-term change in the period 1979–2022.
The vast majority of research in the Colombian territory has been focused on the Andean Region (because it is the region that has the best precipitation station coverage). In this work, we have tried to include information from other natural regionsof Colombia; however, there is still a deep lack of information for the eastern plains (Orinoquia + Amazonian regions of Colombia).
This research is in agreement with the information reported in other studies [33,35,58,59,60,61,62,63,64,69,72] regarding the influence of ENSO on Colombian precipitation: that is, more rain in NAY, and drier conditions in NOY, such as can be observed in the results obtained from PRCPTOT in terms of positive and negative anomalies during the ENSO phases. In the same way, other studies that have been carried out with satellite precipitation estimates in the northern region of South America show positive anomalies during NAY [87,88].
The region of Chocó (west of Colombia, close to the Pacific Ocean shoreline) is the rainiest area in the country, and one of the wettest in the world. There is a long-term trend towards an increase in wet periods and a decrease in dry periods, coinciding with what has been mentioned by other studies [48,73].
For SDII in NOY, there is no defined spatial coherence in the anomalies. The stations with positive/negative anomalies are almost split 50–50%. This was the only EPI that approached 50% of stations showing negative anomalies in NOY, given that for the other EPIs, more than 60% of the stations showed negative anomalies in NOY.
Once there is sufficient information on land, future research is required to carry out evaluations over a greater number of years, since the study period here is relatively short, especially for the evaluation of long-term change. The results of the MWW test may be debatable due to the short period of time for which it was conducted, since there are only nine (9) NOY, seven (7) NAY, and twenty-five (25) NOR. Further studies could confirm the importance of the inter-annual variability driven by ENSO.
It is also proposed to carry out further research by region, and study the relationship between EPI behavior and the altitude of the stations. Maybe the changes seen at the high mountain stations are more important (in percentage) than the changes seen at the stations in the lower plains, or those seen close to the Atlantic or Pacific shoreline. However, these changes are still hidden by the great amount of rain in the Colombian lowlands. Moreover, this research could be improved by merging ground information and satellite information. This would be especially useful for those areas that lack precipitation data. Comparing these databases of results would confirm the continuity of the observed trends in those areas without information.
Thanks to the reviewers’ comments, a very interesting research line has been opened, addressing the relationship between climatic extremes and “the ENSO flavors”. Although progress has been made in detecting ENSO Modoki events (either El Niño Modoki or La Niña Modoki), we have inferred from the literature review that their identification and discrimination with respect to canonical ENSO is still challenging. Following the procedures proposed by several works [89,90,91,92], we divided the entire 43-year period of the study into several ENSO types, and found that the nine (9) El Niño years of our study could be split into seven (7) EP-NOY and two (2) CP-NOY, and the nine (9) La Niña years comprise three (3) EP-NAY and six (6) CP-NAY years. According to the ENSO flavors, the split samples are too small, so the method presented to evaluate the groups’ similarities/differenced would not support statistical analysis. This type of analysis requires a set of sufficiently long time series of precipitation extremes that, unfortunately, does not exist in the territory of Colombia. However, we propose using reanalyzed data to compensate for this lack of information in the territory. The longer time series would allow us to observe the proposed relationship between precipitation extremes and “the ENSO flavors”.
Finally, the conclusions given in [46] should be carefully revised and related to our results—in particular, the relationship between the Pacific Ocean subsurface temperature and the occurrence of climatic extremes. If this relationship were made strong enough, it would become a truly useful tool for decision-making and design in the context of advanced mitigation measures to face the ENSO events impacting Colombia.

Author Contributions

All authors contributed to the study’s conception and methodological design. Data preparation, data analysis, and writing the original draft was done by L.A.V.-L.; J.D.G.-O. guided the methodology. J.D.G.-O. helped in reviewing the manuscript, data preparation and analysis. J.D.G.-O. was the study’s supervisor. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Date are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Hydrology 11 00007 g0a1
Figure A1. Evaluated changes calculated for R20mm. Same description as in Figure 6.
Figure A1. Evaluated changes calculated for R20mm. Same description as in Figure 6.
Hydrology 11 00007 g0a1
Hydrology 11 00007 g0a2
Figure A2. Evaluated changes calculated for R50mm. Same description as in Figure 6.
Figure A2. Evaluated changes calculated for R50mm. Same description as in Figure 6.
Hydrology 11 00007 g0a2
Hydrology 11 00007 g0a3
Figure A3. Evaluated changes calculated for Rx1day. Same description as in Figure 6.
Figure A3. Evaluated changes calculated for Rx1day. Same description as in Figure 6.
Hydrology 11 00007 g0a3
Hydrology 11 00007 g0a4
Figure A4. Evaluated changes calculated for Rx5day. Same description as in Figure 6.
Figure A4. Evaluated changes calculated for Rx5day. Same description as in Figure 6.
Hydrology 11 00007 g0a4
Hydrology 11 00007 g0a5
Figure A5. Evaluated changes calculated for SDII. Same description as in Figure 6.
Figure A5. Evaluated changes calculated for SDII. Same description as in Figure 6.
Hydrology 11 00007 g0a5
Hydrology 11 00007 g0a6
Figure A6. Evaluated changes calculated for R95pTOT. Same description as in Figure 6.
Figure A6. Evaluated changes calculated for R95pTOT. Same description as in Figure 6.
Hydrology 11 00007 g0a6
Hydrology 11 00007 g0a7
Figure A7. Evaluated changes calculated for R99pTOT. Same description as in Figure 6.
Figure A7. Evaluated changes calculated for R99pTOT. Same description as in Figure 6.
Hydrology 11 00007 g0a7
Hydrology 11 00007 g0a8
Figure A8. Evaluated changes calculated for PRCPTOT. Same description as in Figure 6.
Figure A8. Evaluated changes calculated for PRCPTOT. Same description as in Figure 6.
Hydrology 11 00007 g0a8
Hydrology 11 00007 g0a9
Figure A9. Evaluated changes calculated for CDD. Same description as in Figure 6.
Figure A9. Evaluated changes calculated for CDD. Same description as in Figure 6.
Hydrology 11 00007 g0a9
Hydrology 11 00007 g0a10
Figure A10. Evaluated changes calculated for CWD. Same description as in Figure 6.
Figure A10. Evaluated changes calculated for CWD. Same description as in Figure 6.
Hydrology 11 00007 g0a10

References

  1. Marengo, J.A.; Espinoza, J.C. Extreme Seasonal Droughts and Floods in Amazonia: Causes, Trends and Impacts. Int. J. Climatol. 2016, 36, 1033–1050. [Google Scholar] [CrossRef]
  2. Espinoza, J.C.; Garreaud, R.; Poveda, G.; Arias, P.A.; Molina-Carpio, J.; Masiokas, M.; Viale, M.; Scaff, L. Hydroclimate of the Andes Part I: Main Climatic Features. Front. Earth Sci. 2020, 8, 64. [Google Scholar] [CrossRef]
  3. He, Z.; Dai, A.; Vuille, M. The Joint Impacts of Atlantic and Pacific Multidecadal Variability on South American Precipitation and Temperature. J. Clim. 2021, 34, 7959–7981. [Google Scholar] [CrossRef]
  4. Cordova, M.; Celleri, R.; van Delden, A. Dynamics of Precipitation Anomalies in Tropical South America. Atmosphere 2022, 13, 972. [Google Scholar] [CrossRef]
  5. Kayano, M.T.; Ceron, W.L.; Andreoli, R.V.; Souza, R.A.F.; Avila-Diaz, A.; Zuluaga, C.F.; Carvalho, L.M.V. Does the El Nino-Southern Oscillation Affect the Combined Impact of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation on the Precipitation and Surface Air Temperature Variability over South America? Atmosphere 2022, 13, 231. [Google Scholar] [CrossRef]
  6. Banco Mundial. Análisis de La Gestión Del Riesgo de Desastres En Colombia: Un Aporte Para La Construcción de Políticas Públicas; CID PALMERO: Bogotá, Colombia, 2012. [Google Scholar]
  7. Casado, A.; Campo, A.M. Extremos Hidroclimáticos y Recursos Hídricos: Estado de Conocimiento En El Suroeste Bonaerense, Argentina. Cuad. Geogr. 2019, 58, 6. (In Spanish) [Google Scholar] [CrossRef]
  8. IPCC. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Pachauri, R.K., Reisinger, A., Eds.; IPCC: Geneva, Switzerland, 2007; 104p. [Google Scholar]
  9. Arias, P.A.; Bellouin, N.; Coppola, E.; Jones, R.G.; Krinner, G.; Marotzke, J.; Naik, V.; Palmer, M.D.; Plattner, G.-K.; Rogelj, J.; et al. Technical Summary. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S.L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M.I., et al., Eds.; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar]
  10. Rosenzweig, C.; Tubiello, F.N.; Goldberg, R.; Mills, E.; Bloomfield, J. Increased Crop Damage in the US from Excess Precipitation under Climate Change. Glob. Environ. Chang. 2002, 12, 197–202. [Google Scholar] [CrossRef]
  11. Hoyos, N.; Escobar, J.; Restrepo, J.C.; Arango, A.M.; Ortiz, J.C. Impact of the 2010–2011 La Niña Phenomenon in Colombia, South America: The Human Toll of an Extreme Weather Event. Appl. Geogr. 2013, 39, 16–25. [Google Scholar] [CrossRef]
  12. Moreno, H.A.; Vélez, M.V.; Montoya, J.D.; Rhenals, R.L. La Lluvia y Los Deslizamientos de Tierra En Antioquia: Análisis de Su Ocurrencia En Las Escalas Interanual, Intraanual y Diaria. Rev. EIA 2013, 3, 59–69. [Google Scholar]
  13. Aristizábal, E.; Gómez, J. Inventario de Emergencias y Desastres En El Valle de Aburrá Originados Por Fenómenos Naturales y Antrópicos En El Periodo 1880-2007. Gestión Y Ambiente 2007, 10, 17–30. [Google Scholar]
  14. Liu, S.; Huang, S.; Xie, Y.; Leng, G.; Huang, Q.; Wang, L.; Xue, Q. Spatial-Temporal Changes of Rainfall Erosivity in the Loess Plateau, China: Changing Patterns, Causes and Implications. CATENA 2018, 166, 279–289. [Google Scholar] [CrossRef]
  15. Wang, S.; Zhang, K.; van Beek, L.P.H.; Tian, X.; Bogaard, T.A. Physically-Based Landslide Prediction over a Large Region: Scaling Low-Resolution Hydrological Model Results for High-Resolution Slope Stability Assessment. Environ. Model. Softw. 2020, 124, 104607. [Google Scholar] [CrossRef]
  16. Arias, P.A.; Ortega, G.; Villegas, L.D.; Martínez, J.A. Colombian Climatology in CMIP5/CMIP6 Models: Persistent Biases and Improvements. Rev. Fac. Ing. Univ. Antioq. 2021, 100, 75–96. [Google Scholar] [CrossRef]
  17. Zhang, X.; Alexander, L.; Hegerl, G.C.; Jones, P.; Tank, A.K.; Peterson, T.C.; Trewin, B.; Zwiers, F.W. Indices for Monitoring Changes in Extremes Based on Daily Temperature and Precipitation Data. WIREs Clim. Chang. 2011, 2, 851–870. [Google Scholar] [CrossRef]
  18. Kumar, S.; Chanda, K.; Pasupuleti, S. Spatiotemporal Analysis of Extreme Indices Derived from Daily Precipitation and Temperature for Climate Change Detection over India. Theor. Appl. Climatol. 2020, 140, 343–357. [Google Scholar] [CrossRef]
  19. Pita-Diaz, O.; Ortega-Gaucin, D. Analysis of Anomalies and Trends of Climate Change Indices in Zacatecas, Mexico. Climate 2020, 8, 55. [Google Scholar] [CrossRef]
  20. Barry, A.A.; Caesar, J.; Tank, A.M.G.K.; Aguilar, E.; McSweeney, C.; Cyrille, A.M.; Nikiema, M.P.; Narcisse, K.B.; Sima, F.; Stafford, G.; et al. West Africa Climate Extremes and Climate Change Indices. Int. J. Climatol. 2018, 38, E921–E938. [Google Scholar] [CrossRef]
  21. Frich, P.; Alexander, L.V.; Della-Marta, P.; Gleason, B.; Haylock, M.; Tank, A.; Peterson, T. Observed Coherent Changes in Climatic Extremes during the Second Half of the Twentieth Century. Clim. Res. 2002, 19, 193–212. [Google Scholar] [CrossRef]
  22. Aguilar, E.; Peterson, T.C.; Obando, P.R.; Frutos, R.; Retana, J.A.; Solera, M.; Soley, J.; Garcia, I.G.; Araujo, R.M.; Santos, A.R.; et al. Changes in Precipitation and Temperature Extremes in Central America and Northern South America, 1961–2003. J. Geophys. Res. Atmos. 2005, 110, D23107. [Google Scholar] [CrossRef]
  23. Zhang, X.B.; Aguilar, E.; Sensoy, S.; Melkonyan, H.; Tagiyeva, U.; Ahmed, N.; Kutaladze, N.; Rahimzadeh, F.; Taghipour, A.; Hantosh, T.H.; et al. Trends in Middle East Climate Extreme Indices from 1950 to 2003. J. Geophys. Res. Atmos. 2005, 110, D22104. [Google Scholar] [CrossRef]
  24. Alexander, L.V.; Zhang, X.; Peterson, T.C.; Caesar, J.; Gleason, B.; Tank, A.M.G.K.; Haylock, M.; Collins, D.; Trewin, B.; Rahimzadeh, F.; et al. Global Observed Changes in Daily Climate Extremes of Temperature and Precipitation. J. Geophys. Res. Atmos. 2006, 111. [Google Scholar] [CrossRef]
  25. Tank, A.M.G.K.; Peterson, T.C.; Quadir, D.A.; Dorji, S.; Zou, X.; Tang, H.; Santhosh, K.; Joshi, U.R.; Jaswal, A.K.; Kolli, R.K.; et al. Changes in Daily Temperature and Precipitation Extremes in Central and South Asia. J. Geophys. Res. Atmos. 2006, 111, D16105. [Google Scholar] [CrossRef]
  26. Vincent, L.A.; Mekis, E. Changes in Daily and Extreme Temperature and Precipitation Indices for Canada over the Twentieth Century. Atmos. Ocean 2006, 44, 177–193. [Google Scholar] [CrossRef]
  27. Donat, M.G.; Alexander, L.V.; Yang, H.; Durre, I.; Vose, R.; Dunn, R.J.H.; Willett, K.M.; Aguilar, E.; Brunet, M.; Caesar, J.; et al. Updated Analyses of Temperature and Precipitation Extreme Indices since the Beginning of the Twentieth Century: The HadEX2 Dataset. J. Geophys. Res. Atmos. 2013, 118, 2098–2118. [Google Scholar] [CrossRef]
  28. Yosef, Y.; Aguilar, E.; Alpert, P. Changes in Extreme Temperature and Precipitation Indices: Using an Innovative Daily Homogenized Database in Israel. Int. J. Climatol. 2019, 39, 5022–5045. [Google Scholar] [CrossRef]
  29. Galván, R.; Carbonetti, M.; Gende, M.; Brunini, C. Impact of the extreme 2015-2016 ENOS event on the geometry of the earth surface in the equatorial region of South America. Geoacta 2017, 42, 23–44. [Google Scholar]
  30. Timmermann, A.; An, S.-I.; Kug, J.-S.; Jin, F.-F.; Cai, W.; Capotondi, A.; Cobb, K.M.; Lengaigne, M.; McPhaden, M.J.; Stuecker, M.F.; et al. El Niño–Southern Oscillation Complexity. Nature 2018, 559, 535–545. [Google Scholar] [CrossRef]
  31. Tang, T.; Luo, J.-J.; Peng, K.; Qi, L.; Tang, S. Over-Projected Pacific Warming and Extreme El Niño Frequency Due to CMIP5 Common Biases. Natl. Sci. Rev. 2021, 8, nwab056. [Google Scholar] [CrossRef]
  32. Feldman, D.R.; Tadić, J.M.; Arnold, W.; Schwarz, A. Establishing a Range of Extreme Precipitation Estimates in California for Planning in the Face of Climate Change. J. Water Resour. Plan. Manag. 2021, 147, 04021056. [Google Scholar] [CrossRef]
  33. Poveda, G.; Álvarez, D.; Rueda, Ó. Hydro-Climatic Variability over the Andes of Colombia Associated with ENSO: A Review of Climatic Processes and Their Impact on One of the Earth’s Most Important Biodiversity Hotspots. Clim. Dyn. 2011, 36, 2233–2249. [Google Scholar] [CrossRef]
  34. Serrano Vincenti, S.; Zuleta, D.; Moscoso, V.; Jácome, P.; Palacios, E.; Villacís, M. Análisis estadístico de datos meteorológicos mensuales y diarios para la determinación de variabilidad climática y cambio climático en el Distrito Metropolitano de Quito. LGR 2012, 16, 23. [Google Scholar] [CrossRef]
  35. Naranjo Bedoya, K.; Aristizábal Giraldo, E.V.; Morales Rodelo, J.A. Influencia del ENSO en la Variabilidad Espacial y Temporal de la Ocurrencia de Movimientos en Masa Desencadenados por Lluvias en la Región Andina Colombiana. Ing. Y Cienc. 2019, 15, 11–42. [Google Scholar] [CrossRef]
  36. Capel Molina, J.J. “El Niño” y El Sistema Climático Terrestre, 1st ed.; University of Murcia: Barcelona, Spain, 1999; ISBN 84-344-3458-X. [Google Scholar]
  37. Wang, S.; Li, S.; Xing, J.; Yang, J.; Dong, J.; Qin, Y.; Sahu, S.K. Evaluation of the Influence of El Niño-Southern Oscillation on Air Quality in Southern China from Long-Term Historical Observations. Front. Environ. Sci. Eng. 2022, 16, 26. [Google Scholar] [CrossRef]
  38. Ashok, K.; Behera, S.K.; Rao, S.A.; Weng, H.; Yamagata, T. El Niño Modoki and Its Possible Teleconnection. J. Geophys. Res. Ocean. 2007, 112. [Google Scholar] [CrossRef]
  39. Chen, N.; Majda, A.J. Simple Dynamical Models Capturing the Key Features of the Central Pacific el Niño. Proc. Natl. Acad. Sci. USA 2016, 113, 11732–11737. [Google Scholar] [CrossRef] [PubMed]
  40. Trenberth, K.E. ENSO in the Global Climate System. In El Niño Southern Oscillation in a Changing Climate; American Geophysical Union (AGU): Washington, DC, USA, 2020; pp. 21–37. ISBN 978-1-119-54816-4. [Google Scholar]
  41. Freund, M.B.; Henley, B.J.; Karoly, D.J.; McGregor, H.V.; Abram, N.J.; Dommenget, D. Higher Frequency of Central Pacific El Niño Events in Recent Decades Relative to Past Centuries. Nat. Geosci. 2019, 12, 450–455. [Google Scholar] [CrossRef]
  42. Iacovone, M.F.; Pántano, V.C.; Penalba, O.C. Consecutive Dry and Wet Days over South America and Their Association with ENSO Events, in CMIP5 Simulations. Theor Appl Clim. 2020, 142, 791–804. [Google Scholar] [CrossRef]
  43. Pal, M.; Maity, R.; Ratnam, J.V.; Nonaka, M.; Behera, S.K. Long-Lead Prediction of ENSO Modoki Index Using Machine Learning Algorithms. Sci. Rep. 2020, 10, 365. [Google Scholar] [CrossRef]
  44. Navarro-Monterroza, E.; Arias, P.A.; Vieira, S.C.; Navarro-Monterroza, E.; Arias, P.A.; Vieira, S.C. El Niño-Oscilación del Sur, fase Modoki, y sus efectos en la variabilidad espacio-temporal de la precipitación en Colombia. Rev. Acad. Colomb. Cienc. Exactas Físicas Nat. 2019, 43, 120–132. [Google Scholar] [CrossRef]
  45. Liang, X.S.; Xu, F.; Rong, Y.; Zhang, R.; Tang, X.; Zhang, F. El Niño Modoki Can Be Mostly Predicted More than 10 Years Ahead of Time. Sci. Rep. 2021, 11, 17860. [Google Scholar] [CrossRef]
  46. Pinault, J.-L. The Anticipation of the ENSO: What Resonantly Forced Baroclinic Waves Can Teach Us (Part II). J. Mar. Sci. Eng. 2018, 6, 63. [Google Scholar] [CrossRef]
  47. Maher, N.; Wills, R.C.J.; DiNezio, P.; Klavans, J.; Milinski, S.; Sanchez, S.C.; Stevenson, S.; Stuecker, M.F.; Wu, X. The Future of the El Niño–Southern Oscillation: Using Large Ensembles to Illuminate Time-Varying Responses and Inter-Model Differences. Earth Syst. Dyn. 2023, 14, 413–431. [Google Scholar] [CrossRef]
  48. Giraldo-Osorio, J.D.; Trujillo-Osorio, D.E.; Baez-Villanueva, O.M. Analysis of ENSO-Driven Variability, and Long-Term Changes, of Extreme Precipitation Indices in Colombia, Using the Satellite Rainfall Estimates CHIRPS. Water 2022, 14, 1733. [Google Scholar] [CrossRef]
  49. Fernández Lopera, C.C.; Castro Rivera, J.A. Unidad Nacional para la Gestión del Riesgo de Desastres. In Fenómeno El Niño, Análisis comparativo 1997–1998//2014–2016; UNGRD: Bogotá, Colombia, 2016; ISBN 978-958-56-0170-3. [Google Scholar]
  50. Tabari, H. Climate Change Impact on Flood and Extreme Precipitation Increases with Water Availability. Sci. Rep. 2020, 10, 13768. [Google Scholar] [CrossRef] [PubMed]
  51. Balmaceda-Huarte, R.; Olmo, M.E.; Bettolli, M.L.; Poggi, M.M. Evaluation of Multiple Reanalyses in Reproducing the Spatio-Temporal Variability of Temperature and Precipitation Indices over Southern South America. Int. J. Climatol. 2021, 41, 5572–5595. [Google Scholar] [CrossRef]
  52. Vicente-Serrano, S.M.; García-Herrera, R.; Peña-Angulo, D.; Tomas-Burguera, M.; Domínguez-Castro, F.; Noguera, I.; Calvo, N.; Murphy, C.; Nieto, R.; Gimeno, L.; et al. Do CMIP Models Capture Long-Term Observed Annual Precipitation Trends? Clim. Dyn. 2022, 58, 2825–2842. [Google Scholar] [CrossRef]
  53. Aristizábal, E.; Yokota, S. Evolución Geomorfológica Del Valle de Aburra y Sus Implicaciones en la Ocurrencia de Movimientos en Masa. Boletín Cienc. Tierra 2008, 24, 5–18. [Google Scholar]
  54. Guerrero Hoyos, L.Á.; Aristizábal Giraldo, E. Estimación y Análisis de Umbrales Críticos de Lluvia para la Ocurrencia de Avenidas Torrenciales en el Valle de Aburrá (Antioquia). Rev. EIA 2019, 16, 97–111. [Google Scholar] [CrossRef]
  55. Pérez, J.I.; Zuñiga, Y.P.; Nardini, A. Identification Multiattribute of Typologies of Flood-Vulnerable Housing in Riohacha, la Guajira-Colombia. Inf. Tecnol. 2018, 29, 187–201. [Google Scholar] [CrossRef]
  56. Pérez, J.I.; Escobar, J.R.; Fragozo, J.M. Modelación Hidráulica 2D de Inundaciones En Regiones Con Escasez de Datos. el Caso Del Delta Del Río Ranchería, Riohacha-Colombia: 2D Hydraulic Flood Modeling in Data-Scarce Regions The Case of Ranchería River Delta, Riohacha-Colombia. Inf. Tecnol. 2018, 29, 143–157. [Google Scholar] [CrossRef]
  57. Varón Gutiérrez, S.D.; Vargas Cuervo, G. Análisis de La Susceptibilidad Por Inundaciones Asociadas a La Dinámica Fluvial Del Río Guatiquía En La Ciudad de Villavicencio, Colombia. Cuad. De Geogr. Rev. Colomb. De Geogr. 2019, 28, 152–174. [Google Scholar] [CrossRef]
  58. Pabón-Caicedo, J.D. Búsqueda de series de referencia para el seguimiento de la señal regional del calentamiento global. Cuad. Geogr. Rev. Colomb. Geogr. 1995, 5, 164–173. [Google Scholar]
  59. Oscar, M.S.; Poveda, J.G.; Carvajal, L.F. Introducción al Clima de Colombia, 1st ed.; Universidad Nacional de Colombia: Medellín, Colombia, 1997; ISBN 978-958-628-144-7. [Google Scholar]
  60. Pérez, C.; Poveda, G.; Mesa, O.; Carvajal, L.F.; Ochoa, A. Evidencias de cambio climático en Colombia: Tendencias y cambios de fase y amplitud de los ciclos anual y semianual. Bull. L’institut Français D’études Andin. 1998, 27, 537–546. [Google Scholar] [CrossRef]
  61. Pabón, J.D. El cambio climático global y su manifestación en Colombia. Cuad. Geogr. Rev. Colomb. Geogr. 2003, 12, 111–119. [Google Scholar]
  62. Aristizábal Acevedo, L.A. Estimación Hidrológica Bajo Escenarios de Cambio Climático En Colombia. Master Thesis, Universidad Nacional de Colombia-Sede Medellín, Antioquia, Colombia, 2009. [Google Scholar]
  63. Álvarez-Villa, O.D.; Vélez, J.I.; Poveda, G. Improved Long-Term Mean Annual Rainfall Fields for Colombia. Int. J. Climatol. 2011, 31, 2194–2212. [Google Scholar] [CrossRef]
  64. Pabón-Caicedo, J.D. Cambio Climático en Colombia: Tendencias en la Segunda Mitad del Siglo XX y Escenarios Posibles para el Siglo XXI. Rev. Acad. Colomb. Cienc. Exactas Físicas Nat. 2012, 36, 261–278. [Google Scholar]
  65. Arias, P.A.; Martínez, J.A.; Vieira, S.C. Moisture Sources to the 2010–2012 Anomalous Wet Season in Northern South America. Clim. Dyn. 2015, 45, 2861–2884. [Google Scholar] [CrossRef]
  66. Mesa, O.; Urrea, V.; Ochoa, A. Trends of Hydroclimatic Intensity in Colombia. Climate 2021, 9, 120. [Google Scholar] [CrossRef]
  67. Li, X.; Zhang, K.; Gu, P.; Feng, H.; Yin, Y.; Chen, W.; Cheng, B. Changes in Precipitation Extremes in the Yangtze River Basin during 1960–2019 and the Association with Global Warming, ENSO, and Local Effects. Sci. Total Environ. 2021, 760, 144244. [Google Scholar] [CrossRef]
  68. Li, C.; Tian, Q.; Yu, R.; Zhou, B.; Xia, J.; Burke, C.; Dong, B.; Tett, S.F.B.; Freychet, N.; Lott, F.; et al. Attribution of Extreme Precipitation in the Lower Reaches of the Yangtze River during May 2016. Environ. Res. Lett. 2018, 13, 014015. [Google Scholar] [CrossRef]
  69. Cerón, W.L.; Andreoli, R.V.; Kayano, M.T.; Canchala, T.; Ocampo-Marulanda, C.; Avila-Diaz, A.; Antunes, J. Trend Pattern of Heavy and Intense Rainfall Events in Colombia from 1981–2018: A Trend-EOF Approach. Atmosphere 2022, 13, 156. [Google Scholar] [CrossRef]
  70. IDEAM. Zonificación y Codificación de Unidades Hidrográficas e Hidrogeológicas de Colombia; IDEAM: Bogotá, Colombia, 2013. [Google Scholar]
  71. Benavides, H.O.; Márquez, R.M.; Moreno, G.H. Análisis de Índices de Extremos Climáticos Para Colombia Usando El RCLIMDEX; IDEAM: Bogotá, Colombia, 2007. [Google Scholar]
  72. Urrea, V.; Ochoa, A.; Mesa, O. Seasonality of Rainfall in Colombia. Water Resour. Res. 2019, 55, 4149–4162. [Google Scholar] [CrossRef]
  73. Cerón, W.L.; Andreoli, R.V.; Kayano, M.T.; Avila-Diaz, A. Role of the Eastern Pacific-Caribbean Sea SST Gradient in the Choco Low-Level Jet Variations from 1900–2015. Clim. Res. 2021, 83, 61–74. [Google Scholar] [CrossRef]
  74. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 Global Reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  75. Teutschbein, C.; Seibert, J. Bias Correction of Regional Climate Model Simulations for Hydrological Climate-Change Impact Studies: Review and Evaluation of Different Methods. J. Hydrol. 2012, 456–457, 12–29. [Google Scholar] [CrossRef]
  76. Boé, J.; Terray, L.; Habets, F.; Martin, E. Statistical and Dynamical Downscaling of the Seine Basin Climate for Hydro-Meteorological Studies. Int. J. Climatol. 2007, 27, 1643–1655. [Google Scholar] [CrossRef]
  77. Cannon, A.J.; Sobie, S.R.; Murdock, T.Q. Bias Correction of GCM Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes? J. Clim. 2015, 28, 6938–6959. [Google Scholar] [CrossRef]
  78. Chen, J.; Brissette, F.P.; Chaumont, D.; Braun, M. Finding Appropriate Bias Correction Methods in Downscaling Precipitation for Hydrologic Impact Studies over North America. Water Resour. Res. 2013, 49, 4187–4205. [Google Scholar] [CrossRef]
  79. Fang, G.H.; Yang, J.; Chen, Y.N.; Zammit, C. Comparing Bias Correction Methods in Downscaling Meteorological Variables for a Hydrologic Impact Study in an Arid Area in China. Hydrol. Earth Syst. Sci. 2015, 19, 2547–2559. [Google Scholar] [CrossRef]
  80. Teng, J.; Potter, N.J.; Chiew, F.H.S.; Zhang, L.; Wang, B.; Vaze, J.; Evans, J.P. How Does Bias Correction of Regional Climate Model Precipitation Affect Modelled Runoff? Hydrol. Earth Syst. Sci. 2015, 19, 711–728. [Google Scholar] [CrossRef]
  81. Croitoru, A.-E.; Piticar, A.; Burada, D.C. Changes in Precipitation Extremes in Romania. Quat. Int. 2016, 415, 325–335. [Google Scholar] [CrossRef]
  82. Bronaught, D. R Package Climdex.Pcic Version 1.1-11: PCIC Implementation of Climdex Routines. Available online: https://rdrr.io/cran/climdex.pcic/ (accessed on 1 June 2022).
  83. Yue, S.; Wang, C. The Influence of Serial Correlation on the Mann–Whitney Test for Detecting a Shift in Median. Adv. Water Resour. 2002, 25, 325–333. [Google Scholar] [CrossRef]
  84. Ramírez Builes, V.; Jaramillo-Robledo, Á. Relación Entre El Índice Oceánico de El Niño y La Lluvia, En La Región Andina Central de Colombia. Cenicafé. 2009, 60, 161–172. [Google Scholar]
  85. Gibbons, J.D.; Chakraborti, S. Nonparametric Statistical Inference; CRC Press: Boca Raton, FL, USA, 2014. [Google Scholar]
  86. Bauer, D.F. Constructing Confidence Sets Using Rank Statistics. J. Am. Stat. Assoc. 1972, 67, 687–690. [Google Scholar] [CrossRef]
  87. Tedeschi, R.G.; Cavalcanti, I.F.A.; Grimm, A.M. Influences of Two Types of ENSO on South American Precipitation. Int. J. Climatol. 2013, 33, 1382–1400. [Google Scholar] [CrossRef]
  88. Tedeschi, R.G.; Grimm, A.M.; Cavalcanti, I.F.A. Influence of Central and East ENSO on Precipitation and Its Extreme Events in South America during Austral Autumn and Winter. Int. J. Climatol. 2016, 36, 4797–4814. [Google Scholar] [CrossRef]
  89. Jiang, Z.; Li, J. Impact of Eastern and Central Pacific el Niño on Lower Tropospheric Ozone in China. Atmos. Chem. Phys. 2022, 22, 7273–7285. [Google Scholar] [CrossRef]
  90. Kim, H.-M.; Webster, P.J.; Curry, J.A. Impact of Shifting Patterns of Pacific Ocean Warming on North Atlantic Tropical Cyclones. Science 2009, 325, 77–80. [Google Scholar] [CrossRef]
  91. Yeh, S.-W.; Kug, J.-S.; Dewitte, B.; Kwon, M.-H.; Kirtman, B.P.; Jin, F.-F. El Niño in a Changing Climate. Nature 2009, 461, 511–514. [Google Scholar] [CrossRef]
  92. Zhang, Z.; Ren, B.; Zheng, J. A Unified Complex Index to Characterize Two Types of ENSO Simultaneously. Sci. Rep. 2019, 9, 8373. [Google Scholar] [CrossRef]
Hydrology 11 00007 g001
Figure 1. Study zone. (a) Shadow map of the mountain ranges in Colombia, and the location of daily precipitation gauging stations selected for the study. (b) Multi-year mean precipitation map, and the names of the natural regions of the country. The map was interpolated using 1087 precipitation gauging stations with monthly data.
Figure 1. Study zone. (a) Shadow map of the mountain ranges in Colombia, and the location of daily precipitation gauging stations selected for the study. (b) Multi-year mean precipitation map, and the names of the natural regions of the country. The map was interpolated using 1087 precipitation gauging stations with monthly data.
Hydrology 11 00007 g001
Hydrology 11 00007 g002
Figure 2. Availability of stations with daily precipitation data. (a) Availability of all considered stations (3868 rain-gauges) between 1920 and 2022. (b) Availability of selected stations for the period 1979–2022.
Figure 2. Availability of stations with daily precipitation data. (a) Availability of all considered stations (3868 rain-gauges) between 1920 and 2022. (b) Availability of selected stations for the period 1979–2022.
Hydrology 11 00007 g002
Hydrology 11 00007 g003
Figure 3. Conceptual chart of the methodology followed in this study.
Figure 3. Conceptual chart of the methodology followed in this study.
Hydrology 11 00007 g003
Hydrology 11 00007 g004
Figure 4. Filling in of missing data with ERA5. Station Code 26180160 of the municipality of Abejorral in the department of Antioquia, coordinates 5.78 N–75.43 W. (a) Daily precipitation series observed (red) and simulated by ERA5 (black). (b) Cumulative distribution functions (CDF) with observed (red) and simulated (black) data for 2 January between the years 1979 and 2022.
Figure 4. Filling in of missing data with ERA5. Station Code 26180160 of the municipality of Abejorral in the department of Antioquia, coordinates 5.78 N–75.43 W. (a) Daily precipitation series observed (red) and simulated by ERA5 (black). (b) Cumulative distribution functions (CDF) with observed (red) and simulated (black) data for 2 January between the years 1979 and 2022.
Hydrology 11 00007 g004
Hydrology 11 00007 g005
Figure 5. Example of EPI time series for station 48015050 (Vásquez Cobo Airport, coordinates 4.19–69.94 W). For each of the EPIs, the yearly time series is shown, where the normal years (white dots; NOR), the El Niño years (red; NOY) and the La Niña years (blue; NAY) have been identified. Boxplots of EPI values, grouped according to ENSO phases, are also shown.
Figure 5. Example of EPI time series for station 48015050 (Vásquez Cobo Airport, coordinates 4.19–69.94 W). For each of the EPIs, the yearly time series is shown, where the normal years (white dots; NOR), the El Niño years (red; NOY) and the La Niña years (blue; NAY) have been identified. Boxplots of EPI values, grouped according to ENSO phases, are also shown.
Hydrology 11 00007 g005
Hydrology 11 00007 g006
Figure 6. Evaluated changes calculated for R10mm. (a) Anomalies in La Niña years ∆ E P I N A Y = E P I ¯ N A Y − E P I ¯ N O R ; (b) anomalies in El Niño years ∆ E P I N O Y = E P I ¯ N O Y − E P I ¯ N O R ; (c) long-term change ∆ E P I L T C = E P I ¯ 2004 − 2021 − E P I ¯ 1979 − 1996 ; and (d) maximum absolute difference between anomalies driven by ENSO, or long-term change. In maps (a–c), the unfilled points represent a p-value > 0.05, and the triangles show positive (▲) or negative (▼) significance, according to the p-value of the MWW test. The color scale represents the magnitude of the anomaly/change of the EPI.
Figure 6. Evaluated changes calculated for R10mm. (a) Anomalies in La Niña years ∆ E P I N A Y = E P I ¯ N A Y − E P I ¯ N O R ; (b) anomalies in El Niño years ∆ E P I N O Y = E P I ¯ N O Y − E P I ¯ N O R ; (c) long-term change ∆ E P I L T C = E P I ¯ 2004 − 2021 − E P I ¯ 1979 − 1996 ; and (d) maximum absolute difference between anomalies driven by ENSO, or long-term change. In maps (a–c), the unfilled points represent a p-value > 0.05, and the triangles show positive (▲) or negative (▼) significance, according to the p-value of the MWW test. The color scale represents the magnitude of the anomaly/change of the EPI.
Hydrology 11 00007 g006
Hydrology 11 00007 g007
Figure 7. Dispersion of change in each extreme precipitation index. Each box shows, as a probability distribution function, the inter-annual variability between El Niño (red; NOY) and La Niña (blue; NAY), against normal years, and the long-term change (green; LTC).
Figure 7. Dispersion of change in each extreme precipitation index. Each box shows, as a probability distribution function, the inter-annual variability between El Niño (red; NOY) and La Niña (blue; NAY), against normal years, and the long-term change (green; LTC).
Hydrology 11 00007 g007
Table 1. Extreme precipitation indices defined by the ETCCDI.
Table 1. Extreme precipitation indices defined by the ETCCDI.
CategoryIDName of the IndexDefinitionUnits
FrequencyR10mmNumber of days with intense precipitationNumber of days in a year when PRCP ≥ 10 mmdays
R20mmNumber of days with very intense precipitationNumber of days in a year when PRCP ≥ 20 mmdays
R50mmNumber of days over 50 mmNumber of days in a year in which PRCP ≥ nn mm, nn is a parameter defined by the user, which in this case was set to 50 mm, to thus be able to identify days with too intense precipitation.days
IntensityRX1dayMaximum amount of precipitation in one dayAnnual maximum precipitation in 1 daymm
Rx5dayMaximum amount of precipitation in 5 daysAnnual maximum precipitation on 5 consecutive daysmm
SDIISimple daily intensity indexTotal annual precipitation divided by the number of wet days (defined by PRCP ≥ 1.0 mm) in a yearmm/day
R95pTOTVery wet daysTotal annual precipitation where RR > 95 percentilemm
R99pTOTExtremely wet daysTotal annual precipitation where RR > 99 percentilemm
PRCPTOTTotal annual precipitation on wet daysTotal annual precipitation on wet days (RR ≥ 1 mm)mm
DurationCDDConsecutive dry daysMaximum number of consecutive days with RR < 1 mmdays
CWDConsecutive wet daysMaximum number of consecutive days with RR ≥ 1 mmdays
Table 2. Dispersion of the anomalous values in La Niña years ( ∆ E P I N A Y = E P I ¯ N A Y − E P I ¯ N O R ). The table shows the number of stations with significant negative changes (2nd and 3rd columns, according to the p-value), with non-significant negative changes (4th column), with non-significant positive changes (5th column), and with positive and significant changes (6th and 7th columns). The first column shows the EPI names.
Table 2. Dispersion of the anomalous values in La Niña years ( ∆ E P I N A Y = E P I ¯ N A Y − E P I ¯ N O R ). The table shows the number of stations with significant negative changes (2nd and 3rd columns, according to the p-value), with non-significant negative changes (4th column), with non-significant positive changes (5th column), and with positive and significant changes (6th and 7th columns). The first column shows the EPI names.
EPINegative ChangesPositive Changes
p-Value < 0.010.01 < p-Value < 0.05p-Value > 0.050.01 < p-Value < 0.05p-Value < 0.01
R10mm0070275183352
R20mm1199166168445
R50mm162264296508
Rx1day27346642477
Rx5day152592076519
SDII393111950488
R95pTOT071944497538
R99pTOT083071160494
PRCPTOT0166313181319
CDD1831594800355
CWD1316866114528
Table 3. The same as in Table 2, but for the anomalous values in El Niño years ( ∆ E P I N O Y = E P I ¯ N O Y − E P I ¯ N O R .
Table 3. The same as in Table 2, but for the anomalous values in El Niño years ( ∆ E P I N O Y = E P I ¯ N O Y − E P I ¯ N O R .
EPINegative ChangesPositive Changes
p-Value < 0.010.01 < p-Value < 0.05p-Value > 0.050.01 < p-Value < 0.05p-Value < 0.01
R10mm1811404669111
R20mm8512152514540
R50mm2347533264121
Rx1day32451032995
Rx5day133552030174
SDII613409429194
R95pTOT224156624362
R99pTOT325536302113
PRCPTOT1791684419101
CDD018956314483
CWD146454325522
Table 4. The same as in Table 2, but for the long-term change values ( ∆ E P I L T C = E P I ¯ 2004 − 2021 − E P I ¯ 1979 − 1996 ).
Table 4. The same as in Table 2, but for the long-term change values ( ∆ E P I L T C = E P I ¯ 2004 − 2021 − E P I ¯ 1979 − 1996 ).
EPINegative ChangesPositive Changes
p-Value < 0.010.01 < p-Value < 0.05p-Value > 0.050.01 < p-Value < 0.05p-Value < 0.01
R10mm58664172623938
R20mm64594152743038
R50mm47493943372823
Rx1day27354113632618
Rx5day38554293171823
SDII1166226727639120
R95pTOT62604222842725
R99pTOT30404113562518
PRCPTOT70774522302823
CDD9273334404328
CWD94733832762529
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Vargas-León, L.A.; Giraldo-Osorio, J.D. Analysis of Anomalies Due to the ENSO and Long-Term Changes in Extreme Precipitation Indices Using Data from Ground Stations. Hydrology 2024, 11, 7. https://doi.org/10.3390/hydrology11010007

AMA Style

Vargas-León LA, Giraldo-Osorio JD. Analysis of Anomalies Due to the ENSO and Long-Term Changes in Extreme Precipitation Indices Using Data from Ground Stations. Hydrology. 2024; 11(1):7. https://doi.org/10.3390/hydrology11010007

Chicago/Turabian Style

Vargas-León, Luis Alberto, and Juan Diego Giraldo-Osorio. 2024. "Analysis of Anomalies Due to the ENSO and Long-Term Changes in Extreme Precipitation Indices Using Data from Ground Stations" Hydrology 11, no. 1: 7. https://doi.org/10.3390/hydrology11010007

APA Style

Vargas-León, L. A., & Giraldo-Osorio, J. D. (2024). Analysis of Anomalies Due to the ENSO and Long-Term Changes in Extreme Precipitation Indices Using Data from Ground Stations. Hydrology, 11(1), 7. https://doi.org/10.3390/hydrology11010007

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop