Daily Concentration of Precipitation in the Province of Alicante (1981–2020)

Abstract

The precipitation in the Mediterranean region, characterised by its annual variability and concentration in high-intensity events, is a key factor in territorial planning and the management of runoff in urban areas, particularly on the Spanish Mediterranean coast. This study focuses on the province of Alicante, applying the “daily precipitation concentration index (CI)” in 26 meteorological stations for the period 1981–2020, with the aim of analysing the statistical structure of precipitation on an annual scale. It measures the irregularity and intensity of precipitation according to the concentration of most of the annual total in a few days. Furthermore, it examines the synoptic situations and trajectories of the air masses on days of torrential rain using the HYSPLIT model. This is essential to identify the origin of moist air masses, to understand the meteorological mechanisms that intensify extreme rainfall events, and to identify recurrent patterns that explain their frequency and characteristics. The results reveal extreme CI values of between 0.58 in the interior of the province and 0.71 in the southern pre-coastal area, with a value of 0.68 in the city of Alicante. On average, the CI is 0.65, indicating that 25% of days with more rain have a concentration of around 75% of total precipitation, while 10% of the days represent 45% of the total. With respect to the origin of air masses, the most relevant in the mid-troposphere (500 hPa) are those from the north of Africa, particularly during the final periods of their trajectory, with flows from the east on the surface.

1. Introduction

The “daily precipitation concentration index (CI)” is a key tool for understanding the time distribution of rainfall. Within the context of Spain, and more specifically in the southeast of the Iberian Peninsula, this analysis is highly relevant due to the climate specificities of the arid and semi-arid Mediterranean areas [1].

The study of torrential rain is vitally important, as the Spanish Mediterranean coast is characterised by a high daily concentration of rainfall. In other words, a high percentage of total annual rainfall is recorded in a few days [2], giving rise to extraordinary episodes. The interest of this study is not purely climatological but also affects other areas, such as the environment or society. The main consequences of high-intensity events in short periods of time include frequent floods in the Western Mediterranean basin [3,4,5,6] and environmental impacts such as the erosion of sparsely vegetated soil. Furthermore, broadening the knowledge on the spatial and time variation of precipitation concentration facilitates water planning in Spain.

The study of rainfall is of vital importance, since the Spanish Mediterranean coastline is characterised by a high daily concentration of rainfall, with the coastal area of the province of Alicante reaching the maximum [7,8,9]. It is a key factor to take into account in flood risk management on a global scale, and particularly on a local scale, as it provides us with information on the areas most prone to flooding in situations of heavy rainfall. Torrential rainfall is a significant problem in the cities of the Spanish Mediterranean coast [10]. This intense and short-duration rainfall generates an increase in runoff that, combined with urban growth and soil sealing, results in sewage system overflows and an increased risk of flooding. This phenomenon is especially relevant in urban areas that have integrated river systems into their development, increasing the vulnerability of the population.

In the province of Alicante, there are actions taken to mitigate the effects of torrential rains and adapt the territory in a sustainable way [11]. Sustainable urban drainage systems (SUDSs) and nature-based solutions (NBSs) reduce the volume of runoff, complement sewerage networks, and minimise flooding problems [12]. Water management at the source and appropriate spatial planning limit construction in high-risk areas, creating green corridors and flood parks to reduce risks. An example of this is the “Vega Renhace Plan” in the Vega Baja del Segura comarca, which includes actions such as the creation of green areas and the installation of retention systems to control runoff flows [13]. In addition, environmental education is proposed for adaptation to climate change and to strengthen social and urban resilience to these phenomena. These measures reflect a paradigm shift towards more integrated and sustainable flood risk management.

The study of the CI of precipitation has aroused great interest, as reflected in the scientific literature, where most publications apply the methodology developed in this article. The CI has been used to analyse rainfall characteristics in some countries such as Spain [2,14,15,16,17], Italy [18], Iran [19,20], India [21], China [22,23], Malaysia [24], Serbia [25], Brazil [26], Mexico [27], Parana [28], and Chile [29].

On a global level, Monjo and Martin-Vide [30] analysed 66,409 daily precipitation time series in order to estimate the precipitation concentration using several indices in the period of 1950–2014. The study concludes that the high temporal concentration of precipitation is associated with the rapid rate of physical processes such as convection in areas with high insolation and warm seas, as is the case on the Mediterranean coast of the Iberian Peninsula. For the whole of Europe, Cortesi et al. [31] analysed 530 daily precipitation series in the period of 1971–2010. The highest daily rainfall concentrations, both annually and seasonally, were observed in the western Mediterranean basin, particularly along the Spanish and French coasts. Factors such as latitude and distance from the sea appear to play a significant role in the spatial distribution of the CI, while at a sub-regional scale, topography also becomes an important determinant.

In the Mediterranean basin, the research conducted by Mathbout et al. [32] is noteworthy. These authors studied the space–time distribution of daily rainfall concentration and its relationship with teleconnection patterns. To do so, they used daily precipitation series in the period of 1975–2015. The results showed annual values varying from 0.57 to 0.70 throughout the Mediterranean region. In addition, trend analysis showed increasing trends in southern France, the northern coasts of the Iberian Peninsula, Greece, and Tunisia. In the western Mediterranean basin [33], the highest values of the daily precipitation CI were recorded on the eastern coasts of Spain and southern France, while the lowest values were observed on the African coast.

For mainland Spain, this index was widely tested by Martín-Vide [2] for 32 meteorological stations and for the time period of 1951–1990. The CI values range from 0.70 in Valencia to 0.55 in Ourense, dividing mainland Spain into two regions: the eastern façade, with a high concentration (25% of rainy days account for 70% or more of the annual total), and the rest of the country, where rainfall is more evenly distributed. The southern Gulf of Valencia stands out for having the highest daily and hourly rainfall intensity and concentration in Spain. These findings align with the broader spatial analysis conducted by Sánchez-Lorenzo and Martín-Vide [34], who examined 41 meteorological stations across Spain and Portugal for the same time period. The highest daily rainfall concentration values are recorded in the Gulf of Valencia, reaching an index of 0.70 in its capital, reflecting the influence of torrential rains of Mediterranean origin. In contrast, regions such as Galicia, northwest Portugal, and the eastern part of the plateaus show less irregularity, with values of 0.56 to 0.57 and a minimum of 0.55 in Ourense. Areas such as the Bay of Biscay, the peninsular interior (Ávila and Madrid), and the western fringe, from Galicia to the Gulf of Cádiz, show moderate values (0.59–0.62), associated with the favourable exposure to precipitation from western and southwestern components.

De Luis et al. [35] analysed the changes in the CI of precipitation in Spain during the period of 1946–2005. To do so, they calculated CI values at annual, seasonal, and wet and dry period scales, comparing two 30-year subperiods (1946–1975 and 1976–2005). The results indicate a significant increase in CI over much of the Iberian Peninsula, especially in the wet season (October–March), reflecting a greater concentration of precipitation in shorter periods. In addition, regional differences are observed: the CI is higher in the southeast (Mediterranean areas) of the Iberian Peninsula, where rainfall is more irregular, while the north shows lower values due to the Atlantic influence. In this context, another study by De Luis et al. [36] pointed out that, on a regional scale, negative and non-significant trends in annual precipitation seem to be associated with an increase in or a more heterogeneous monthly distribution of precipitation. This suggests that in some stations where decreases in precipitation totals are observed, these may be accompanied by an increase in monthly rainfall concentration.

Benhamrouche and Martín Vide [14] apply a methodological advancement in the analysis of the daily precipitation concentration for 32 stations in mainland Spain for the period between 1951 and 2010 and compare the results for the sub-periods of 1951–1980 and 1981–2010. The daily precipitation concentration in the eastern Iberian Peninsula is high, with a concentration index close to 0.70, indicating that a few rainy days contribute a large percentage of the annual precipitation. Between the sub-periods of 1951–1980 and 1981–2010, the daily precipitation concentration increased at many observatories.

The analysis of the temporal fractality of precipitation and its relationship with the CI was previously explored by Meseguer-Ruiz et al. [37], who studied the spatial distribution of this fractality in peninsular Spain. Their results highlight the close relationship between temporal fractality and daily precipitation concentration, providing a solid basis for understanding the temporal irregularity of rainfall in different regions of the country. The CI shows a significant negative correlation with the fractal dimension. The highest CI values, above 0.70, are recorded on the Mediterranean slope, where rainfall is highly concentrated in intense events, such as accumulations of more than 200 litres in less than 24 h. On the other hand, the lowest CI values, around 0.55, are found on the Atlantic slope, characterised by more distributed and less intense rainfall. This spatial gradient of the CI reflects significant differences in rainfall patterns between the two regions. The study carried out at the Fabra Observatory in Barcelona (1927–2016) applies a multifractal model (n-index) to analyse the temporal concentration of extreme precipitation, integrating intensity–duration–frequency (IDF) curves and climate simulations based on CMIP6 models [38]. This work projects an increase of up to 10% in the n-index by 2100, suggesting an intensification of the concentration and severity of extreme events under climate change scenarios. Both studies converge in highlighting the importance of analysing precipitation concentration and its temporal distribution as key tools for understanding extreme event patterns, highlighting the relevance of fractal and multifractal approaches to modelling both current irregularity and future trends in precipitation.

For the whole of the Iberian Peninsula, the Balearic Islands, and the Canary Islands, Serrano-Notivoli et al. [17] analysed the spatial and time variability of the daily precipitation CI based on daily precipitation data using a high-resolution grid for the period of 1950–2012. Their study reveals marked spatial differences in CI values across Spain, with higher concentrations in the Mediterranean regions compared to the Atlantic areas. The research also highlights a temporal trend of increasing CI in some regions, particularly in the eastern part of the peninsula, suggesting a rising intensity and irregularity in rainfall events. Máyer Suarez and Marzol Jaén [39] analysed the daily concentration of precipitation in the Canary Islands, using data from 29 rainfall stations distributed throughout the archipelago over a period between 1970 and 2003. The authors calculated the CI and analysed the spatial distribution of rainfall. Their results indicate CI values above 0.63 at all stations, with the highest values recorded on the southern and eastern slopes of the most mountainous islands, where 25% of the rainiest days contribute up to 80% of the annual rainfall. Similarly, Máyer et al. [16] analysed seasonal and annual precipitation trends in the Canary Islands for a slightly extended period (1970–2013) and calculated the daily CI for 23 stations, highlighting significant spatial variability. Their findings show higher CI values in the eastern and southern islands, associated with more irregular and intense rainfall, while northern and western areas experience more evenly distributed precipitation due to orographic and wind influences.

On a detailed level, in the province of Alicante, Benhamrouche and Martín-Vide [15] conducted a comprehensive analysis of the daily precipitation CI in the province of Alicante, providing a fundamental base for understanding the rainfall patterns in the region. However, this study focuses on the time period of 1976–2009 and a group of 28 meteorological stations, with daily rainfall data grouped into 5 mm class intervals. The present research does not seek to update the results of that study but rather to provide a more up-to-date view for the same study area, extending the analysis to the period of 1981–2020 and using 26 stations with daily rainfall data grouped into 1 mm class intervals. Since the CI analysis is performed with 1 mm class intervals, high-quality data recording all daily precipitation amounts is required, which limits the number of precipitation series used (see Section 2). The aim of this study is to obtain CI results with higher accuracy, reflecting recent changes in precipitation behaviour and providing a more up-to-date and detailed picture.

In Spain, several studies have been carried out on the recent evolution of precipitation in order to validate the hypothesis of increasing daily precipitation in recent decades. In this sense, it has been pointed out that the most extreme episodes have an increasing probability of occurrence [40] and are linked to the presence of atmospheric rivers [41,42]. In addition, studies have shown evidence of increased convective precipitation on the Spanish Mediterranean coast, triggered by the increase in potential instability caused by the increase in the surface temperature of the Mediterranean Sea [43].

Miró et al. [44] analysed precipitation trends and dry/wet episodes in the Júcar and Segura river basins during 1955–2016. Their results highlight a general decrease in precipitation in key areas, especially in the headwaters of the Júcar River, together with an increase in the duration of dry periods and a greater temporal concentration of extreme rainfall in coastal areas. On the other hand, Hidalgo-Muñoz et al. [45] evaluated extreme rainfall events in Andalusia between 1955 and 2006, establishing seasonal trends by means of intensity and duration indices, and relating them to synoptic patterns. They identified a reduction in extreme rainfall intensity in western and central Andalusia, with increases in the southeast. Both studies coincide in pointing to an increase in the temporal and spatial concentration of extreme rainfall. These observations underline the importance of detailed analysis at the local scale to understand the dynamics of daily precipitation. For the Andalusian case, Senciales-González and Ruiz-Sinoga [46] examined the synoptic types causing torrential rainfall in the southern Spanish Mediterranean (Andalusian coastal sector), finding that more than 50% of the heavy precipitation events recorded in southern Spain were associated with NE–E and SE–S winds. Recently, Estrela et al. [47] analysed the rainfall trend in the southeast of the Iberian Peninsula and also pointed to an increase in heavy rainfall (≥40 mm/d) for the study period (1952–2021) and the 224 stations analysed, which showed a strong trend towards an increase in both the amount and the frequency of events, with a very high statistical significance (99% confidence level).

The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model has proven to be a key tool for analysing the trajectories of air masses associated with extreme precipitation events. In the study by Meseguer-Ruiz et al. [48], this model was applied to identify synoptic attributions of extreme precipitation in the Atacama Desert, revealing that air mass trajectories play a fundamental role in the intensification of these events. The results highlighted the influence of flows from specific oceanic and continental areas on the occurrence of intense rainfall in an arid region. These findings, based on the detailed characterisation of atmospheric trajectories, underline the relevance of the HYSPLIT model in the understanding of precipitation patterns, a perspective that is also adopted in the present study to analyse the trajectories of the air masses responsible for torrential events in the province of Alicante. On the other hand, Cloux et al. [49] pointed out the need to correctly identify the origin of moisture in torrential rainfall episodes in the Western Mediterranean using Eulerian (WRF–WVT) and Lagrangian (FLEXPART–WRF) approaches to trace moisture sources in catastrophic floods that occurred in 1982.

The principal research question as the central theme of this study is, does the daily precipitation CI contribute to improving the knowledge on the torrentiality of rainfall in the province of Alicante? To answer this question, the following objectives are established: (a) to apply the methodology developed by Martín-Vide [2] to calculate the daily precipitation CI, (b) to represent the results through maps in order to determine the spatial variations, and (c) to examine the synoptic situations and trajectories of the air masses on days of torrential rain using the HYSPLIT model.

Study Area

The rainfall regime in the province of Alicante can be described as being an arid or semi-arid Mediterranean regime, with a distinct seasonality and spatial variability. The maxima are in the autumn months, when the greatest amounts of rainfall are recorded. The typical Mediterranean climate is predominant in coastal areas and inland plains, with annual rainfall ranging between 300 and 600 mm. Rainfall is mainly concentrated in autumn and spring, with an average of 40 to 60 rainy days per year, while there is little or no rainfall during the summer months. The semi-arid Mediterranean climate is characteristic of the south of the province, with an annual rainfall of less than 300 mm and representing the driest region of Alicante. The frequency of rainfall is very low, with only 20 to 30 rainy days per year, marked by long periods of drought. When it does occur, the rains are usually torrential, especially in autumn. On the other hand, in the mountainous inland areas are the Mediterranean high mountain climate, characterised by colder winters and cooler summers than in the rest of the province. Here, rainfall is more abundant, exceeding 700 mm per year in the higher areas. There are between 60 and 80 rainy days per year, with rainfall more evenly distributed throughout the year. In winter, precipitation may be in the form of snow on the highest peaks.

Rainfall records reveal considerable differences between the north and south of the province. This circumstance is determined by factors such as latitude, altitude, the SW–NE orographic position, and the orientation of the coastline. For example, in Marina Alta areas, Gregale storms are common (NE winds), which, after meeting powerful orographic formations in this sector, discharge their precipitations on the windward side [50]. On the other hand, with this type of advection from the NE, the southern part of the province is downwind from these flows, so the precipitations in this area depend on the formalisation of convective cells—a product of the instability generated, on the one hand, by the presence of cold air pockets in the middle and high layers of the troposphere and, on the other hand, by Mediterranean advections with extremely humid air from the E (east) or SE (Sirocco).

Due to these factors and despite the aridity that defines the Mediterranean climate of the southeast of the Iberian Peninsula, in the province of Alicante there is a dichotomy between the northern part, which records more than 700 mm per year, and the south, with annual values of below 300 mm. This gradation from north to south in this province is reflected in the precipitation values observed in areas of the Marina Alta comarca, with 1000 mm, while in Vega Baja del Segura, they do not exceed 250 mm (Figure 1).

In the Mediterranean climate, extreme precipitation events, such as DANAs (isolated depression at high levels), have a critical impact due to the combination of intense rainfall over short periods and the inherent vulnerability of infrastructure and the landscape. These phenomena, characteristic of autumn, can concentrate large volumes of water in just a few hours, exceeding the capacity of natural channels and drainage systems.

A paradigmatic example is the DANA that occurred on 29 October 2024 in Valencia, with official data recorded by the AEMET highlighting its exceptional intensity (Figure 2). During this episode, 771.8 L per square metre were recorded in just 14 h. This episode set a record in Spain, with a maximum rainfall of 184.6 L per square metre in one hour. Additionally, peaks of 102.8 L per square metre in 30 min and 42.0 L per square metre in just 10 min were documented. This resulted in devastating floods that affected many municipalities in this province [52].

2. Materials and Methods

This study uses the daily precipitation data recorded by the meteorological stations of the province of Alicante provided by the Agencia Estatal de Meteorología (State Meteorological Agency, AEMET) available for the period of study from 1 January 1981 to 31 December 2020, a period of forty years, which is appropriate for an area of study located in southeastern Spain where precipitations patterns exhibit specific characteristics that differentiate it from the rest of the Iberian Peninsula [53] (Figure 3). In order to use the most complete series of data possible, the authors only worked with the meteorological stations that contained a minimum of 95% of the complete series. A total of 26 meteorological stations were selected (Figure 4), following a real precision index (RPI) analysis [54] carried out in previous research by Sánchez-Almodóvar [55] corresponding to the primary and secondary climatological networks of the AEMET (

Table 1. Characteristics of the location of the meteorological stations used.

Code	Meteorological Station	Latitude	Longitude	Altitude (m)
1	Agost (Escuela Nacional)	38°26′	0°38′	306
2	Alicante	38°22′	0°29′	81
3	Alicante-Elx (Airport)	38°16′	0°34′	43
4	Almudaina	38°45′	0°21′	580
5	Banyeres de Mariola	38°42′	0°39′	816
6	Beneixama (Casa Crespo)	38°41′	0°45′	661
7	Bolulla	38°40′	0°06′	240
8	Callosa de Segura	38°07′	0°52′	18
9	Callosa d’en Sarrià (El Algar)	38°39′	0°05′	95
10	Callosa d’en Sarrià (Tossal de Salomó)	38°38′	0°07′	218
11	Elche	38°16′	0°41′	95
12	Elche (C.H.S)	38°15′	0°42′	85
13	Elche (Campo Agrícola)	38°14′	0°41′	63
14	Gata de Gorgos	38°46′	0°05′	79
15	Jijona	38°32′	0°30′	484
16	La Marina de Elche	38°08′	0°38′	15
17	La Romana (Algesar)	38°22′	0°53′	445
18	Orihuela (Los Desamparados)	38°04′	0°58′	26
19	Pego Convento	38°50′	0°06′	70
20	Pinoso (C.H.S.)	38°24′	1°02′	575
21	Rojales (El Molino)	38°05′	0°42′	31
22	San Vicente del Raspeig	38°23′	0°32′	115
23	Tàrbena (C.H.J. Poble de Dalt)	38°41′	0°06′	587
24	Tibi (Taleca)	38°31′	0°34′	538
25	Vall de Laguard (Fontilles)	38°46′	0°05′	250
26	Villena (La Vereda)	38°41′	0°55′	533

) [56].

In order to gain a more in-depth understanding of rainfall irregularity and intensity and assess its potential erosivity in the province of Alicante, the methodology developed by Martín-Vide [2] to calculate the daily precipitation CI was implemented for the 26 meteorological stations. The CI was used to analyse the structure of the precipitation amounts from the accumulated number of days with precipitation. This index, which is based on negative exponential curves, evaluates the differences between the percentages of rainfall corresponding to the different classes [2]. Therefore, the daily amounts of precipitation are classified into 1 mm classes, with the initial class being [0.1–0.9], with a successive increasing order. A possible limitation of using 1 mm classes for the calculation of the CI lies in the resolution of the rain gauges, particularly in the lower rainfall range (0.1–0.9 mm). This could introduce a bias in the frequency of low-intensity events, especially if stations have problems with resolution or recording. To mitigate this risk, strict quality control was applied to the data series and stations where irregularities in the frequency distribution were detected and were excluded. An example is described below:

Table 2. Frequency distribution for 1 mm classes (N), relative cumulative frequencies X, and the corresponding percentages of the total precipitation Y in Alicante (Ciudad Jardín, 1981–2020).

Class	Ma (Midpoint)	Ni	∑Ni	Pi (Ma×Ni)	∑Pi	X = (∑Ni%)	Y = (∑Pi%)
0.9	0.5	900	900	450	450	38.69	3.63
1.9	1.5	351	1251	526.5	976.5	53.78	7.87
2.9	2.5	216	1467	540	1516.5	63.07	12.22
3.9	3.5	155	1622	542.5	2059	69.73	16.59
4.9	4.5	89	1711	400.5	2459.5	73.56	19.82
5.9	5.5	82	1793	451	2910.5	77.09	23.45
6.9	6.5	65	1858	422.5	3333	79.88	26.85
7.9	7.5	57	1915	427.5	3760.5	82.33	30.30
8.9	8.5	53	1968	450.5	4211	84.61	33.93
9.9	9.5	41	2009	389.5	4600.5	86.37	37.07
10.9	10.5	34	2043	357	4957.5	87.83	39.94
11.9	11.5	16	2059	184	5141.5	88.52	41.43
12.9	12.5	27	2086	337.5	5479	89.68	44.14
13.9	13.5	21	2107	283.5	5762.5	90.58	46.43
14.9	14.5	18	2125	261	6023.5	91.36	48.53
15.9	15.5	15	2140	232.5	6256	92.00	50.41
16.9	16.5	15	2155	247.5	6503.5	92.65	52.40
17.9	17.5	9	2164	157.5	6661	93.04	53.67
18.9	18.5	13	2177	240.5	6901.5	93.59	55.61
19.9	19.5	14	2191	273	7174.5	94.20	57.81
20.9	20.5	5	2196	102.5	7277	94.41	58.63
21.9	21.5	9	2205	193.5	7470.5	94.80	60.19
22.9	22.5	12	2217	270	7740.5	95.31	62.37
23.9	23.5	6	2223	141	7881.5	95.57	63.50
24.9	24.5	11	2234	269.5	8151	96.04	65.67
25.9	25.5	5	2239	127.5	8278.5	96.26	66.70
26.9	26.5	5	2244	132.5	8411	96.47	67.77
27.9	27.5	8	2252	220	8631	96.82	69.54
28.9	28.5	6	2258	171	8802	97.08	70.92
29.9	29.5	4	2262	118	8920	97.25	71.87
30.9	30.5	2	2264	61	8981	97.33	72.36
31.9	31.5	4	2268	126	9107	97.51	73.38
32.9	32.5	6	2274	195	9302	97.76	74.95
33.9	33.5	4	2278	134	9436	97.94	76.03
34.9	34.5	1	2279	34.5	9470.5	97.98	76.30
35.9	35.5	4	2283	142	9612.5	98.15	77.45
36.9	36.5	3	2286	109.5	9722	98.28	78.33
37.9	37.5	1	2287	37.5	9759.5	98.32	78.63
38.9	38.5	2	2289	77	9836.5	98.41	79.25
40.9	40.5	4	2293	162	9998.5	98.58	80.56
41.9	41.5	2	2295	83	10,081.5	98.67	81.23
42.9	42.5	4	2299	170	10,251.5	98.84	82.60
43.9	43.5	1	2300	43.5	10,295	98.88	82.95
44.9	44.5	2	2302	89	10,384	98.97	83.67
45.9	45.5	1	2303	45.5	10,429.5	99.01	84.03
46.9	46.5	3	2306	139.5	10,569	99.14	85.16
48.9	48.5	1	2307	48.5	10,617.5	99.18	85.55
49.9	49.5	1	2308	49.5	10,667	99.23	85.95
52.9	52.5	1	2309	52.5	10,719.5	99.27	86.37
53.9	53.5	1	2310	53.5	10,773	99.31	86.80
54.9	54.5	2	2312	109	10,882	99.40	87.68
55.9	55.5	1	2313	55.5	10,937.5	99.44	88.12
59.9	59.9	1	2314	59.9	10,997.4	99.48	88.61
61.9	61.5	1	2315	61.5	11,058.9	99.53	89.10
67.9	67.5	1	2316	67.5	11,126.4	99.57	89.65
68.9	68.5	1	2317	68.5	11,194.9	99.61	90.20
75.9	75.5	1	2318	75.5	11,270.4	99.66	90.81
86.9	86.5	1	2319	86.5	11,356.9	99.70	91.50
90.9	90.5	1	2320	90.5	11,447.4	99.74	92.23
109.9	109.5	1	2321	109.5	11,556.9	99.79	93.12
112.9	112.5	1	2322	112.5	11,669.4	99.83	94.02
119.9	119.5	1	2323	119.5	11,788.9	99.87	94.98
131.9	131.5	1	2324	131.5	11,920.4	99.91	96.04
220.9	220.5	1	2325	220.5	12,140.9	99.96	97.82
270.9	270.5	1	2326	270.5	12,411.4	100	100
N 65	Total	2326	140,696	12,411.4	54,0473.2	6048.84	4354.65

contains the data of the observatory of the city of Alicante for the period of 1981–2020.

The first column of Table 2 shows the upper limits of the absolute class frequencies in ascending order (class), while the second column (Ma) shows the midpoints (marks) of the absolute frequencies. The third column (Ni) is the number of days of precipitation recorded in each class. In this example, 900 days of rainfall were recorded with amounts ranging from 0.1 to 0.9 mm, and 351 days with amounts between 1.0 and 1.9 mm. On the day when the greatest precipitation was observed, the amount was between 270.0 and 270.9 mm. In total, there were 2326 days of rainfall, distributed into 65 classes. The fourth column (∑Ni) shows the accumulated frequencies, which are the consecutive sums of the days of rainfall of each frequency (Ni). The value in the last class should correspond to the total number of days (2326). The fifth column (Pi) is the result of multiplying the second column (Ma) and third column (Ni), which is the total rainfall of each class. In the sixth column (∑Pi), the values refer to the accumulated sums of the fifth column (Pi); therefore, the final value (54,0473.2 mm) is the total precipitation for the period of 1981–2020. Finally, columns X and Y are the percentages of columns ∑Ni and ∑Pi, respectively.

These results can be shown graphically (Figure 5) through the accumulated percentage of days with rainfall (∑Ni %) on the X axis and the accumulated percentage of the amounts of rainfall (∑Pi %) on the Y axis, and can be interpreted as follows: on 38.69% of the rainy days, an amount equal to or less than 0.9 mm was recorded, accounting for only 3.63% of total rainfall.

By calculating the percentage of the contribution of each frequency to the annual total, it can be observed that the less frequent but more abundant amounts can have a considerable weight in the total amount. These percentages are related to positive exponential curves or normalised rainfall curves [2]:

$$Y=aX e^{bX},$$

(1)

where a and b are constants.

The a and b constants can be determined through minimum least squares:

$$\mathrm{I}\mathrm{n} a={\displaystyle \frac{\sum x_i^2\sum \ln y_i+\sum x_i\sum x_i\mathrm{l}\mathrm{n}{x}_i-\sum x_i^2\sum \mathrm{l}\mathrm{n}{x}_i-\sum x_i\sum x_i\mathrm{l}\mathrm{n}{y}_i}{N\sum x_i^2-(\sum x_i{)}^2}},$$

(2)

$$b={\displaystyle \frac{N\sum x_i\ln y_i+\sum x_i\sum \mathrm{l}\mathrm{n}{x}_i-N\sum x_i\mathrm{l}\mathrm{n}{ x}_i-\sum x_i\sum \mathrm{l}\mathrm{n}{y}_i}{N\sum x_i^2-(\sum x_i{)}^2}},$$

(3)

where N is the number of classes of values.

The two constants (a and b) that define the integral of the exponential curve between 0 and 100 are determined, representing the area under the curve, defined as:

$$S={\left[{\displaystyle \frac{a}{b}}{e}^{bx}\left(x-{\displaystyle \frac{1}{b}}\right)\right]}_0^{100},$$

(4)

The area S′ compressed between the curve and the equidistribution line is the difference between 5000 (area of the triangle below the equidistribution line) and the value of S, to find the area between the curve, the equidistribution line, and ordinate 100 (S′). With this value, the CI is defined as:

$$CI={\displaystyle \frac{S^{\prime }}{5000}}$$

(5)

By applying the CI, higher values are obtained in meteorological stations that have a higher concentration of daily precipitation.

The results obtained from applying the CI are represented cartographically. To do so, the inverse distance weighted interpolation (IDW) algorithm [57] was used with ArcGis Pro (3.1.0) software.

In order to determine the origin of the air masses that caused extreme episodes in the province of Alicante for the period of study of 1981–2020, the dates and locations where torrential rain of over 200 mm/day occurred were selected to calculate the inverse trajectories of the air masses according to the dispersion and atmospheric transport modelling system HYSPLIT. HYSPLIT was developed by the National Oceanic and Atmospheric Administration (NOAA). It is a complete system that allows us to calculate the trajectories of simple air particles and their dispersion, chemical transformation, and deposition [58]. Unlike the Eulerian model (latitude/longitude and elevation system), the Lagrangian models use a reference system that follows the middle atmospheric movement. The calculation of the back trajectories is one of the most frequent applications of this model, used to determine the origin of the air mass for a specific established location, determining the relationships between the source and receiver [59]. In this case, 26 dates were selected with different locations, using the geographical coordinates of the meteorological stations that recorded extreme events equal to or greater than 200 mm/day as the end of the trajectory. The entry parameters of the model were established with a normal trajectory, and the meteorological data used for the calculation were those of the Reanalysis (global, 1948–present) of the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) [60]. The configuration of the model was based on the back trajectories on the surface at 0 m [61] as a first level and 5500 m at 500 hPa (to identify possible cut-off lows) as a second level. With these adjustments, the back trajectories were obtained, with a total execution time of 72 h, with an exit at 00:00 and intervals of 6 h for each of the specific dates during the period of 1981–2020.

3. Results

3.1. Concentration Index (CI)

Applying the CI enabled us to analyse the quality of the data of the group of meteorological stations in terms of the distribution of the absolute frequencies of the daily amounts of precipitation. This is because the statistical structure of the daily precipitation is distributed in amount frequencies, which, in general, take the form of negative exponential distributions [2]. The reason for this is that when the daily precipitation amounts are classified according to their absolute frequencies they diminish exponentially, beginning with the lowest class. Therefore, the general structure is that of the occurrence of many small daily amounts of precipitation, while large daily amounts are rare.

It is not surprising that several data issues occurred in the observations, either because they were not a priority or because the monitoring was not performed correctly by the meteorological station. Therefore, days of rainfall with small amounts may have been lost or consecutive days of precipitation may have been concentrated. This loss mostly occurs in the frequencies of small amounts of precipitation due to a lack of monitoring by the observers on night shifts, public holidays, or during the holiday season, and this leads to a distribution of frequencies that follow a positive exponential function instead of a negative one. The distribution of the frequency intervals of narrow classes enables us to identify which meteorological stations do not collect the minimum precipitation values correctly, as the distribution in 1 mm classes has to follow a negative exponential distribution and not a positive one.

As can be observed in the case of the municipality of Elche, which has five meteorological stations, the results provided by the CI are discordant, with abnormal values in the number of days of rain with minimum precipitation (absolute frequency of 0.1–0.9 mm). For example, in the Elche (C.H.S.) meteorological station, in the 0.1–0.9 mm interval, 204 days of precipitation were recorded, with the total number of rainy days being 1399 for the 40-year period. In the Elche (Campo Agrícola) stations, in the 0.1–0.9 mm interval, 303 days with precipitation were recorded, with a total of 1413 rainy days for the same period. The comparison of these values with nearby meteorological stations and their concentration indices highlights data inaccuracies in recording days with very light precipitation. As a result, these stations were excluded from the analysis. In the case of the municipality of Banyeres de Mariola, the distribution of the function is positive and should follow a negative exponential function because the first classes (those with the lower amount) should be the most numerous. However, the opposite is the case, as the frequency of the classes are more numerous as they advance. This is the same for the meteorological stations of Bolulla and Almudaina. However, these meteorological stations (

Table 3. Five meteorological stations rejected for CI analysis in the province of Alicante.

Code	Meteorological Station	Latitude	Longitude	Altitude (m)	a	b	CI
4	Almudaina	38°45′	0°21′	580	0.036	0.032	0.62
5	Banyeres de Mariola	38°42′	0°39′	816	0.064	0.026	0.56
7	Bolulla	38°40′	0°06′	240	0.044	0.030	0.60
12	Elche (C.H.S.)	38°15′	0°42′	85	0.041	0.030	0.61
13	Elche (Campo Agrícola)	38°14′	0°41′	63	0.035	0.032	0.61

) were not considered for the analysis and representation of the CI.

For the full period of 1981–2020, the CI values were calculated in absolute frequencies of 1 mm, according to the methodology of Martín-Vide [2], for the 21 meteorological stations considered valid for this analysis (

Table 4. Values of the constants a and b of the exponential curves, number of days with precipitation, and CI value for the 21 meteorological stations in the province of Alicante (1981–2020).

Code	Meteorological Station	a	b	Rainy Days	CI
1	Agost (Escuela Nacional)	0.049	0.029	1401	0.58
2	Alicante	0.014	0.041	2326	0.68
3	Alicante-Elx (Airport)	0.014	0.041	2315	0.68
6	Beneixama (Casa Crespo)	0.035	0.033	2430	0.61
8	Callosa de Segura	0.021	0.037	1827	0.65
9	Callosa d’en Sarrià (El Algar)	0.019	0.038	1958	0.67
10	Callosa d’en Sarrià (Tossal de Salomó)	0.017	0.039	2208	0.67
11	Elche	0.017	0.039	2147	0.67
14	Gata de Gorgos	0.013	0.042	2198	0.69
15	Jijona	0.031	0.034	2108	0.62
16	La Marina de Elche	0.021	0.037	1844	0.66
17	La Romana (Algesar)	0.049	0.029	1701	0.58
18	Orihuela (Los Desamparados)	0.020	0.037	1791	0.67
19	Pego (Convento)	0.019	0.038	2153	0.67
20	Pinoso (C.H.S.)	0.038	0.032	1957	0.60
21	Rojales (El Molino)	0.010	0.044	2234	0.71
22	San Vicente Del Raspeig	0.028	0.034	1724	0.64
23	Tàrbena (C.H.J. Poble de Dalt)	0.021	0.037	2052	0.67
24	Tibi (Taleca)	0.050	0.029	1778	0.59
25	Vall de Laguard (Fontilles)	0.035	0.032	1961	0.62
26	Villena (La Vereda)	0.028	0.035	2403	0.63

) after having rejected meteorological stations in which imprecisions were detected, as described above.

For the period of study, the extreme values of the CI are between 0.58 and 0.71 (a difference of 0.13 hundredths). The CI values fluctuate between 0.58 in Agost (Escuela Nacional) and La Romana (Algesar), in the interior of the province, and 0.71 in Rojales (El Molino), in the pre-coastal part of the south of the province. In the coastal area, the values of 0.68 in Alicante (Ciudad Jardín) and Alicante-Elx (Airport) particularly stand out, together with the high values in the north of the province in Gata de Gorgos (0.69), Tárbena (CHJ), Callosa d’en Sarrià (El Algar), Pego (Convento), and Callosa d’en Sarrià (Tossal de Salomó), with 0.67.

For this range of CI values (0.58–0.71), the mean is established at 0.65. This value coincides with the 25% of days in which most rainfall is concentrated, with approximately 75% of total precipitation, while 45% of total rainfall is concentrated in 10% of the days (therefore, 90% of the remaining days represent 55% of total recorded precipitation), as can be seen in the meteorological stations of Callosa de Segura and La Marina de Elche. Therefore, higher values of CI concentrate more precipitation in a few days. This is broadly related to the erosive and aggressive nature [62] of the large amounts of precipitation concentrated in a short space of time.

With respect to the spatial distribution of the CI of daily precipitation represented in Figure 6, the pattern of this distribution for the province of Alicante is determined through isopleths. The highest values are recorded in the far east of the northern part of the province (with a maximum index in Gata de Gorgos of 0.69) and the southern half of the coast (up to 0.71 in Rojales). In general, a reduction in the CI value from the coast can be observed, with maximum values of between 0.67 and 0.71 and, towards the interior of the province, lower values of almost 0.60 in three nuclei in Tibi (0.59), Agost, and La Romana (0.58). In this study, the isopleth 0.65 is the average value (for the range of 0.58–0.71) where the threshold that discriminates the regions with a high concentration of daily precipitation is located. On the other hand, for the Iberian Peninsula as a whole, Martín-Vide [2] determined the discrimination threshold with a high concentration as the isopleth 0.61. Therefore, the province of Alicante as a whole has high values of concentration of daily precipitation.

The highest CI values are found in the meteorological stations closest to the coastal strip, both in the north and the south of the province. This is due to the high influence of the Mediterranean component fronts, which are prominent players in the torrential events recorded in the province. The Mediterranean Sea factor exercises a fundamental influence of triggering extreme episodes of high intensity and rainfall concentration.

The percentages of total precipitation were calculated for 25% (Pi 25%) and 10% (Pi 10%) of the rainiest days for each observatory using exponential concentration curves (Equation (1)), where the values of the constants a and b are substituted (

Table 5. Values of the constants a and b of the exponential curves, of the CI, and of the percentage that represents 25% and 10% of the rainiest days of the 21 meteorological stations of the province of Alicante (1981–2020).

Code	Meteorological Station	a	b	CI	Pi (25%)	Pi (10%)
1	Agost (Escuela Nacional)	0.049	0.029	0.58	67.50	39.61
2	Alicante	0.014	0.041	0.68	76.96	49.06
3	Alicante-Elx (Airport)	0.014	0.041	0.68	76.86	48.53
6	Beneixama (Casa Crespo)	0.035	0.033	0.61	70.08	41.46
8	Callosa de Segura	0.021	0.037	0.65	74.13	45.77
9	Callosa d’en Sarrià (El Algar)	0.019	0.038	0.67	75.46	48.15
10	Callosa d’en Sarrià (Tossal de Salomó)	0.017	0.039	0.67	75.66	47.44
11	Elche	0.017	0.039	0.67	75.71	47.27
14	Gata de Gorgos	0.013	0.042	0.69	77.30	49.03
15	Jijona	0.031	0.034	0.62	70.71	41.52
16	La Marina de Elche	0.021	0.037	0.66	74.46	46.37
17	La Romana (Algesar)	0.049	0.029	0.58	67.51	39.75
18	Orihuela (Los Desamparados)	0.020	0.037	0.67	75.25	47.93
19	Pego (Convento)	0.019	0.038	0.67	75.53	47.92
20	Pinoso (C.H.S.)	0.038	0.032	0.60	69.20	40.64
21	Rojales (El Molino)	0.010	0.044	0.71	79.25	51.51
22	San Vicente Del Raspeig	0.028	0.034	0.64	72.38	44.70
23	Tàrbena (C.H.J. Poble de Dalt)	0.021	0.037	0.67	75.48	49.04
24	Tibi (Taleca)	0.050	0.029	0.59	67.55	39.94
25	Vall de Laguard (Fontilles)	0.035	0.032	0.62	71.02	43.75
26	Villena (La Vereda)	0.028	0.035	0.63	71.58	42.54

), and X corresponds to the value subtracted from one hundred percent of the days for which we wish to obtain the total percentage contributed.

The extreme percentage values of Pi 25% (Figure 7a) and Pi 10% (Figure 7b) are found in the same stations as those that recorded the maximum (Rojales 0.71) and minimum (Agost 0.58) concentration index values. There are some percentage variations for Pi 25% of 11.75% and for Pi 10% of 11.9%, indicating the difference in concentration existing between the coastal and pre-coastal areas and the more interior area of the province, which is interpreted in the following way. If the highest CI value corresponding to Rojales (0.71) is taken, this means that 25% of the rainiest days represent 79.25% of total precipitation, while 10% of the rainiest days representing more than half of total precipitation (51.51%). In this case, Rojales has 2234 days of precipitation, with 25% of these days comprising 558.5 days, while 10% comprises 223.4 days. On the other hand, the minimum value of the concentration index is found in Agost and La Romana (0.58), where 25% of the rainy days (350.25 days of the total 1401 rainy days) accounts for 67.50% of total precipitation and 10% (140.1 days) represents 39.61% of total precipitation for the 40-year period.

For the case of the city of Alicante, 25% of the rainiest days represent a concentration of 76.96% of total precipitation and only 10% of the rainiest days represent a concentration of almost half of total precipitation (49.06%) recorded by this observatory for the 40-year period analysed.

3.2. Analysis of the Backward Trajectories of the HYSPLIT Model

The use of the NOAA’s HYSPLIT model determines the back trajectories of the episodes in which a precipitation equal to or greater than 200 m/day in the province of Alicante was recorded. Twenty-six dates were obtained in which one or more of the 21 meteorological stations selected in this research observed a precipitation higher than the established threshold. In order to determine the receiving location of the air mass in the case where there were several meteorological stations that exceeded the threshold, the one recording the highest amount was taken (

Table 6. Classification of the back trajectories of the 26 dates with records >200 mm/day in the province of Alicante (1981–2020).

Season	Date	Meteorological Station	mm/day	Origin of Air Masses
				At altitude	On the surface
Autumn (16)	19 October 1982	Alicante-Elx (Airport)	235.0	Atlantic	Atlantic
	15 November 1985	Pego (Convento)	249.0	North African	Mediterranean
	29 September 1986	Tàrbena (C.H.J. Poble de Dalt)	241.1	Atlantic	Northern
	03 November 1987	Pego (Convento)	371.5	North African	Mediterranean
	04 November 1987	Orihuela (Los Desamparados)	316.0	Atlantic	Mediterranean
	30 September 1988	Gata de Gorgos	206.0	Atlantic	Mediterranean
	04 September 1989	Tàrbena (C.H.J. Poble de Dalt)	255.6	Atlantic	Mediterranean
	05 September 1989	Gata de Gorgos	201.0	Atlantic	Mediterranean
	30 September 1997	Alicante	270.2	North African	Mediterranean
	11 November 1999	Pego (Convento)	237.7	Northern	Northern
	23 Octpber 1900	Vall de Laguard (Fontilles)	226.0	North African	Mediterranean
	11 October 2007	Vall de Laguard (Fontilles)	371.2	Northern	Northern
	12 October 2007	Gata de Gorgos	258.9	Northern	Mediterranean
	11 November 2012	Vall de Laguard (Fontilles)	230.0	Atlantic	Mediterranean
	11 September 2019	Vall de Laguard (Fontilles)	215.0	Northern	Northern
	12 September 2019	Orihuela (Los Desamparados)	230.8	North African	Mediterranean
Winter (7)	04 December 1997	Vall de Laguard (Fontilles)	400.0	Atlantic	Northern
	25 January 2010	Tàrbena (C.H.J. Poble de Dalt)	236.0	Atlantic	Mediterranean
	19 December 2016	Vall de Laguard (Fontilles)	213.0	North African	Mediterranean
	21 January 2017	Tàrbena (C.H.J. Poble de Dalt)	338.0	North African	Mediterranean
	19 January 2020	Tàrbena (C.H.J. Poble de Dalt)	277.0	Atlantic	North African (SW)
Spring (5)	03 May 1992	Callosa d’en Sarrià (El Algar)	201.0	Northern	Northern
	08 April 1997	Callosa d’en Sarrià (El Algar)	230.0	North African	Mediterranean
	06 May 2002	Pego (Convento)	295.1	Atlantic	Northern
	15 April 2003	Vall de Laguard (Fontilles)	202.5	North African	Mediterranean
	21 April 2019	Gata de Gorgos	226.0	North African	Mediterranean

). Using the NOAA’s dispersion and transport model, based on the reanalysis (global, 1948-present) conducted by the NCEP–NCAR, 26 backward trajectories were obtained to analyse the origin of the air masses 72 h before the torrential rains occurred in different points of the province of Alicante on the given dates. Seven types of origin were established in the backward trajectories, described in the final column of Table 6 for each of the events.

Table 7. Absolute frequencies of trajectory flow origins (72 h before) at altitude (500 hPa) and on the surface.

Origin of Air Masses from the Back-Trajectories		Absolute Frequencies	%
At Altitude	On the Surface
North African	Mediterranean	8	28.57
Atlantic	Mediterranean	5	17.86
Northern	Northern	6	21.43
Atlantic	Northern	2	7.14
North African	Northern	2	7.14
Atlantic	West (Atlantic, Iberian, or North African SW)	3	10.71

shows the frequency distribution in accordance with six types of origins of the trajectories established in this analysis. The first group with the most relevant result is attributed to the origin of air masses, which, at height (500 hPa), come from northern Africa, with particular attention on the final periods of their trajectory, and at surface level, come from the east (32.14%) (Figure 8). Furthermore, the definition of the cyclonic edges in the flows at height, which determine the direction of the cyclonic swirl of the low pressures, should be noted. The second most represented group is that of the northern trajectories (21.43%) both at height and on the surface. The flows at height usually originate from the northeastern coast of North America (Figure 9a), Greenland (Figure 9b), or the British Isles (Figure 9c). The origin of the surface flows is usually France and, in the later periods of the trajectory in a north–south direction, the Mediterranean. The total cases analysed that describe this kind of trajectory occur in the meteorological stations located in the north of the province of Alicante (Vall de Laguard, Tàrbena, Pego (Convento), and Callosa d’en Sarrià (El Algar)). Third, the trajectories originating in the Atlantic at height and those from the Mediterranean on the surface were classified (17.86%). The first and third group often describe a long maritime course on the surface over the Mediterranean. This is a fundamental factor for explaining the genesis of torrential rains in the province. These three groups represent 67.86% of the most frequent backward trajectories that give rise to extreme events. There are three less relevant categories when the origin of the flow at height is the Atlantic and on the surface is from the north, and when the flow on the surface is North African and from the north (7.14%). Finally, there is a group of three categories (10.71%) that describe the same origin at height—the Atlantic—but have flows on the surface that, although from the west, have an Atlantic, Iberian, or North African origin.

4. Discussion

In accordance with the results obtained, the research question can be answered. The CI significantly contributes to improving the knowledge of the torrentiality of the precipitation in the province of Alicante. This index enables us to quantify the concentration of the precipitation in a few days of the year, a common phenomenon in Mediterranean regions and particularly notable in Alicante. When a quantitative value of this concentration is provided, the CI shows the uneven distribution of the rainfall, revealing how in certain areas, particularly the coastal and pre-coastal areas, a large part of the annual precipitation is concentrated in short and high-intensity events.

The results obtained from the CI show a significant geospatial variation in the province of Alicante, with values fluctuating between 0.58 and 0.71. These results indicate a concentration of the precipitation in a few days, particularly in coastal and pre-coastal areas. This fact is reflected in studies carried out on a global scale that confirm the correlation between the high concentration of precipitation and physical processes such as convection in areas with high solar radiation and warm tides, which is the case of the Iberian Peninsula and, specifically, southeast Spain [30]. Cortesi et al. [31], on the other hand, focused on the concentration of the daily precipitation throughout Europe between 1971 and 2010. With 530 precipitation series, they determined that annual CI values ranged from 0.51 to 0.72. The distribution pattern of intense rainfall across the continent highlighted a notable NW–SE gradient. The highest indices were found in the Western Mediterranean basin, on the coasts of Italy, France, and, in particular, Spain (indices > 0.66) in the stations of the Spanish Mediterranean coast. This suggests a susceptibility in Spain to extreme concentrated rain events, particularly in semi-arid areas, which has implications for the management of water and flood risk.

Mathbout et al. [32] examined the space–time variability of the daily concentration of precipitation for the Mediterranean region between 1975 and 2015 for 233 meteorological stations, resulting in CI values of between 0.57 and 0.70. The highest CI values were observed in the Western basin of the Mediterranean along the Spanish and French coasts. For Spain, the strongest gradient in the CI values was detected between the west and east of the Iberian Peninsula. However, other research has also observed regional differences between higher CI values in the southeast of the Iberian Peninsula and lower values in the north [35].

At the regional scale, in the Valencian Community, De Luis et al. [62,63] analysed the spatial distribution of the CI of daily precipitation and its aggressiveness (1961–1990), showing a gradient of degradation from the coastal strip towards the interior. The highest values of the index (0.69) are concentrated in coastal areas, while inland they progressively decrease, reaching minimum values at around 0.56, where the precipitation concentration is lower. Furthermore, it should be noted that rainfall aggressiveness is concentrated in a small number of rainy days. In the southeast of the peninsula, specifically, it is shown that between 50 and 60% of the annual erosion occurs in only three or four days.

In the present study, high CI values were recorded in stations close to the sea, such as those of Rojales (0.71), Alicante (0.68), and the two stations in Callosa d’en Sarrià (0.67), while the lowest values were found in interior areas of the province, such as Agost and La Romana (0.58). This pattern coincides with the results of previous studies on the Iberian Peninsula. According to Martín-Vide [2], the values recorded in 32 meteorological stations between 1951 and 1990 fluctuate between 0.55 on the Atlantic seaboard (Ourense) and the highest values in the eastern Mediterranean strip, reaching maxima in Tortosa (0.69), Valencia (0.70), Alicante (0.68), and Murcia (0.67), influenced by Mediterranean depressions. The CI data for the city of Alicante coincide with both of these research projects. Furthermore, Benhamrouche and Martín Vide [14] propose a methodological advance in the analysis of CI, defining different class intervals: 1 mm, 5 mm, 10 mm, and the Gini index for the period of 1951–2010 and the 32 meteorological stations of peninsular Spain used by Martín-Vide [2]. The Gini index, traditionally used in economics to measure income inequality, has been adapted to precipitation studies to quantify the disparity in daily rainfall distribution within a series. It complements the CI by providing an alternative measure of concentration. Higher Gini values indicate a greater concentration of rainfall in fewer days, aligning closely with CI trends. In this context, the highest values are found in Tortosa and Valencia (0.69), Alicante (0.68), and Murcia (0.67), with the eastern strip of the Iberian Peninsula recording the highest daily concentration of precipitation. The result obtained by these authors for the Alicante meteorological station coincides with that obtained in this publication, with the CI value being 0.68 in all of them.

In a similar study to this one, Benhamrouche and Martín Vide [15] obtained the CI for the province of Alicante with a distribution of 5 mm class intervals. A broader distribution of classes enables the use of more meteorological stations, as the measurement of daily precipitation data does not require high precision. The CI values obtained from the 5 mm class intervals fluctuate between 0.54 in Ibi in the interior of the province and 0.67 in Benissa and Gata de Gorgos in the north of the province. The lowest values are found in Ibi (0.54), Banyeres de Mariola (0.55), and Agost (0.58), and the highest values are found in Callosa d’en Sarrià (0.66), Bolulla, and Alicante (0.65). These values are lower than those obtained in the present research, where in Gata de Gorgos a CI of 0.69 (2 hundredths more) in both the Callosa d’en Sarrià (0.67 (1 hundredth more)) and the Alicante (0.68 (3 hundredths more)) station, was obtained. Comparing the results of both investigations, there are differences of between 1 and 4 hundredths for the same weather stations in both investigations. This may be due to the time period used and the definition of classes, with both results showing that the spatial distribution of the CI in the province is influenced by the Mediterranean Sea, as the highest values are always found in areas close to the coast.

The average CI values obtained for Alicante (0.65) are higher than the daily precipitation concentration discrimination threshold for the Iberian Peninsula, established at 0.61 [2], which places Alicante in a category of high precipitation concentration in comparison with the peninsula average. In general terms, a value of 0.61 indicates that 70% of total precipitation is concentrated in 25% of the rainiest days. In this study, the percentages of total precipitation corresponding to 25% (Pi 25%) and 10% (Pi 10%) of the rainiest days were calculated in each observatory using exponential concentration curves. The extreme values of Pi 25% and Pi 10% coincide with the maxima and minima of the CI, such as in Rojales (CI 0.71) and Agost (CI 0.58). In Rojales, 25% of the rainiest days account for 79.25% of total precipitation and 10% of the rainiest days to 51.51%. In Alicante, 25% of days with rain account for 76.96%, reflecting the difference in concentration between coastal and interior areas.

This phenomenon can be attributed partly to its proximity to the Mediterranean Sea, which generates atmospheric conditions that favour concentrated precipitation events that are more pronounced than in other areas of the Iberian Peninsula. This difference underlines the importance of considering regional variations in precipitation distribution studies, as local factors such as the relief and closeness to the sea can substantially alter the rainfall patterns within the same climate region [35].

The CI trends in the case of Europe [31] do not display a significant uniform pattern in the increase in precipitation concentration. For the Mediterranean basin region, a growing trend can be observed in 61% of the stations used in this study [32]. For peninsular Spain, the precipitation concentration increases in the period of 1946–2005 [35]. This positive trend is also confirmed between the sub-periods of 1951–1980 and 1981–2010 for mainland Spain [14]. In the case of Spain, the CI trend is positive during the whole period of study (1950–2012) [17]. In the Canary Islands, for the period of 1970–2013, the majority of the series analysed reveal a trend towards a more intense concentration of precipitation [16].

The results obtained through the analysis of back trajectories with the HYSPLIT model in the province of Alicante show significant similarities to and differences from the findings of Meseguer-Ruiz et al. [48] in the Atacama Desert. While in the Chilean region extreme precipitation is mainly associated with oceanic and continental flows, in Alicante there is a pre-eminence of high-altitude air masses—North African and Atlantic flows—that interact with the local relief and humid flows from the Mediterranean Sea to generate torrential rainfall. In both cases, the HYSPLIT model highlights the importance of trajectories in the intensification of extreme events, although the differences in climatic contexts—arid in the Atacama and the Mediterranean Sea in Alicante—underline the influence of the regional environment in the modulation of rainfall. These results reinforce the usefulness of the HYSPLIT model not only for understanding the underlying atmospheric dynamics but also for designing effective risk management strategies and early warning systems adapted to the characteristics of each region. In the western Mediterranean, Cloux et al. [49] highlights the relevance of remote sources such as the tropical Atlantic, whose influence is less in Chile but could be compared with the long-haul humid flows from the Amazon. In addition, torrential rains in Alicante share with the western Mediterranean the importance of convective processes fuelled by local humidity. These contrasts underline how the orography and the regional climatic environment condition the trajectories and sources of humidity that intensify extreme events in each region.

5. Conclusions

This study has allowed us to make significant progress in our understanding of the statistical structure of precipitation in the province of Alicante, providing a detailed analysis of the daily concentration of precipitation using the CI. This index has proven to be an essential tool for understanding the temporal distribution of precipitation, especially in a region characterised by extreme events and intense rainfall concentrated in a few days. The results obtained show CI values that range between 0.58 in inland areas and 0.71 in the pre-coastal and coastal areas of the south, confirming a high concentration of precipitation in the region. On average, 25% of the rainiest days represent a concentration of 75% of the annual precipitation, highlighting the marked irregularity of the Mediterranean rainfall regime.

The high concentration of daily precipitation in Alicante, assessed using the CI, is also inherent to the neighbouring provinces. As a significant example, during the catastrophic DANA (isolated depression at altitude) at the end of October 2024 in the province of Valencia and nearby areas, some meteorological stations accumulated more than 700 mm in a single day (29 October 2024), far exceeding their annual average. This event highlights the importance of studying and monitoring the concentration of precipitation in the context of recurrent extreme events in the Mediterranean region.

The application of the HYSPLIT model has been key to identifying the trajectories of the air masses responsible for torrential events, providing valuable information on their origin and behaviour. The trajectories analysed reflect the predominant influence of air masses of North African, Atlantic, and Mediterranean origin. In particular, altitude flows from North Africa, combined with humid Mediterranean surface flows, have been identified as determining factors in the most extreme episodes. These findings underline the importance of interactions between atmospheric conditions at different levels and local orography in generating intense and concentrated rainfall.

This study not only validates the usefulness of CI and trajectory analysis as complementary tools to characterise precipitation patterns in regions vulnerable to extreme events but also offers practical implications for flood risk management and territorial planning. The results obtained allow for the design of adaptation strategies and early warning systems tailored to local conditions. In a context of climate change, where an increase in the intensity and frequency of extreme events is expected, this integrated approach is essential.

Future lines of research could focus on the application of CI and the HYSPLIT model in other Mediterranean regions, exploring in greater detail the role of interactions between moisture sources and local atmospheric dynamics. Likewise, it would be valuable to incorporate predictive climate models to assess how climate change could modify these precipitation patterns in the future, thus improving the capacity to respond to extreme events.