The Relationship Between The Atlantic Multidecadal Oscillation and The Urucuia Aquifer System Recharge

The Relationship Between The Atlantic Multidecadal Oscillation and The Urucuia Aquifer System Recharge | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Relationship Between The Atlantic Multidecadal Oscillation and The Urucuia Aquifer System Recharge Myrla de Souza Batista Vieira, José Eloi Gimarães Campos, Eber José de Andrade Pinto, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-806881/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract This study investigates and detects links between the precipitation characteristics with meteorological systems and teleconnections around the Urucuia Aquifer System (UAS). Several studies show the influence of meteorological systems and teleconnections on the volume and intensity of precipitation in South America, mainly the Atlantic Multidecadal Oscillation (AMO), the El Niño South Oscillation (ENSO), and the Pacific Decadal Oscillations (PDO). Then, the precipitation series’ statistical characterization impacted the aquifer system’s recharge from 1973 to 2006. Monthly and annual series were analyzed and tested the correlation analysis with the indexes of the AMO, PDO, and ENSO. Finally, the series of maximum daily rainfall on the UAS was determined, and the 15 largest events were chosen to analyze the retroactive trajectories of air masses and thus try to estimate which atmospheric systems was acting and their origin. It concluded that the total annual precipitation data indicated a decreasing linear trend and that external climatic phenomena can influence precipitation characteristics. The correlation with the AMO index revealed a potential teleconectivity between climate circulation patterns with average annual precipitation over the UAS (p-value ≤ 0.03). Moreover, the analysing of precipitation trajectories observed a greater amount of specific humidity in the atmosphere during the AMO negative period concerning the AMO positive period. Also, the negative AMO phase’s trajectories had higher latitudes closer to the Intertropical Convergence Zone, as opposed to the positive AMO phase, where the trajectory altitudes were lower and closer to the Capricorn tropic. Atmospheric Sciences Urucuia. Teleconexões. Precipitation. Atlantic Multidecadal Oscillation (AMO). Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Highlights Potential teleconectivity between patterns of climatic circulation and precipitation. No significant correlation was verified between precipitation and the Pacific Decadal Oscillation (PDO) index or Southern Oscillation Index (SOI) or the Multivariate El Niño South Oscillation (ENSO) index. The regulatory reserve of the Urucuia Aquifer System (UAS) is constituted exclusively from the infiltration of rainwater. It was not possible to correlate the high daily rainfall rates with the occurrence of a predominant atmospheric system. Introduction Current studies demonstrate that climate change causes warming or cooling in the atmosphere, which causes changes in moisture retention at high heights, influencing the rainfall regime over various planet regions. In the central part of South America, normally, the observed rain is characterized by tropical and subtropical precipitation, with a marked seasonal cycle over the continent. In winter, it experiences its dry season, and, in the summer, a low-pressure zone located approximately at latitude of 25°S forces the east winds over the Amazon basin to turn south. These winds channel the wet masses between the eastern hillslope of the Andes and the Brazilian Plateau, which feed the convective summer storms over the subtropical plains to latitudes of 35°S (Garreaud et al. 2009 ). Thus, creating the South America Monsoon System which marks a well-defined rainy season in the summer and a season with little rainfall in winter (Liebmann and Mechoso 2011 ). Associated with this monsoon system, there are the minimum pressure belt and the intense low-level convergence of trade winds over the equatorial oceans, which cause a band of clouds with great vertical development. This system surrounds the globe close to the equator, forming storm activities, generated due to solar heating and the vertical movement of trade winds, corresponding to the Intertropical Convergence Zone (ITZC), where the active precipitation is mostly of a convective nature, produced by deep cumulus-nimbus (Garreaud et al. 2009 ). It is important to note that the ITCZ has more zonal behavior between November and December over the southern hemisphere when it starts moving south. The monsoon systems of South America and ITCZ, acting together, make Brazilian southeast that are close to the Tropic of Capricorn not to have characteristics of arid and dry areas, such as the Sahara, Atacama, Kalahari, and Australia. The air masses' dynamics create the meteorological system called the South Atlantic Convergence Zone (SACZ), which is a typical summer system. This system is characterized by a band of cloudiness oriented in the northwest-southeast direction, whose area of operation encompasses the center-south of the Amazon and the center-west and southeast regions of Brazil, and can also reach the center-south of Bahia, the north of Paraná and extend to the South Atlantic Ocean (between the latitudes of 15S and 25S - southeast region of Brazil), associated with a convergence zone of the flow of humidity in the lower troposphere (Reboita et al. 2012 ). These systems have already been extensively studied and had their influence on rainfall occurrence (Marengo and Dias 2006 ; CEPED 2012 ; Reboita et al. 2012 ; Marshall et al. 2014 ; Gan et al. 2015 ; Escobar et al. 2016 ; Nielsen et al. 2019 ; Rodrigues et al. 2019 ). However, several current studies show that climate change impacts the rainfall pattern in the central part of South America (Ouachani et al. 2013 ; Ríos-Cornejo et al. 2015 ; Zhong et al. 2017 ; Silva et al. 2020 ), and also indicate the that changes are influenced by teleconnections, which are trends in associations between the variability of atmospheric pressure and circulation patterns that occur in one location, with their effect elsewhere (Limberger 2016 ). These studies show the relationship between teleconnections and precipitation in South America, with several modes of variability. The climatic teleconnections more studies are (IPCC 2007 ): Atlantic Multidecadal Oscillation (AMO): natural variability based on average anomalies of sea surface temperatures (TSM) in the North Atlantic. El Niño South Oscillation (ENSO): natural variability over the tropical Pacific Ocean, which originates when El Niño and the South Oscillation co-occur, with El Niño being a process of high pressure and heating of sea temperatures, and the Southern Oscillation an atmospheric component, associated with changes in sea temperature. Pacific Decadal Oscillation (PDO): pattern of climatic variability centered in the North Pacific Ocean, which occurs due to the variation in the temperatures of the Pacific Ocean, whose positive phase provides an increase in temperature, decrease in humidity and increase numbers of occurrences and the intensity of El Niño, and in the negative phase, a consequent decrease in temperatures, an increase in air humidity and a greater incidence and intensity of La Niña. In Figure 1 , the areas of teleconnection indexes presented the area for identifying teleconnection standards, adapted from https://meteorologia.unifei.edu.br/teleconexoes/ , (Souza and Reboita 2021 ) and the location of the UAS area. For example, the relationship of Atlantic Multidecadal Oscillation and El Niño Southern Oscillation and its influence on rainfall in South America, analyzed by Kayano and Capistrano ( 2014 ), showed that El Niño events, concomitant with the Cold AMO (CAMO) phase, are generally stronger than events in the Warm AMO (WAMO) phase due to negative temperatures in the inter-Pacific-Atlantic sea surface (Kayano and Capistrano 2014 ). Another example is the study in Chile, that showed that the relationships between low-frequency variability of precipitation and the PDO are significant for the north of the country, while connections with the AMO are significant for the north and also for the south and that this low-frequency variability in Chile appears to be largely linked to PDO and AMO modulation (Valdés-Pineda et al. 2018 ). The AMO's impact in the summer monsoon of South America was investigated in the La Plata River Drainage Basin (Chiessi et al. 2009 ). The authors concluded that the sea surface temperature and the anomalies of atmospheric circulation triggered by AMO would control the variability of the South American Monsoon System (SAMS). In Brazil, a study carried out in dry and rainy years in the Northeast of the country showed that the main difference in the anomaly patterns of sea surface temperature between the AMO phases is the differential positioning of the heating or cooling surface waters in the equatorial Atlantic. As a result, Hadley and Walker's anomalous circulations also show differences between the phases of the AMO, justifying the precipitation anomalies observed in tropical South America (Kayano et al. 2016 ). Therefore, to add more information about the climate interference on rainfall patterns, this study investigated the links between precipitation characteristics, especially volume and intensity, with meteorological systems and teleconnections. The objective is to detect correlations that help define better water availability scenarios, including assessing the issue of aquifer recharge, which has precipitation as a natural source. Because the regulatory reserve of aquifers is formed from the infiltration of rainwater. The area chosen to carry out this study is located on the Urucuia Aquifer System (UAS), a region experiencing an accelerated commercial agriculture expansion that began 30 years ago, to the point that it is currently considered one of the most important centers of irrigated agriculture of the country. However, the inherent scenario of high surface and underground water demand is accompanied by changes in vegetation cover, notably the suppression of extensive areas of Cerrado forest, and a high risk of contamination by fertilizers and pesticides, in addition to the inevitable urban expansion of certain outbreaks. Therefore, it is essential to know the region's water availability, considering the part of precipitation that will contribute to its recharge, influencing the regulatory reserve. If the meteorological systems and teleconnections influence the volume and intensity of precipitation, they must be considered in the management of water availability in the region of the UAS. The Urucuia Aquifer System - Uas The UAS is a groundwater reservoir located between latitudes 9º25'38'' and 16º15'00'' and longitudes 47º41'42'' and 43º46'43'' (Figure 2 a), under the geomorphological unit called Western Bahia Plateau or São Francisco Plateau (IBGE 2006 ). In an area of 121,653 km², this aquifer has altitudes ranging from 300 to more than 1000 m above sea level (Figure 2 b) with steep escarpments and the presence of colluviums, comprising essentially sandy soils (Figure 2 c). The climate in this region is classified as Tropical Savanna Climate (Aw), according to Köppen. It presents a wet season (excess water) corresponding to the summer, which occurs between October and April when the volume of precipitation is greater than evapotranspiration (Gaspar 2006 ). The dry season occurs from May to September, encompassing the whole Winter in South Hemisphere, and evapotranspiration is greater than precipitation, causing water deficit (Gaspar 2006 ). Therefore, during the period from May to September, the rivers of the region are maintained by the base flow, which according to Gaspar ( 2006 ), corresponds to a mean value of 1,35x109 m³/ano (42,8 m³/s), which on average is equal to 20% of the precipitation (6,86 x109 m³/ano or 1.241 mm) and 91% of the total streamflow (1,48 x109 m³/ano or 46,9 m³/s), based on a data series from 1982 to 2002. Also according to Gaspar ( 2006 ), the average temperatures vary annually between 26° and 20° C, and the relative humidity of the air varies between 80% (December) and 50% (August). However, in recent years, there has been climatic alteration, which points to an increase in the intensity of droughts in the region, as shown by the Drought Monitoring Project's maps (Figure 3 ) from the National Water Agency (ANA 2018 ). The Figure 3 shows the spatial spread of the drought intensity over Brazil's northeast region in December of 2014, 2015, and 2016. The green rectangle highlights the area where the Urucuia aquifer is located. Comparing the spatial distribution along the years, we observed that the drought intensity has been increasing in the region. Two hypotheses are considered to explain this pattern: just the effect of agricultural expansion in the region, or the climatic variation exerts influence too, whether to arising from Low-Frequency Variability associated with teleconnections, or global climate change. That is a point to be addressed in this work. Materials And Methods The average monthly rainfall series were initially obtained from the historical daily series of ten gauges in the area of the UAS. Table 1 displays general information on these. A simple descriptive statistical analysis was performed for these series to describe, evaluate, and summarize the data. Then, from these monthly precipitation data series, the annual precipitation data series for each station was defined and the representative regional monthly and annual precipitation series of the area. Table 1 Information from the historical series of ten pluviometric stations installed over the Urucuia Aquifer System area. Code Name Initials Sub Basin Municipality / State Latitude (°) Longitude (°) Altitude (m) 01145013 Ponte Serafim – Montante PSM 46 Barreiras / BA -11.8961 -45.6119 713 01245014 Fazenda Johá FJH 46 Barreiras / BA -12.1256 -45.8108 725 01245015 Roda Velha RVE 46 São Desidério / BA -12.7653 -45.9439 761 01346006 Fazenda Planalto FPL 45 Correntina / BA -13.7519 -46.14 947 01346007 Fazenda Prainha (Faz. Antas) FPR 21 São Desidério / BA -13.3125 -46.0631 824 01347000 Cavalcante CAL 21 Cavalcante / GO -13.7969 -47.4617 821 01445000 Cajueiro CAJ 45 Januária / MG -14.8361 -45.1733 700 01447000 Alto Paraíso de Goiás APG 20 Alto Paraíso de Goiás / GO -14.1347 -47.5117 1197 01447002 São João D'Aliança AJD 20 São João D'Aliança / GO -14.7072 -47.5236 1009 01546005 Cabeceiras CAB 43 Cabeceiras / GO -15.8008 -46.9247 900 The region in which the UAS is characterized by the rainy period from October to March, as in April initiating the dry period when the rains begin to decrease. In this work, monthly precipitation average data from September 1973 to August 2006 were used. This period of precipitation data was selected with the intention of the time series had only 10% of failures in the daily data and without impacting the monthly and annual total values of the stations. Therefore, no other consistency analyses were performed and there was no a failure filling. Then, a local frequency analysis used in the precipitation series for each station and the regional averages (monthly and yearly) to assess the fulfilment of the criteria of independence, homogeneity, stationarity, and randomness, which are essential to verify whether the results are statistically valid (WMO 2009 ). These criteria will be evaluated by the following non-parametric tests (Naghettini and Pinto 2007 ). Randomness of values suggested by the test of NERC ( 1975 ), which indicates the presence of a structure or intervention of a non-random nature; Independence of values is assessed with the test proposed by Wald and Wolfowitz ( 1943 ), which verifies that no observation can influence the occurrence, or non-occurrence, of the next observation; Homogeneity of the sample is analyzed through the test by Mann and Whitney ( 1947 ), which considers an homogeneous sample when all elements come from one and identical population; and the Stationarity of a series is verified by the Spearman test described by NERC ( 1975 ), which is associated with the non-alteration of statistical characteristics over time, which means that there are no trends, jumps and other properties. Besides the adjustments of the series to the Log-Normal distribution (Naghettini and Pinto 2007 ), which presents good results for annual values averages (Pinto and Alves 2001 ), were evaluated by the Anderson-Darling (AD) adherence test, at the 5% level of significance, calculated by: where P is the probability of X < x, X é random variate (RV), x is a value of RV, σ is the standard deviation of ln(x), µ is the mean of ln(x). The empirical distribution estimate was performed with descending order of the series and the calculation of the plot position using the Weibull formula: $$(\text{P}>\text{p})=\frac{\text{m}}{\text{N}+1}$$ (2) Where: m is the order number and N the sample size. For the annual series, the hydrological year starting in September and ending in August was considered, and the data dates from September 1973 to August 2006, a total of 33 years. With the distribution adjusted, it is possible to determine the return period of an observed rain height. The following correlation estimates the recurrence time or return period (T): The return period is associated with the probability that an event will be equalled or exceeded in any given year. Then, the values of monthly and annual precipitation average representative of the area were correlated with the respective indexes of teleconnections, AMO, PDO, and ENSO, obtained from the NOAA website (NOAA 2019a , b , c ). AMO is an index of natural variability, generated from anomalies in the sea surface temperature of the North Atlantic Ocean, in an interval of 5 to 8 decades (Santos et al. 2016 ), which cause changes in atmospheric circulation, based on three main processes: the atmospheric waves, the continuity of mass and the change of phases of the water (gaseous to liquid or solid). These anomalies are measured using the AMO Index, proposed by Raa et al. ( 2009 ) and (Oldenborgh et al. 2012 ), and makes it possible to evaluate the occurrence of gradients responsible for generating the phenomena called Atlantic dipole, which can be of positive or negative phase (Kayano and Andreoli 2009 ). The AMO Index monthly series is calculated from 1856 to 2020 (NOAA 2019b ). Similar to AMO, the PDO teleconnection consists of a climatic phenomenon observed in the North Pacific Ocean, where a Sea Surface Temperature (SST) pattern is identified for 20 to 30 years, and which seems to coincide with the cooling periods (negative values) and warming (positive values) of the waters. It results in changes in marine ecosystems, in addition to affecting the currents' trajectories (Ndehedehe and Ferreira 2020 ) as it affects the temperatures of the coastal sea and the continental surface air from Alaska to California. This variability is verified by the Decadal Oscillation Index, which is a function of the monthly anomalies of the sea surface temperature over the North Pacific (about 20°N) after removing the global average temperature of the sea surface (Molion 2005 ; Buffon and Binda 2014 ). The PDO Index monthly series was considered in this study from 1854 to 2020 (NOAA 2019c ). ENSO is a periodic variation in the tropical Pacific Ocean of winds and sea surface temperature, which occurs in the warming phase and accompanies the high pressure of the air surface (Walker cell changes), affecting the climate of a large part of the tropics and subtropics. ENSO can be assessed by the Southern Oscillation Index, which is a standardized index based on the differences in sea level pressure observed between Tahiti and Darwin in Australia, and the prolonged periods of negative oscillation values coincide with ocean waters abnormally warm throughout the eastern tropical Pacific, typical of El Niño episodes. The South Oscillation Index (SOI) series obtained was monthly from 1951 to 2020 (NOAA 2019a ). It was also used to characterize the intensity of an ENSO event, the multivariate index, which consists of a complex interaction of a variety of climatic systems, and which is considered the most comprehensive index for monitoring El Niño, as it combines the analysis of multiple meteorological and oceanographic components (Mazzarella et al. 2013 ). The series of the Multivariate Index (MI) obtained was monthly from 1979 to 2020 (NOAA 2019a ). Therefore, due to the data available at NOAA for each teleconnection, the period from September/1963 to August/2006 was used to relate the AMO, PDO, SOI (for ENSO) with the historical precipitation series. But to relate with MEI (for ENSO) was used the serie from September/1979 to August/2006. The climate indices were correlated with monthly and annual precipitation data form stations located in the UAS, in pairs, and for periods defined based on the phases of each index. The correlation was evaluated by analyzing the significance of the differences and using the Student's t-test, and the results were considered statistically significant with a p-value ≤ 0.05. This is because the p-value depends directly on the data series and presents a quantitative measure that assists the decision-making process as evidence that if the null hypothesis (H0) is true, then the chance of random variation will be the only explanation for sample differences. Therefore, a reasonable interpretation of the p-value (Arsham 1988 ) shows that there is robust evidence against H0 if P < 0.01; if 0.01 ≤ P < 0.05 there is moderate evidence against H0; if 0.05 ≤ P < 0.1 there is suggestive evidence against H0; but if P ≥ 0.10 there is little or no real evidence against H0. After analysing the correlation of the monthly and annual precipitation data with the values of the climate indices, the series of maximum daily rainfall over the Urucuia Aquifer System was determined, and the 15 largest events were chosen to analyse the retroactive trajectories of the air masses, and so try to estimate which atmospheric systems are operating and their origin. The retroactive trajectories of the air masses were estimated by the Lagrangian approach, which determines the water balance (Evaporation – Precipitation), for a given period of time, from the sum of the humidity changes (Sodemann; Schwierz; Wernli, 2008), that is: $$\frac{\text{D}\text{q}}{\text{D}\text{t}}\approx \frac{\varDelta \text{q}}{\varDelta \text{t}}=\text{E}-\text{P}$$ (4) Knowing that the moisture content is interpolated to its position every time (Stohl and James, 2004), water vapour gains and losses are measured by changes in specific humidity (Δq) over time (Δt). The rates of increase (x) and reduction (y) of specific humidity occur with the gains and losses of water vapour along the particle's trajectory (Δq = x-y). So, for a large number of particles (K) passing over an area (A), the equation of (4) can be written as follows: $$\text{E}-\text{P}=\frac{{\sum }_{\text{k}=1}^{\text{K}}(\text{x}-\text{y})}{\text{A}}$$ (5) This Lagrangian modelling for trajectory determination was performed using the HySplit model (Stein et al. 2015 ), which uses the data set of the reanalysis project carried out by NOAA to produce new atmospheric analyzes using historical data (from 1948) and also produce analyzes of the current atmospheric state (Climate Data Assimilation System). The model estimates (at 1-degree resolution) and reports the position of the air packages (latitude, longitude, and height), pressure, and specific humidity in grams of water vapour per kilogram of air (g/kg), thus forming the trajectories of the air masses (Santos and Lima 2019 ). The trajectories were calculated for the days that precipitation occurred above 125 mm in the Urucuia aquifer region, a criterion of rainfall greater than the 3rd quartile of the data series, which corresponds to a limit commonly used in similar studies (Kim et al. 2004 ; Amato and Hopke 2012 ; Alves et al. 2018 ). The trajectories were started at heights from 10 to 6010 m, at intervals of 400 m, above ground level, and at the times of 12 h, 18 h of the previous day and 0 h and 06 h of the day of measurement, because all the measurements take place at 07 h and refer to the accumulated rainfall of the last 24 hours. The heights were defined based on previously performed works (Santos et al. 2018 ). The trajectories were simulated retroactively in time, that is, from the specification of the destination coordinates (sampling point) the trajectories are plotted from the possible points of origin, depending on the climatic conditions, in a specified time interval (1-hour interval for 10 days or 240 hours), to the destination (Back Trajectory). The trajectory data show, for each storm, the path that the particle travelled defined considering time, latitude, longitude, and heights of the trajectory, informing the respective pressure and specific humidity of the particle, which allows a better analysis of the data. In other words, the HySplit model informs the rainfall trajectories according to the time, day and location, so it was necessary to evaluate the daily precipitation at the stations to detect the highest daily rainfall and where it occurred. It analyzed 22,400 trajectories (350 dates * 16 times * 4 times). However, only 15 daily rainfall records were higher than 122 mm, which was equivalent to 10% of the region's average annual rainfall in a single day. Therefore, it analyzed with more detail to understand if their occurrence derives from the presence of any specific atmospheric system. Results Characterization of precipitation variability in the Urucuia Aquifer System region The region in which the UAS is inserted is characterized by the rainy period from October to April, but in April, the rains begin to decrease, initiating the dry period, as can be seen in the (Figure 4 ), where monthly precipitation average data from September 1973 to August 2006 were used, in 94 pluviometric stations located on and near the area. The wettest quarter is December to February with a quarterly average of 619 mm, and the driest is from June to August with a quarterly average of 10 mm. The precipitation in the region begins in September, but the rainiest period occurs between October to April. The greatest precipitated volumes occur from west to east. The largest rainfall was observed in Ponte Alta do Bom Jesus station, located in the Tocantins River's Hydrographic Region. Over Urucuia Aquifer System, the average annual precipitation is equal to 1220.8mm, defined between 1973 and 2006. This temporal variability of precipitation was analyzed by the spatialization of precipitation information from the National Hydrometeorological Network stations, from September 1973 to August 2006. This spatialization resulted in a surface or "raster", where monthly rainfall estimates are made for all cells to generate a continuous map. In this work, the Kriging method was chosen, which according to Murara ( 2019 ), is the most used method for spatialization of climatic data since it creates more uniform variations, not presenting variations and smoothing its representation since it homogenizes the information presented. After using the Rainfall Anomaly Index (RAI) methodology, proposed by (Rooy 1965 ), which characterizes the existence of dry periods (precipitation below the historical average) and rainy periods (annual precipitation above average), in addition to the intensity of the phenomenon in the period (Kraus 1977 ; Souza et al. 1980 ; Freitas 1998 ; Araújo et al. 2009 ; Cruz et al. 2013 ; Moraes 2014 ; Alves and Araújo 2015 ; Dutta et al. 2015 ; Noronha et al. 2016 ; Hänsel et al. 2016 ; Cerqueira et al. 2018 ; Lima et al. 2019 ; Surendran et al. 2019 ), the analysis showed 12 rainy years and 21 dry years in the region, considering the hydrological year beginning in September and ending in August of the following year, since 98.6% of the rain that occurred in the region above 2 mm, occurred between September to May of the following year and, besides, evaluating the precipitated volume throughout the period only 0.95% fell on UAS, between May and August. The isohyets map of annual rainfall average in the region (Figure 4 ) assists in the evaluation of the behavior of annual rainfall average, showing that the rainfall in the western part of the UAS is higher, especially in the area of the “Ponte Alta do Bom Jesus” station, which presents a higher value of average annual precipitation compared to the other values of the analyzed rainfall stations. In contrast, the eastern part presents precipitation with lower annual averages, reaching values below 1000 mm in the vicinity of the area in which the aquifer is located. This spatial variation in rainfall is attributed to the orographic control of rainfall in which air masses are discharged progressively from west to east. It presents a niche of the greatest rainfall occurring around Ponte Alta do Bom Jesus Station, which reaches an average annual rainfall of 2,033 mm, much higher than the region's average of 1,200 mm. Considering as a dry year, the years with annual precipitation below the historical average, and the years with annual precipitation above, such as rainy years, the variability of the spatialization of the precipitated volume in the dry and rainy years was analyzed, and the result showed that the spatialization of the rains demonstrated no difference between the dry or rainy years (Figure 5 ) in the region. Observe the comparison of the average annual rain's spatialization between dry and rainy years. In reference to the monthly historical average, dry years were lower monthly rainfall, and in rainy years observed higher monthly rainfall. Not observed differences between the occurrences of rains regardless of the dry or rainy year. Moreover, from the evaluation of the precipitation spatialization in all months of the year, observing the existence of variability between the rainy and dry months, it is concluded that in the driest period of the year, between May and September, there is no variation in the spatialization of rain in the region, that is, it is uniform in the investigated area. However, in the wettest period of the year, between October and April, it is observed that the western region presents a greater volume of precipitation, especially in the region around the municipality of Ponte Alta do Bom Jesus (Figure 6 ). However, concerning the precipitation of the “Ponte Alta do Bom Jesus” station (code: 01246000), where the concentration of precipitation occurs (Figures 4 , 5 and 6 ), it was observed that the behaviour of precipitation is different, with different mean and variance, such as show the daily hyetograms that compare the rainfall on the UAS and with that observed at the “Ponte Alta do Bom Jesus” station – code: 01246000. In Figure 7 , note through the daily hyetograms that precipitation over the Urucuia aquifer system is different from the frequency observed on the station, which is installed around the UAS region, not on it. Also, analyzing the series statistically by the t-test, assuming different variances, the results show that the data did not present correlation (p-value >>>> 0.05). Geographically, the “Ponte Alta do Bom Jesus” station is not located on the UAS. However, on the slope area, which circulates the entire plateau, it was understood that the meteorological systems phenomena, which may influence and/or generate precipitation at this point, are not the same as those acting in the rains over the aquifer. Therefore, the precipitation in this place must be analyzed separately. Another issue is that, depending on the location of this station, the rains that occur in this location should not contribute to the recharge of the Regional Free Aquifer, which occupies the largest area in the basin, since the variable static level of this subsystem, varies from 4 to about 50 meters. The “Ponte Alta do Bom Jesus” station is located at an altitude of 518 meters, while the height of the plateau closest to this location is at an altitude of 903 meters, another at an elevation of 385 meters. Therefore, if the rain in this place contributes to the UAS recharge, it will only be for the free Deep Aquifer subsystem's recharge, which can reach depths greater than 100 m. Annual and monthly rainfall averages over the UAS area The monthly precipitation series over the Urucuia aquifer show that the differences between the means and the variances are not significant, according to the tests for samples assuming equivalent means (z test) and equivalent variances (t-test), with the true null hypothesis (H0) claims that random variation will be the only explanation for sample differences (p-value <0.05). But, when we compare the monthly flows series of one station with the other stations series, the results found were not statistically significant (p-value ≥ 0.05), because the lowest p-value found was equal to 0.057. The stations' series of monthly data were correlated in pairs through the coefficient of determination (R²) to adjust a generalized linear statistical model. Thus, it was found 45 R² values for correlations with monthly data, with the lowest value being 0.6564 and the maximum 0.8336, for an average R² of 0.7455, which allows to conclude that there is a good correlation between the series of monthly precipitation of the stations located on the UAS in the period of 33 years (1973 to 2006), which represents the main source of recharge for this aquifer. Therefore, the station series of monthly precipitation data showed equivalent averages and variances, in addition to a good correlation, when correlated with the regional monthly precipitation series, which the lowest R² found was 0.8352, indicating a good correlation. Evaluated by the series formed by each month, the monthly data were divided into twelve series for each station. The series was assembled month by month. That is 120 series analysed. Nine series did not approve: three in independence teste (July series from Ponte Serafim – Montante station, July series from Fazenda Johá station and June from Fazenda Prainha station); two in randomness test (May and June series from Cabeceiras station); and four in stationarity test (June series of Ponte Serafim – Montante station, and October series of Cavalcante, Alto Paraíso de Goiás, and São João d'Aliança stations). The homogeneity test as not evaluated due to the small size of the series. However, even passing the criteria of independence, randomness, and stationarity, all series from May to September were not accepted by the Anderson-Darling (AD) adherence test, at the 5% significance level, to be modelled due to the Log-Normal distribution, this occurs precisely due to the occurrence of the dry season in the region, where rainfall is not frequent. In May and September, which are the months of the beginning of the descent and rise of precipitated volumes, it was possible to adjust Gumbel extremes' distribution, being accepted by the Anderson-Darling (AD) adhesion test, to the level of 5% of significance. For the series of November, December, January, and February, the null hypothesis was accepted by the AD test for 5% of significance, showing that the Log-Normal distribution can model the series of these months. Also, analyzing the series of mean data on the UAS, month by month, it was observed that the Log-Normal distribution could also model the series for December, January, February, March, and April since the null hypothesis was accepted by the AD adherence, at the 5% level. The data of the descriptive statistics of the monthly precipitation series are shown in Figure 8 . The higher monthly rainfall totals range from 524 mm to 780 mm. However, most of the observed precipitations are less than 250 mm. The total annual precipitation data series of each station on the UAS, in addition to the series of regional annual total precipitation, over the period of 33 years, were also evaluated. It is concluded that the series are independent and stationary, except for the “Fazenda Planalto” station (code: 01346006), which presented dependent values and presented non-stationary values on “São João d'Aliança” station series (code: 01447002) and regional annual totals series (Figure 9 and Table 2 ). Table 2 Descriptive statistics of the ten series of annual rainfall data for stations installed over the Urucuia Aquifer System area and the series of regional annual total precipitation. Code Station Name Station Initials Test statistics Test result Independence Stationary Independence Stationary 01145013 PONTE SERAFIM - MONTANTE PSM 1.09 -1.56 Independent Observations Stationary Observations 01245014 FAZENDA JOHÁ FJH -1.81 0.09 Independent Observations Stationary Observations 01245015 RODA VELHA RVE -1.51 -1.22 Independent Observations Stationary Observations 01346006 FAZENDA PLANALTO FPL -2.04 -0.05 Dependent Observations Stationary Observations 01346007 FAZENDA PRAINHA (FAZ.ANTAS) FPR 0.80 -0.90 Independent Observations Stationary Observations 01347000 CAVALCANTE CAL 0.91 -1.39 Independent Observations Stationary Observations 01445000 CAJUEIRO CAJ -0.71 -0.69 Independent Observations Stationary Observations 01447000 ALTO PARAÍSO DE GOIÁS APG 1.39 -0.81 Independent Observations Stationary Observations 01447002 SÃO JOÃO D'ALIANÇA SJA -0.33 -2.79 Independent Observations No Stationary Observations 01546005 CABECEIRAS CAB 0.56 -0.59 Independent Observations Stationary Observations ANNUAL MEAN REGIONAL SERIES 1.46 -2.61 Independent Observations No Stationary Observations Nevertheless, all the annual totals indicated a linear downward trend, represented by the hyetogram of the series of regional annual total precipitation (Figure 10 ). The Mann-Kendall test was applied to define whether there is a statistically significant time trend, where the p-value of the test less than the level of significance (0.10, 0.05 and 0.01), indicates statistically significant evidence to reject the null hypothesis H0, which indicates an increasing trend in the data (positive S) or decreasing (negative S) (WMO 2000 ; Chiew and Siriwardena 2005 ). And results indicated that there is and that a large part of the differences between the values in the time series was negative, pointing to a downward trend over time (S = -172 and p-value = 0.004). So, the series of annual totals indicated a linear downward trend, represented by the hyetogram of the series of regional annual total precipitation. This downward trend was also observed in the precipitation series between 1980 and 2015 (Costa et al. 2019 ). From the total annual rainfall series evaluated by the Anderson-Darling (AD) adherence test, at the 5% level of significance, eight accepted to modelled by the log-normal distribution(Naghettini and Pinto 2007 ), with the parameters of position (β) and form (α) calculated by the L-moment method (Hosking and Wallis 1997 ). This information makes it possible to calculate the quartiles associated with different return times or calculate the return time for each annual quartile, as shown in Table 3 . Table 3 Local frequency analysis results of the annual rainfall series from the stations installed over the Urucuia Aquifer System area and of the regional annual total precipitation series. Code Station Initials Parameters of the log-normal distribution model Annual Precipitation for Return Time Equal 100 Years Average Annual Maximum Annual Minimum Annual Location Scale ξ α (Tr = 100 years) P (mm) Tr (years) P (mm) Tr (years) P (mm) Tr (years) 01145013 PSM 7.0560 0.2205 1937.4 1186.7 2 1732 29 762.4 1 01245014 FJH 7.1140 0.1805 1870.3 1247.7 2 1781 51 824.3 1 01245015 RVE 7.1102 0.2465 2172.5 1258.2 2 1856 22 660.8 1 01346007 FPR 7.1656 0.1719 1930.6 1311.4 2 1659 13 907.5 1 01347000 CAL 7.4922 0.1642 2628.3 1817.4 2 2679 137 1311.6 1 01445000 CAJ 7.0428 0.2188 1904.2 1170.5 2 1810 55 765.7 1 01447000 APG 7.2830 0.2040 2339.4 1485.4 2 2431 168 964.3 1 01546005 CAB 7.2315 0.2261 2339.0 1415.6 2 2337 99 921.1 1 Failure in the independence and stationarity tests implies the non-acceptance to model by the log-normal distribution; therefore, “Fazenda Planalto” and “São João d’Aliança” stations do not appear in Table 3 . The statistical tests showed that the annual precipitation series is independent and stationary. Nevertheless, when dividing the series half, with 1989/1990 hydrological year of as the series center, the analysis shows that up to 1989/1990, 73% of the rainfall had a return time of up to 5 years and 27% greater than six years. After this hydrological year, 92% of the rainfall had a return time of up to five years and only 8% greater than six years. That represents an 18% decrease. Analyzing also the average of all annual rainfall, it is observed that there is a significant difference between the period up to the hydrological year 1989/1990 and the subsequent period, which leads to the assumption that the existence of external climatic phenomena may influence some of the precipitation characteristics, mainly volume and intensity, such as, for example, meteorological systems and teleconnections, which would imply considerable changes in water availability, even so, the issue of aquifer recharge. Comparison of total regional precipitations (annual and monthly) with Climate Indexes Analyzing the North Atlantic SST anomaly time series, Enfield et al. ( 2001 ) found that the positive (warm) AMO phase occurred during the periods 1860-1885 and 1925-1965 and the negative (cold) AMO phase, during the periods 1895-1924 and 1970-1990. And the downward trend, shown in the hyetogram in Figure 10 , shows that the greatest amounts of rainfall occurred between 1973 and 1990, a period in which the negative (cold) phase of the AMO occurred, according to other studies (Raa et al. 2009 ; Oldenborgh et al. 2012 ), and for this reason, this was the first teleconnection, whose index was compared with regional precipitation data. However, analysis of the AMO index series showed that the negative AMO phase occurred until 1993 and, only in 1994, a positive AMO phase started, a fact confirmed in other studies too (Valdés-Pineda et al. 2018 ). Thus, and considering that the series are independent and stationary, the analyzes used the complete series to compare the indexes without links to the hydrological year of 1989/1990. The analysis compared the precipitation annual with the average AMO index by means of correlation analysis, and the results showed statistical significance (p-value ≤ 0.03), revealing the potential teleconectivity of the climatic circulation patterns with average annual precipitation over the UAS. Correlating also the monthly precipitation average values in the region with the AMO indices values (Figure 11 ), a p-value less than 0.001 is obtained for the regional data series and less than 0.025 for the series of monthly data of the stations, proving a strong correlation between this teleconnection and precipitation in the region. Therefore, these results suggest a correlation between AMO and total annual and monthly rainfall in the region where the Urucuia aquifer occurs. Searching for more evidence of this correlation, the series of monthly rainfall that occurred when the AMO index was positive was compared with the series of rainfall that occurred when this index was negative, using the t-test at the 0.05 level of significance. In this case, it is concluded that there is a significant difference between the averages of precipitation that occur when the oscillation index is positive or negative (p-value < 0.008). Based on this evidence, the boxplot graph was proposed (Figure 12 ), where the differences between the means (123.3 and 94.9 mm), the 3rd Quartiles (207.5 and 166.9 mm), and the 1st Quartiles (15.3 and 2.7 mm) can be observed, that suggest interference of AMO in the negative and positive phase of the dipole. The medians (99.5 and 51.9 mm) also show this difference. This means that there is, in the positive phase of the dipole, inhibition of cloud formation, decreasing precipitation, and causing dry periods. This is because the North Tropical Atlantic waters become warmer, and the Equatorial Atlantic and Tropical South waters become colder. In contrast, in the negative phase of the dipole, the North Tropical Atlantic waters become colder and the waters of the South Tropical Atlantic warmer, generating an increase in upward movements over these regions, intensifying the formation of clouds and increasing rainfall. Regarding the PDO phenomenon, the mean precipitation series over the UAS did not show any correlation (Figure 13 ), as the results were not statistically significant, neither with the annual data, where p-values greater than 0.7 were observed nor with the data monthly, whose lowest p-value was greater than 0.1, since p-values ≥ 0.10 indicate little or no real evidence of correlation (Arsham 1988 ). Still looking for some correlation, the series analysis was performed according to the hot or cold phase of the PDO. As several authors (Mantua et al. 1997 ; Molion 2005 ; Kayano and Andreoli 2009 ; Alves 2012 ) stated that the cold phase of the PDO occurred between 1947 and 1976 and the warm phase between 1977 and 1998, it was correlated monthly and annual rainfall with the PDO index for the period between 1947 and 1976, then for the period between 1977 and 1998, but no correlation was also obtained, resulting in a p-value ≥ 0.8. The regional monthly and annual precipitation series also did not correlate with the Southern Oscillation Index, as the results were also not statistically significant, with p-value ≥ 0.5 for monthly data and p-value ≥ 0.3 for the annual data (Figure 14 ). The Multivariate ENSO Index application also did not show any correlation since the p-values were greater than 0.7. That is, they were not statistically significant. This is a noteworthy point since no correlation was detected that indicated ENSO and PDOs influence on the precipitation of the studied area. However, some of the studies mentioned above indicate that the negative phase of these indices is favour precipitation over the Northeast of Brazil because there is an increase in rainfall intensity along the rainy season. In their positive phase, the PDOs and ENSO there seems to occur a greater number of El Niño episodes and more intensely, and a lower number of La Niña events, which occur less intensely. The opposite occurs in the negative phase, where there is a higher occurrence of La Niña episodes, which tend to be more intense, and a lower frequency of El Niños, which tend to be short and fast. Comparison of the UAS total, annual, and monthly rainfall patterns with meteorological systems Some of the studies previously mentioned have shown that the ENSO and PDO teleconnections also greatly influence meteorological systems, as they affect the longitudinal positioning of the ascending branches of the Walker Cell (Wang 2004 ). This is because, in the hot phase, its main ascending branch is positioned over the warm waters of the Pacific and causes subsidence and high pressure over the North of South America, blocking the Intertropical Convergence Zone further north and the cold Fronts of the Southern Hemisphere and the South Atlantic Convergence Zone (ITCZ) further south, which causes severe droughts in the region. In the cold phase, there is an intensification of the Walker Cell's ascending branches on the continents, increasing the rainfall totals. Therefore, if it was not possible to detect an influence between the ENSO and the PDOs with the precipitation series, it came up that it may be possible to define a relationship between the series and the meteorological systems that act on the UAS, which was investigated through simulation based on the HySplit model. However, it is important to note that the HySplit model informs rain trajectories according to time, day, and location, so it was necessary to evaluate the daily rainfall in the stations to detect the greatest daily rainfall and where it occurred. In the assessment of the daily rain series, 12,053 days were analysed in 10 stations and found 24% of failures in the data series and 56.3% of the days have no rain occurrences (precipitation equal to zero). For the other 19.7% of the data, on 54.7% of them precipitation were less than 10 mm, on 42.7% were greater than 10 mm and less than 60 mm and only 2.6% were greater than 60 mm. In other words, only 618 rain records greater than 60 mm verified on the UAS. In the period analysed from September 1973 to August 2006, there are only 49 rain daily records greater than 100 mm, equivalent to only 0.2% of observation records in the region (Figure 15 ). To define from which precipitation value the daily data would be analyzed by the HySplit model, the series of maximum annual precipitation of the stations was evaluated, and it was found that for the recurrence time of 1 year, the rain would be 74 mm. Thus, all precipitations equal to or greater than 70 mm were analysed. That is, 22,400 trajectories were analyzed (350 dates * 16 times * 4 times). With this assessment, it was possible to observe that the specific humidity of the air and the pressure decrease exponentially with the altitude, but in the period from 1973 to 1990 (negative AMO) the specific humidity was 26% higher than in the period from 1990 to 2006 (positive AMO), which shows a greater amount of water vapor in the atmosphere in the period of negative AMO concerning the period of positive AMO. The trajectory data showed no variation between the values of specific air humidity and atmospheric pressure with the increase or decrease in the total precipitation, as the results were not statistically significant, with a p-value ≥ 0.5. On average, the trajectories along the negative AMO phase were at higher latitudes closer to the ITCZ, showing that this atmospheric system may have an influence on precipitation during the negative phase of the AMO. However, in the phase of positive AMO, on average, the trajectories latitudes were lower, closer to the Tropic of Capricorn, suggesting that the rainfall received little influence from ITCZ in the phase of positive AMO, and perhaps more influence from the Atlantic Subtropical Anticyclone Sul (ASAS), which transport heat and humidity from the Atlantic Ocean to the interior of Brazil. In the positive AMO phase, the trajectories presented anticlockwise snail aspects around the initial coordinates, mainly at higher altitudes, suggesting the presence of anticyclonic vortexes, which are high-pressure regions formed by a descending dry air mass, which due to its weight, force to descend. This dry air causes low relative humidity levels and prevents the formation of clouds and precipitation (Kousky and Alonso Gan 1981 ; Jury and Engert 1999 ). This type of trajectory suggests the influence of ASAS in its formation. Figure 15 shows the sets of trajectories estimated for the 15 largest daily precipitation events in the area over the UAS. It is important to highlight that the color scale used is associated with each moment of the trajectories' specific moisture content. A more bluish coloration represents the trajectories that present the highest moisture content and are generally below 2000 m of altitude, corroborating with (Wallace and Hobbs 2006 ). The opposite occurs with the reddest trajectories, which have less moisture content and are generally at higher atmospheres levels. Thais is, the color scale used is associated with the specific moisture content of each trajectories moment. A more bluish coloration represents the highest moisture content trajectories and is generally below 2000 m of altitude. The opposite occurs with the reddest trajectories, which have less moisture content and are generally at higher atmospheres levels. Moreover, it is possible to observe different types of storms generated by different atmospheric systems, but all coming from the South Tropical Atlantic Ocean. About the systems, observed indication a direct influence from the South Atlantic Convergence Zone, the predominance of the Amazon systems with a strong contribution from systems in the Equatorial Atlantic Ocean, the performance of High-Level Cyclonic Vortexes, and the performance of cold fronts from the Southern or Indian Ocean. Also, in Figure 15 , it is possible to observe different storms generated by different atmospheric systems. However, all coming from the South Tropical Atlantic Ocean, corroborating the hypothesis that AMO, in its cold (negative) phase, influences precipitation generated in the region. Concerning the systems, it can be seen that the period from 1977 and 1988 had a direct influence from the South Atlantic Convergence Zone, but in 1978 one can assume the predominance of systems from the Amazon with a strong contribution from systems in the Equatorial Atlantic Ocean. In 1982, it suggested the performance of High-Level Cyclonic Vortexes. In 1976, there is an indication of cold fronts' performance from the Southern or Indian Ocean. Therefore, it was not possible to correlate the high daily rainfall with a predominant atmospheric system. As the rain pattern distribution shows a decreasing trend in the amount of precipitation in the last 45 years in the Urucuia Aquifer area it is expected a proportional reduction in groundwater recharge. This reduction is more important to the confined aquifer subsystem, but also may also affect the unconfined subsystems and must be considered when developing the management actions of the regional water resources. Conclusions In this work, ten series of total annual rainfall evaluated by the Anderson-Darling (AD) adherence test, at the 5% level of significance, but eight were accepted to be modeled by the log-normal distribution, with the position parameters (β) and shape (α) calculated by the L-moments method, namely: Ponte Serafim Montante (01145013), Fazenda Johá (01245014), Roda Velha (01245015), Fazenda Prainha (01346007), Cavalcante (01347000), Cajueiro (01445000), Alto Paraíso de Goiás (01447000) and Cabeceiras (01546005). It showed that rainfall values are independent and stationary, with no indication of abrupt changes to suggest interference of climate change. However, analyzing the average of all annual rainfall, it is observed that there is a significant difference between the series’ first 16 years and the series’ last 16 years, which leads to considering that external climatic phenomena may influence mainly the volume and intensity of the precipitation. And it was possible to obseve that the total annual rainfall da series over the UAS indicates a downward linear trend, confirmed by the Mann-Kendall test (p-value = 0.004), which justify a decrease in the UAS recharge and, consequently, a decrease in the base flow from the aquifer. This decrease in annual precipitation can be explained by the AMO phase change, because the correlation of the monthly and annual rainfall with the AMO index, were statistically significant, revealing the potential teleconnection of the sea surface temperature of the North Atlantic Ocean with precipitation over the Urucuia Aquifer System. Furthermore, the comparison between the monthly rainfall series that occurred when the AMO index was positive with the series of rainfall that occurred when this index was negative showed a significant difference between the average precipitation in the dipole's negative and positive phases. Therefore, in the positive phase of the dipole, there is the inhibition of cloud formation, a decrease in precipitation, and, consequently, causing dry periods because the waters of the North Tropical Atlantic become warmer and the waters of the Equatorial Atlantic and Tropical South become colder. On the other hand, in the negative phase of the dipole, the North Tropical Atlantic waters become colder and the South Tropical Atlantic warmer waters, generating an increase in upward movements over these regions, intensifying the formation of clouds and increasing rainfall. No correlation was found between precipitation over UAS and climate indices: Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), El Niño Multivariate Southern Oscillation Index (ENSO). However, the correlation between precipitation and PDO and ENSO teleconnections may not have occurred due to the indexes chosen to represent these phenomena, as there are several indexes that can be used in the analysis. Therefore, we indicate a more detailed exploratory analysis to conclude if the phenomena that occur in the Equatorial Pacific Ocean influence precipitation over the Urucuia Aquifer System. Regarding the evaluation of the precipitation trajectories, it was observed that the specific humidity of the air and the pressure decrease exponentially with the altitude, but that the specific humidity in the period from 1973 to 1990 (negative AMO) was 26% higher than the specific humidity in the period of 1990 to 2006 (positive AMO), which shows a greater amount of water vapor in the atmosphere during the negative AMO period compared to the positive AMO period. However, the trajectory data show no variation between the values of specific air humidity and atmospheric pressure with the increase or decrease in the total precipitation, as the results were not statistically significant. On average, the trajectories along the negative AMO phase, on average, occurred at greater latitudes and closer to the Intertropical Convergence Zone (ITCZ), showing that this atmospheric system can influence the precipitation during the negative phase of the AMO. In contrast, in the positive AMO phase, on average, the latitudes of the trajectories were lower and closer to the Capricorn Tropic, suggesting that the rainfall received little influence from ITCZ in the phase of positive AMO, and perhaps more influence from the South Atlantic Subtropical Anticyclone (SASA). In the phase of positive AMO, it was observed that the trajectories had snail aspects around the initial coordinates, mainly at higher altitudes, which may suggest the presence of high-level cyclonic vortex, as these are closed cyclonic circulations in the upper troposphere as shown by the atmospheric waves, whose intensity is higher in the upper troposphere and decreases towards the surface (Reboita et al. 2010 ). It was also observed that different atmospheric systems generate the storms, but all coming from the South Tropical Atlantic Ocean, corroborating the hypothesis that AMO influences the precipitation in the region. And, concerning the systems, it is possible to identify a possible performance of the South Atlantic Convergence Zone, of systems coming from the Amazon with a strong contribution from equatorial Atlantic Ocean systems, High-Level Cyclonic Vortexes, and cold fronts coming from the Antarctic or the Indian Ocean. But, it was not possible to correlate the high daily rainfall with a predominant atmospheric system. Therefore, we conclude that as the precipitation pattern shows a decrease trend in the last decades it is expected a reduction in the aquifer recharge and in the water availability in the near future. Declarations ACKNOWLEDGMENTS The authors would like to thank the Geological Service of Brazil (SGB/CPRM) for the financial and technical support for this research. References Alves AS, Araújo LE de (2015) Avaliação climática da precipitação da bacia hidrográfica do rio Camaratuba – PB. 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Clim Dyn 49:3123–3139. https://doi.org/10.1007/s00382-016-3500-1 Supplementary Files AnalyseSeriesVieiraetal20oct2020.xlsx FiguresArticleVieiraetal09aug2020.rar INDEXVieiraetal20oct2020.xlsx PonteAltaStationAnalysisVieiraetal20oct2020.xlsx StatisticsResultVieiraetal20oct2020.xlsx Cite Share Download PDF Status: Under Revision Version 1 posted Reviews received at journal 12 Nov, 2021 Reviewers invited by journal 12 Nov, 2021 Editor assigned by journal 12 Nov, 2021 First submitted to journal 11 Aug, 2021 Editorial decision: Major Revision 06 May, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-806881","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":63120836,"identity":"6def42a9-c9e3-4a51-8ff2-e54986c52b6b","order_by":0,"name":"Myrla de Souza Batista Vieira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYHACxgNAgoeBGUh+QIgewKsHroVxBilawICZhxgtujNyHxz4wXBYhp+d9+Fnm5o7+boN7Bcf8zDcycelxexGusHBHobDPJLN7MbSOceeWW47wFNszMPwzLIBp5Y0hgM8DGk8BofZGKRzGw4bmB3gSZOcwXDYALctaQwH/wC12B9mY/5tSayWwzwMNjwGzGxs0oxgLezHJD7g03LmGdDvBjY8EofZ2Cx7jj0zMDvMw2zwweAZbi3H0xgfvqmQsOfvP8Z840fNHQOz4+0PHyRU3MGphUEgAUggpA+AYscAWQQT8B9A4YJ47A/wqB8Fo2AUjIIRCADWO1T8+Rn3MgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6822-4647","institution":"Serviço Geológico do Brasil: CPRM","correspondingAuthor":true,"prefix":"","firstName":"Myrla","middleName":"de Souza Batista","lastName":"Vieira","suffix":""},{"id":63120837,"identity":"bd88ac90-88b3-4da1-af06-9409a2f8c183","order_by":1,"name":"José Eloi Gimarães Campos","email":"","orcid":"","institution":"UnB: Universidade de Brasilia","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Eloi Gimarães","lastName":"Campos","suffix":""},{"id":63120838,"identity":"8fa61064-71a0-45af-a0d2-ae22f78bd3c7","order_by":2,"name":"Eber José de Andrade Pinto","email":"","orcid":"","institution":"Serviço Geológico do Brasil: CPRM","correspondingAuthor":false,"prefix":"","firstName":"Eber","middleName":"José de Andrade","lastName":"Pinto","suffix":""},{"id":63120839,"identity":"107526e8-631f-4cc6-88a0-b417fbf5745a","order_by":3,"name":"Marcus Suassuna Santos","email":"","orcid":"","institution":"Serviço Geológico do Brasil: CPRM","correspondingAuthor":false,"prefix":"","firstName":"Marcus","middleName":"Suassuna","lastName":"Santos","suffix":""}],"badges":[],"createdAt":"2021-08-13 00:37:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-806881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-806881/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":15555561,"identity":"486b4528-414a-46f0-b851-8538d4233157","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":759376,"visible":true,"origin":"","legend":"Area for identifying teleconnection standards (Adapted from https://meteorologia.unifei.edu.br/teleconexoes/). (Souza and Reboita 2021)","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/65cb039fb166087d89aaa974.png"},{"id":15555556,"identity":"d9f292a5-01ce-4132-baeb-0af86199bd44","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":682322,"visible":true,"origin":"","legend":"(a) Location of the Urucuia Aquifer System in central Brazil. (b) Digital Elevation Model of the Urucuia Aquifer System domain. (c) Altimetry of the Western Chapadão of Bahia geomorphological domain in different sections.","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/ba31e9ba343a31b0b6130c0f.png"},{"id":15555558,"identity":"700e5cb4-8a17-4a32-96da-b27e29474567","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":179062,"visible":true,"origin":"","legend":"Classification of droughts in the Urucuia Aquifer System region (ANA 2018).","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/500ba54a41bc964711c936d2.png"},{"id":15555555,"identity":"2259d859-86de-413f-bae7-9e6693822485","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":459482,"visible":true,"origin":"","legend":"Analysis of Annual Precipitations Average in the Urucuia Aquifer System region (1973 to 2006).","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/5b1e61eae1b5e9d2cd3a68c3.png"},{"id":15556690,"identity":"f6d2bdca-5896-47a3-a4fb-54bd2189ff69","added_by":"auto","created_at":"2021-11-15 16:52:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":736630,"visible":true,"origin":"","legend":"Analysis of Annual Precipitations Average in the Urucuia Aquifer System region.","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/5fd522db736de1db8e6ba51b.png"},{"id":15556691,"identity":"a7df1588-5912-40b4-b921-9bfdf716be71","added_by":"auto","created_at":"2021-11-15 16:52:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":622928,"visible":true,"origin":"","legend":"Analysis of Monthly Precipitations Average in the Urucuia Aquifer System region. ","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/6b3de17585fb99233e0d9223.png"},{"id":15556041,"identity":"22e2d097-ce5c-4766-8acf-eb234192dc61","added_by":"auto","created_at":"2021-11-15 16:46:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":82262,"visible":true,"origin":"","legend":"Daily hyetograms comparing the precipitation on the Urucuia aquifer System and that observed at “Ponte Alta do Bom Jesus” station (code: 01246000).","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/b406d9d0f4424b725aaac768.png"},{"id":15555559,"identity":"cfd45060-caf9-4feb-9ea9-fec164778498","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":47510,"visible":true,"origin":"","legend":"Box Plot of the ten series of monthly rainfall data for stations installed over the Urucuia Aquifer System area.","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/41a867dceb601c82e810aab0.png"},{"id":15556040,"identity":"7a3ea3e5-feb6-41a7-bc66-387194670b18","added_by":"auto","created_at":"2021-11-15 16:46:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":49623,"visible":true,"origin":"","legend":"Box Plot of the ten series of annual rainfall data for stations installed over the Urucuia Aquifer System area. ","description":"","filename":"fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/a39c3596d80628a533a12e8b.png"},{"id":15556365,"identity":"812237f2-42a9-4bce-ad50-bdf58326d08c","added_by":"auto","created_at":"2021-11-15 16:49:05","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":100117,"visible":true,"origin":"","legend":"Hyetogram of annual rainfall average at the Urucuia Aquifer System. ","description":"","filename":"fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/eb30a75d34d4146385236527.png"},{"id":15556043,"identity":"17ebba02-9399-4b85-a0cf-371844db3991","added_by":"auto","created_at":"2021-11-15 16:46:05","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":444976,"visible":true,"origin":"","legend":"Graph of monthly rainfall in the Urucuia Aquifer System region and of the monthly values of the AMO Index.","description":"","filename":"fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/c9213fe0011bbe0bf7765486.png"},{"id":15556367,"identity":"b11800fc-ff55-4a4a-b289-882f33eb0355","added_by":"auto","created_at":"2021-11-15 16:49:05","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":48889,"visible":true,"origin":"","legend":"Boxplot illustrating monthly precipitation for positive and negative AMO Index values.","description":"","filename":"fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/9c9c5dc3b10cfd6cd2339825.png"},{"id":15555564,"identity":"f26ef881-c335-4f1b-9245-712b13b477ab","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":162781,"visible":true,"origin":"","legend":"Graph of monthly precipitation in the Urucuia Aquifer System region and of the monthly values of the PDO Index. ","description":"","filename":"fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/6d049993e6554067527ead8c.png"},{"id":15555573,"identity":"08b172bc-5e7d-4691-a5af-c66669aa74b3","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":186099,"visible":true,"origin":"","legend":"Graph of monthly precipitation in the Urucuia Aquifer System region and of the monthly values of the Southern Oscillation Index and the Multivariate ENSO Index.","description":"","filename":"fig14.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/9c20da3c387b42b0fa8032a9.png"},{"id":15556049,"identity":"9994e541-b9b5-4b5b-8495-0ad0d1ae569a","added_by":"auto","created_at":"2021-11-15 16:46:05","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":1715915,"visible":true,"origin":"","legend":"Trajectories determined by the HySplit model of the 15 largest daily precipitation events on the Urucuia Aquifer System. ","description":"","filename":"fig15.png","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/925b45abfb0be768907acabe.png"},{"id":15556931,"identity":"957a2bb1-62a3-440a-ad92-271b4f2bbed9","added_by":"auto","created_at":"2021-11-15 16:55:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5664898,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/9a7b933f-6735-4f5b-86bf-a15f271c7ea4.pdf"},{"id":15556930,"identity":"c434a74f-6d08-4926-9499-204f25d5404b","added_by":"auto","created_at":"2021-11-15 16:55:05","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":290841,"visible":true,"origin":"","legend":"","description":"","filename":"AnalyseSeriesVieiraetal20oct2020.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/b85a0633337e0642c0c2d2ea.xlsx"},{"id":15555574,"identity":"5e13e33e-7ebc-4d42-9593-16362e1f7e87","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"rar","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":8907652,"visible":true,"origin":"","legend":"","description":"","filename":"FiguresArticleVieiraetal09aug2020.rar","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/10ea875202cdf40bb76df9f4.rar"},{"id":15555567,"identity":"27eb182a-25ae-417e-9aab-7b77b48520c0","added_by":"auto","created_at":"2021-11-15 16:43:05","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":141991,"visible":true,"origin":"","legend":"","description":"","filename":"INDEXVieiraetal20oct2020.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/962812bda1ddc5b74264ad0c.xlsx"},{"id":15556045,"identity":"5f6f18ad-f273-4fd9-8bed-95dd108ca18d","added_by":"auto","created_at":"2021-11-15 16:46:05","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1633485,"visible":true,"origin":"","legend":"","description":"","filename":"PonteAltaStationAnalysisVieiraetal20oct2020.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/e9a25584f0961bcc03695ec1.xlsx"},{"id":15556369,"identity":"792c8dee-8e53-4454-bea7-8daaa18b5b70","added_by":"auto","created_at":"2021-11-15 16:49:05","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":33388,"visible":true,"origin":"","legend":"","description":"","filename":"StatisticsResultVieiraetal20oct2020.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-806881/v1/f5f26ddaf6b31c328748bfef.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe Relationship Between The Atlantic Multidecadal Oscillation and The Urucuia Aquifer System Recharge\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePotential teleconectivity between patterns of climatic circulation and precipitation.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNo significant correlation was verified between precipitation and the Pacific Decadal Oscillation (PDO) index or Southern Oscillation Index (SOI) or the Multivariate El Ni\u0026ntilde;o South Oscillation (ENSO) index.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe regulatory reserve of the Urucuia Aquifer System (UAS) is constituted exclusively from the infiltration of rainwater.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIt was not possible to correlate the high daily rainfall rates with the occurrence of a predominant atmospheric system.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eCurrent studies demonstrate that climate change causes warming or cooling in the atmosphere, which causes changes in moisture retention at high heights, influencing the rainfall regime over various planet regions.\u003c/p\u003e \u003cp\u003eIn the central part of South America, normally, the observed rain is characterized by tropical and subtropical precipitation, with a marked seasonal cycle over the continent. In winter, it experiences its dry season, and, in the summer, a low-pressure zone located approximately at latitude of 25\u0026deg;S forces the east winds over the Amazon basin to turn south. These winds channel the wet masses between the eastern hillslope of the Andes and the Brazilian Plateau, which feed the convective summer storms over the subtropical plains to latitudes of 35\u0026deg;S (Garreaud et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, creating the South America Monsoon System which marks a well-defined rainy season in the summer and a season with little rainfall in winter (Liebmann and Mechoso \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssociated with this monsoon system, there are the minimum pressure belt and the intense low-level convergence of trade winds over the equatorial oceans, which cause a band of clouds with great vertical development. This system surrounds the globe close to the equator, forming storm activities, generated due to solar heating and the vertical movement of trade winds, corresponding to the Intertropical Convergence Zone (ITZC), where the active precipitation is mostly of a convective nature, produced by deep cumulus-nimbus (Garreaud et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). It is important to note that the ITCZ has more zonal behavior between November and December over the southern hemisphere when it starts moving south.\u003c/p\u003e \u003cp\u003eThe monsoon systems of South America and ITCZ, acting together, make Brazilian southeast that are close to the Tropic of Capricorn not to have characteristics of arid and dry areas, such as the Sahara, Atacama, Kalahari, and Australia. The air masses' dynamics create the meteorological system called the South Atlantic Convergence Zone (SACZ), which is a typical summer system. This system is characterized by a band of cloudiness oriented in the northwest-southeast direction, whose area of operation encompasses the center-south of the Amazon and the center-west and southeast regions of Brazil, and can also reach the center-south of Bahia, the north of Paran\u0026aacute; and extend to the South Atlantic Ocean (between the latitudes of 15S and 25S - southeast region of Brazil), associated with a convergence zone of the flow of humidity in the lower troposphere (Reboita et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese systems have already been extensively studied and had their influence on rainfall occurrence (Marengo and Dias \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; CEPED \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Reboita et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Marshall et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gan et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Escobar et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nielsen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rodrigues et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, several current studies show that climate change impacts the rainfall pattern in the central part of South America (Ouachani et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; R\u0026iacute;os-Cornejo et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhong et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and also indicate the that changes are influenced by teleconnections, which are trends in associations between the variability of atmospheric pressure and circulation patterns that occur in one location, with their effect elsewhere (Limberger \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese studies show the relationship between teleconnections and precipitation in South America, with several modes of variability. The climatic teleconnections more studies are (IPCC \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAtlantic Multidecadal Oscillation (AMO): natural variability based on average anomalies of sea surface temperatures (TSM) in the North Atlantic.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEl Ni\u0026ntilde;o South Oscillation (ENSO): natural variability over the tropical Pacific Ocean, which originates when El Ni\u0026ntilde;o and the South Oscillation co-occur, with El Ni\u0026ntilde;o being a process of high pressure and heating of sea temperatures, and the Southern Oscillation an atmospheric component, associated with changes in sea temperature.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePacific Decadal Oscillation (PDO): pattern of climatic variability centered in the North Pacific Ocean, which occurs due to the variation in the temperatures of the Pacific Ocean, whose positive phase provides an increase in temperature, decrease in humidity and increase numbers of occurrences and the intensity of El Ni\u0026ntilde;o, and in the negative phase, a consequent decrease in temperatures, an increase in air humidity and a greater incidence and intensity of La Ni\u0026ntilde;a.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn Figure \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the areas of teleconnection indexes presented the area for identifying teleconnection standards, adapted from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://meteorologia.unifei.edu.br/teleconexoes/\u003c/span\u003e\u003c/span\u003e, (Souza and Reboita \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and the location of the UAS area.\u003c/p\u003e \u003cp\u003eFor example, the relationship of Atlantic Multidecadal Oscillation and El Ni\u0026ntilde;o Southern Oscillation and its influence on rainfall in South America, analyzed by Kayano and Capistrano (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), showed that El Ni\u0026ntilde;o events, concomitant with the Cold AMO (CAMO) phase, are generally stronger than events in the Warm AMO (WAMO) phase due to negative temperatures in the inter-Pacific-Atlantic sea surface (Kayano and Capistrano \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother example is the study in Chile, that showed that the relationships between low-frequency variability of precipitation and the PDO are significant for the north of the country, while connections with the AMO are significant for the north and also for the south and that this low-frequency variability in Chile appears to be largely linked to PDO and AMO modulation (Vald\u0026eacute;s-Pineda et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe AMO's impact in the summer monsoon of South America was investigated in the La Plata River Drainage Basin (Chiessi et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The authors concluded that the sea surface temperature and the anomalies of atmospheric circulation triggered by AMO would control the variability of the South American Monsoon System (SAMS).\u003c/p\u003e \u003cp\u003eIn Brazil, a study carried out in dry and rainy years in the Northeast of the country showed that the main difference in the anomaly patterns of sea surface temperature between the AMO phases is the differential positioning of the heating or cooling surface waters in the equatorial Atlantic. As a result, Hadley and Walker's anomalous circulations also show differences between the phases of the AMO, justifying the precipitation anomalies observed in tropical South America (Kayano et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, to add more information about the climate interference on rainfall patterns, this study investigated the links between precipitation characteristics, especially volume and intensity, with meteorological systems and teleconnections. The objective is to detect correlations that help define better water availability scenarios, including assessing the issue of aquifer recharge, which has precipitation as a natural source. Because the regulatory reserve of aquifers is formed from the infiltration of rainwater.\u003c/p\u003e \u003cp\u003eThe area chosen to carry out this study is located on the Urucuia Aquifer System (UAS), a region experiencing an accelerated commercial agriculture expansion that began 30 years ago, to the point that it is currently considered one of the most important centers of irrigated agriculture of the country. However, the inherent scenario of high surface and underground water demand is accompanied by changes in vegetation cover, notably the suppression of extensive areas of Cerrado forest, and a high risk of contamination by fertilizers and pesticides, in addition to the inevitable urban expansion of certain outbreaks. Therefore, it is essential to know the region's water availability, considering the part of precipitation that will contribute to its recharge, influencing the regulatory reserve. If the meteorological systems and teleconnections influence the volume and intensity of precipitation, they must be considered in the management of water availability in the region of the UAS.\u003c/p\u003e\n\u003ch2\u003eThe Urucuia Aquifer System - Uas\u003c/h2\u003e\n\u003cp\u003eThe UAS is a groundwater reservoir located between latitudes 9\u0026ordm;25'38'' and 16\u0026ordm;15'00'' and longitudes 47\u0026ordm;41'42'' and 43\u0026ordm;46'43'' (Figure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), under the geomorphological unit called Western Bahia Plateau or S\u0026atilde;o Francisco Plateau (IBGE \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In an area of 121,653 km\u0026sup2;, this aquifer has altitudes ranging from 300 to more than 1000 m above sea level (Figure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) with steep escarpments and the presence of colluviums, comprising essentially sandy soils (Figure \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe climate in this region is classified as Tropical Savanna Climate (Aw), according to K\u0026ouml;ppen. It presents a wet season (excess water) corresponding to the summer, which occurs between October and April when the volume of precipitation is greater than evapotranspiration (Gaspar \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The dry season occurs from May to September, encompassing the whole Winter in South Hemisphere, and evapotranspiration is greater than precipitation, causing water deficit (Gaspar \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, during the period from May to September, the rivers of the region are maintained by the base flow, which according to Gaspar (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), corresponds to a mean value of 1,35x109 m\u0026sup3;/ano (42,8 m\u0026sup3;/s), which on average is equal to 20% of the precipitation (6,86 x109 m\u0026sup3;/ano or 1.241 mm) and 91% of the total streamflow (1,48 x109 m\u0026sup3;/ano or 46,9 m\u0026sup3;/s), based on a data series from 1982 to 2002. Also according to Gaspar (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), the average temperatures vary annually between 26\u0026deg; and 20\u0026deg; C, and the relative humidity of the air varies between 80% (December) and 50% (August).\u003c/p\u003e \u003cp\u003eHowever, in recent years, there has been climatic alteration, which points to an increase in the intensity of droughts in the region, as shown by the Drought Monitoring Project's maps (Figure \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e3\u003c/span\u003e) from the National Water Agency (ANA \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Figure \u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the spatial spread of the drought intensity over Brazil's northeast region in December of 2014, 2015, and 2016. The green rectangle highlights the area where the Urucuia aquifer is located. Comparing the spatial distribution along the years, we observed that the drought intensity has been increasing in the region.\u003c/p\u003e \u003cp\u003eTwo hypotheses are considered to explain this pattern: just the effect of agricultural expansion in the region, or the climatic variation exerts influence too, whether to arising from Low-Frequency Variability associated with teleconnections, or global climate change. That is a point to be addressed in this work.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eThe average monthly rainfall series were initially obtained from the historical daily series of ten gauges in the area of the UAS. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e displays general information on these. A simple descriptive statistical analysis was performed for these series to describe, evaluate, and summarize the data. Then, from these monthly precipitation data series, the annual precipitation data series for each station was defined and the representative regional monthly and annual precipitation series of the area.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInformation from the historical series of ten pluviometric stations installed over the Urucuia Aquifer System area.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eName\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInitials\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSub Basin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMunicipality / State\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatitude (\u0026deg;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLongitude (\u0026deg;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAltitude (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01145013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePonte Serafim \u0026ndash; Montante\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarreiras / BA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.8961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-45.6119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01245014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFazenda Joh\u0026aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFJH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBarreiras / BA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.1256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-45.8108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e725\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01245015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoda Velha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u0026atilde;o Desid\u0026eacute;rio / BA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.7653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-45.9439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01346006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFazenda Planalto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrentina / BA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.7519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-46.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e947\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01346007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFazenda Prainha (Faz. Antas)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u0026atilde;o Desid\u0026eacute;rio / BA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.3125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-46.0631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01347000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCavalcante\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCavalcante / GO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.7969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-47.4617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01445000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCajueiro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJanu\u0026aacute;ria / MG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.8361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-45.1733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01447000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlto Para\u0026iacute;so de Goi\u0026aacute;s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlto Para\u0026iacute;so de Goi\u0026aacute;s / GO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.1347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-47.5117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01447002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u0026atilde;o Jo\u0026atilde;o D\u0026apos;Alian\u0026ccedil;a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAJD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u0026atilde;o Jo\u0026atilde;o D\u0026apos;Alian\u0026ccedil;a / GO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.7072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-47.5236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01546005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCabeceiras\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCabeceiras / GO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.8008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-46.9247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe region in which the UAS is characterized by the rainy period from October to March, as in April initiating the dry period when the rains begin to decrease. In this work, monthly precipitation average data from September 1973 to August 2006 were used. This period of precipitation data was selected with the intention of the time series had only 10% of failures in the daily data and without impacting the monthly and annual total values of the stations. Therefore, no other consistency analyses were performed and there was no a failure filling.\u003c/p\u003e\n\u003cp\u003eThen, a local frequency analysis used in the precipitation series for each station and the regional averages (monthly and yearly) to assess the fulfilment of the criteria of independence, homogeneity, stationarity, and randomness, which are essential to verify whether the results are statistically valid (WMO \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). These criteria will be evaluated by the following non-parametric tests (Naghettini and Pinto \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). Randomness of values suggested by the test of NERC (\u003cspan class=\"CitationRef\"\u003e1975\u003c/span\u003e), which indicates the presence of a structure or intervention of a non-random nature; Independence of values is assessed with the test proposed by Wald and Wolfowitz (\u003cspan class=\"CitationRef\"\u003e1943\u003c/span\u003e), which verifies that no observation can influence the occurrence, or non-occurrence, of the next observation; Homogeneity of the sample is analyzed through the test by Mann and Whitney (\u003cspan class=\"CitationRef\"\u003e1947\u003c/span\u003e), which considers an homogeneous sample when all elements come from one and identical population; and the Stationarity of a series is verified by the Spearman test described by NERC (\u003cspan class=\"CitationRef\"\u003e1975\u003c/span\u003e), which is associated with the non-alteration of statistical characteristics over time, which means that there are no trends, jumps and other properties.\u003c/p\u003e\n\u003cp\u003eBesides the adjustments of the series to the Log-Normal distribution (Naghettini and Pinto \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), which presents good results for annual values averages (Pinto and Alves \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e), were evaluated by the Anderson-Darling (AD) adherence test, at the 5% level of significance, calculated by:\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere P is the probability of X \u0026lt; x, X \u0026eacute; random variate (RV), x is a value of RV, \u0026sigma; is the standard deviation of ln(x), \u0026micro; is the mean of ln(x).\u003c/p\u003e\n\u003cp\u003eThe empirical distribution estimate was performed with descending order of the series and the calculation of the plot position using the Weibull formula:\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ2\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$(\\text{P}\u0026gt;\\text{p})=\\frac{\\text{m}}{\\text{N}+1}$$ (2)\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere: m is the order number and N the sample size. For the annual series, the hydrological year starting in September and ending in August was considered, and the data dates from September 1973 to August 2006, a total of 33 years.\u003c/p\u003e\n\u003cp\u003eWith the distribution adjusted, it is possible to determine the return period of an observed rain height. The following correlation estimates the recurrence time or return period (T):\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ3\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe return period is associated with the probability that an event will be equalled or exceeded in any given year.\u003c/p\u003e\n\u003cp\u003eThen, the values of monthly and annual precipitation average representative of the area were correlated with the respective indexes of teleconnections, AMO, PDO, and ENSO, obtained from the NOAA website (NOAA \u003cspan class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003ec\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAMO is an index of natural variability, generated from anomalies in the sea surface temperature of the North Atlantic Ocean, in an interval of 5 to 8 decades (Santos et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), which cause changes in atmospheric circulation, based on three main processes: the atmospheric waves, the continuity of mass and the change of phases of the water (gaseous to liquid or solid). These anomalies are measured using the AMO Index, proposed by Raa et al. (\u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e) and (Oldenborgh et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), and makes it possible to evaluate the occurrence of gradients responsible for generating the phenomena called Atlantic dipole, which can be of positive or negative phase (Kayano and Andreoli \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The AMO Index monthly series is calculated from 1856 to 2020 (NOAA \u003cspan class=\"CitationRef\"\u003e2019b\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSimilar to AMO, the PDO teleconnection consists of a climatic phenomenon observed in the North Pacific Ocean, where a Sea Surface Temperature (SST) pattern is identified for 20 to 30 years, and which seems to coincide with the cooling periods (negative values) and warming (positive values) of the waters. It results in changes in marine ecosystems, in addition to affecting the currents\u0026apos; trajectories (Ndehedehe and Ferreira \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) as it affects the temperatures of the coastal sea and the continental surface air from Alaska to California. This variability is verified by the Decadal Oscillation Index, which is a function of the monthly anomalies of the sea surface temperature over the North Pacific (about 20\u0026deg;N) after removing the global average temperature of the sea surface (Molion \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Buffon and Binda \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The PDO Index monthly series was considered in this study from 1854 to 2020 (NOAA \u003cspan class=\"CitationRef\"\u003e2019c\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eENSO is a periodic variation in the tropical Pacific Ocean of winds and sea surface temperature, which occurs in the warming phase and accompanies the high pressure of the air surface (Walker cell changes), affecting the climate of a large part of the tropics and subtropics. ENSO can be assessed by the Southern Oscillation Index, which is a standardized index based on the differences in sea level pressure observed between Tahiti and Darwin in Australia, and the prolonged periods of negative oscillation values coincide with ocean waters abnormally warm throughout the eastern tropical Pacific, typical of El Ni\u0026ntilde;o episodes. The South Oscillation Index (SOI) series obtained was monthly from 1951 to 2020 (NOAA \u003cspan class=\"CitationRef\"\u003e2019a\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIt was also used to characterize the intensity of an ENSO event, the multivariate index, which consists of a complex interaction of a variety of climatic systems, and which is considered the most comprehensive index for monitoring El Ni\u0026ntilde;o, as it combines the analysis of multiple meteorological and oceanographic components (Mazzarella et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The series of the Multivariate Index (MI) obtained was monthly from 1979 to 2020 (NOAA \u003cspan class=\"CitationRef\"\u003e2019a\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTherefore, due to the data available at NOAA for each teleconnection, the period from September/1963 to August/2006 was used to relate the AMO, PDO, SOI (for ENSO) with the historical precipitation series. But to relate with MEI (for ENSO) was used the serie from September/1979 to August/2006.\u003c/p\u003e\n\u003cp\u003eThe climate indices were correlated with monthly and annual precipitation data form stations located in the UAS, in pairs, and for periods defined based on the phases of each index. The correlation was evaluated by analyzing the significance of the differences and using the Student\u0026apos;s t-test, and the results were considered statistically significant with a p-value \u0026le; 0.05. This is because the p-value depends directly on the data series and presents a quantitative measure that assists the decision-making process as evidence that if the null hypothesis (H0) is true, then the chance of random variation will be the only explanation for sample differences.\u003c/p\u003e\n\u003cp\u003eTherefore, a reasonable interpretation of the p-value (Arsham \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e) shows that there is robust evidence against H0 if P \u0026lt; 0.01; if 0.01 \u0026le; P \u0026lt; 0.05 there is moderate evidence against H0; if 0.05 \u0026le; P \u0026lt; 0.1 there is suggestive evidence against H0; but if P \u0026ge; 0.10 there is little or no real evidence against H0.\u003c/p\u003e\n\u003cp\u003eAfter analysing the correlation of the monthly and annual precipitation data with the values of the climate indices, the series of maximum daily rainfall over the Urucuia Aquifer System was determined, and the 15 largest events were chosen to analyse the retroactive trajectories of the air masses, and so try to estimate which atmospheric systems are operating and their origin.\u003c/p\u003e\n\u003cp\u003eThe retroactive trajectories of the air masses were estimated by the Lagrangian approach, which determines the water balance (Evaporation \u0026ndash; Precipitation), for a given period of time, from the sum of the humidity changes (Sodemann; Schwierz; Wernli, 2008), that is:\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ4\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\frac{\\text{D}\\text{q}}{\\text{D}\\text{t}}\\approx \\frac{\\varDelta \\text{q}}{\\varDelta \\text{t}}=\\text{E}-\\text{P}$$ (4)\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eKnowing that the moisture content is interpolated to its position every time (Stohl and James, 2004), water vapour gains and losses are measured by changes in specific humidity (\u0026Delta;q) over time (\u0026Delta;t). The rates of increase (x) and reduction (y) of specific humidity occur with the gains and losses of water vapour along the particle\u0026apos;s trajectory (\u0026Delta;q = x-y).\u003c/p\u003e\n\u003cp\u003eSo, for a large number of particles (K) passing over an area (A), the equation of (4) can be written as follows:\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equ5\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$\\text{E}-\\text{P}=\\frac{{\\sum }_{\\text{k}=1}^{\\text{K}}(\\text{x}-\\text{y})}{\\text{A}}$$ (5)\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThis Lagrangian modelling for trajectory determination was performed using the HySplit model (Stein et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), which uses the data set of the reanalysis project carried out by NOAA to produce new atmospheric analyzes using historical data (from 1948) and also produce analyzes of the current atmospheric state (Climate Data Assimilation System).\u003c/p\u003e\n\u003cp\u003eThe model estimates (at 1-degree resolution) and reports the position of the air packages (latitude, longitude, and height), pressure, and specific humidity in grams of water vapour per kilogram of air (g/kg), thus forming the trajectories of the air masses (Santos and Lima \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe trajectories were calculated for the days that precipitation occurred above 125 mm in the Urucuia aquifer region, a criterion of rainfall greater than the 3rd quartile of the data series, which corresponds to a limit commonly used in similar studies (Kim et al. \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e; Amato and Hopke \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; Alves et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The trajectories were started at heights from 10 to 6010 m, at intervals of 400 m, above ground level, and at the times of 12 h, 18 h of the previous day and 0 h and 06 h of the day of measurement, because all the measurements take place at 07 h and refer to the accumulated rainfall of the last 24 hours. The heights were defined based on previously performed works (Santos et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The trajectories were simulated retroactively in time, that is, from the specification of the destination coordinates (sampling point) the trajectories are plotted from the possible points of origin, depending on the climatic conditions, in a specified time interval (1-hour interval for 10 days or 240 hours), to the destination (Back Trajectory). The trajectory data show, for each storm, the path that the particle travelled defined considering time, latitude, longitude, and heights of the trajectory, informing the respective pressure and specific humidity of the particle, which allows a better analysis of the data.\u003c/p\u003e\n\u003cp\u003eIn other words, the HySplit model informs the rainfall trajectories according to the time, day and location, so it was necessary to evaluate the daily precipitation at the stations to detect the highest daily rainfall and where it occurred. It analyzed 22,400 trajectories (350 dates * 16 times * 4 times). However, only 15 daily rainfall records were higher than 122 mm, which was equivalent to 10% of the region\u0026apos;s average annual rainfall in a single day. Therefore, it analyzed with more detail to understand if their occurrence derives from the presence of any specific atmospheric system.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003eCharacterization of precipitation variability in the Urucuia Aquifer System region\u003c/h2\u003e\n \u003cp\u003eThe region in which the UAS is inserted is characterized by the rainy period from October to April, but in April, the rains begin to decrease, initiating the dry period, as can be seen in the (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), where monthly precipitation average data from September 1973 to August 2006 were used, in 94 pluviometric stations located on and near the area. The wettest quarter is December to February with a quarterly average of 619 mm, and the driest is from June to August with a quarterly average of 10 mm. The precipitation in the region begins in September, but the rainiest period occurs between October to April. The greatest precipitated volumes occur from west to east. The largest rainfall was observed in Ponte Alta do Bom Jesus station, located in the Tocantins River\u0026apos;s Hydrographic Region. Over Urucuia Aquifer System, the average annual precipitation is equal to 1220.8mm, defined between 1973 and 2006.\u003c/p\u003e\n \u003cp\u003eThis temporal variability of precipitation was analyzed by the spatialization of precipitation information from the National Hydrometeorological Network stations, from September 1973 to August 2006. This spatialization resulted in a surface or \u0026quot;raster\u0026quot;, where monthly rainfall estimates are made for all cells to generate a continuous map. In this work, the Kriging method was chosen, which according to Murara (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), is the most used method for spatialization of climatic data since it creates more uniform variations, not presenting variations and smoothing its representation since it homogenizes the information presented. After using the Rainfall Anomaly Index (RAI) methodology, proposed by (Rooy \u003cspan class=\"CitationRef\"\u003e1965\u003c/span\u003e), which characterizes the existence of dry periods (precipitation below the historical average) and rainy periods (annual precipitation above average), in addition to the intensity of the phenomenon in the period (Kraus \u003cspan class=\"CitationRef\"\u003e1977\u003c/span\u003e; Souza et al. \u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e; Freitas \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e; Ara\u0026uacute;jo et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Cruz et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Moraes \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Alves and Ara\u0026uacute;jo \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dutta et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Noronha et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; H\u0026auml;nsel et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cerqueira et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lima et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Surendran et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), the analysis showed 12 rainy years and 21 dry years in the region, considering the hydrological year beginning in September and ending in August of the following year, since 98.6% of the rain that occurred in the region above 2 mm, occurred between September to May of the following year and, besides, evaluating the precipitated volume throughout the period only 0.95% fell on UAS, between May and August.\u003c/p\u003e\n \u003cp\u003eThe isohyets map of annual rainfall average in the region (Figure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) assists in the evaluation of the behavior of annual rainfall average, showing that the rainfall in the western part of the UAS is higher, especially in the area of the \u0026ldquo;Ponte Alta do Bom Jesus\u0026rdquo; station, which presents a higher value of average annual precipitation compared to the other values of the analyzed rainfall stations. In contrast, the eastern part presents precipitation with lower annual averages, reaching values below 1000 mm in the vicinity of the area in which the aquifer is located. This spatial variation in rainfall is attributed to the orographic control of rainfall in which air masses are discharged progressively from west to east. It presents a niche of the greatest rainfall occurring around Ponte Alta do Bom Jesus Station, which reaches an average annual rainfall of 2,033 mm, much higher than the region\u0026apos;s average of 1,200 mm.\u003c/p\u003e\n \u003cp\u003eConsidering as a dry year, the years with annual precipitation below the historical average, and the years with annual precipitation above, such as rainy years, the variability of the spatialization of the precipitated volume in the dry and rainy years was analyzed, and the result showed that the spatialization of the rains demonstrated no difference between the dry or rainy years (Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) in the region. Observe the comparison of the average annual rain\u0026apos;s spatialization between dry and rainy years. In reference to the monthly historical average, dry years were lower monthly rainfall, and in rainy years observed higher monthly rainfall. Not observed differences between the occurrences of rains regardless of the dry or rainy year.\u003c/p\u003e\n \u003cp\u003eMoreover, from the evaluation of the precipitation spatialization in all months of the year, observing the existence of variability between the rainy and dry months, it is concluded that in the driest period of the year, between May and September, there is no variation in the spatialization of rain in the region, that is, it is uniform in the investigated area. However, in the wettest period of the year, between October and April, it is observed that the western region presents a greater volume of precipitation, especially in the region around the municipality of Ponte Alta do Bom Jesus (Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eHowever, concerning the precipitation of the \u0026ldquo;Ponte Alta do Bom Jesus\u0026rdquo; station (code: 01246000), where the concentration of precipitation occurs (Figures \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), it was observed that the behaviour of precipitation is different, with different mean and variance, such as show the daily hyetograms that compare the rainfall on the UAS and with that observed at the \u0026ldquo;Ponte Alta do Bom Jesus\u0026rdquo; station \u0026ndash; code: 01246000. In Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e, note through the daily hyetograms that precipitation over the Urucuia aquifer system is different from the frequency observed on the station, which is installed around the UAS region, not on it. Also, analyzing the series statistically by the t-test, assuming different variances, the results show that the data did not present correlation (p-value \u0026gt;\u0026gt;\u0026gt;\u0026gt; 0.05).\u003c/p\u003e\n \u003cp\u003eGeographically, the \u0026ldquo;Ponte Alta do Bom Jesus\u0026rdquo; station is not located on the UAS. However, on the slope area, which circulates the entire plateau, it was understood that the meteorological systems phenomena, which may influence and/or generate precipitation at this point, are not the same as those acting in the rains over the aquifer. Therefore, the precipitation in this place must be analyzed separately.\u003c/p\u003e\n \u003cp\u003eAnother issue is that, depending on the location of this station, the rains that occur in this location should not contribute to the recharge of the Regional Free Aquifer, which occupies the largest area in the basin, since the variable static level of this subsystem, varies from 4 to about 50 meters.\u003c/p\u003e\n \u003cp\u003eThe \u0026ldquo;Ponte Alta do Bom Jesus\u0026rdquo; station is located at an altitude of 518 meters, while the height of the plateau closest to this location is at an altitude of 903 meters, another at an elevation of 385 meters. Therefore, if the rain in this place contributes to the UAS recharge, it will only be for the free Deep Aquifer subsystem\u0026apos;s recharge, which can reach depths greater than 100 m.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eAnnual and monthly rainfall averages over the UAS area\u003c/h2\u003e\n \u003cp\u003eThe monthly precipitation series over the Urucuia aquifer show that the differences between the means and the variances are not significant, according to the tests for samples assuming equivalent means (z test) and equivalent variances (t-test), with the true null hypothesis (H0) claims that random variation will be the only explanation for sample differences (p-value \u0026lt;0.05). But, when we compare the monthly flows series of one station with the other stations series, the results found were not statistically significant (p-value \u0026ge; 0.05), because the lowest p-value found was equal to 0.057.\u003c/p\u003e\n \u003cp\u003eThe stations\u0026apos; series of monthly data were correlated in pairs through the coefficient of determination (R\u0026sup2;) to adjust a generalized linear statistical model. Thus, it was found 45 R\u0026sup2; values for correlations with monthly data, with the lowest value being 0.6564 and the maximum 0.8336, for an average R\u0026sup2; of 0.7455, which allows to conclude that there is a good correlation between the series of monthly precipitation of the stations located on the UAS in the period of 33 years (1973 to 2006), which represents the main source of recharge for this aquifer.\u003c/p\u003e\n \u003cp\u003eTherefore, the station series of monthly precipitation data showed equivalent averages and variances, in addition to a good correlation, when correlated with the regional monthly precipitation series, which the lowest R\u0026sup2; found was 0.8352, indicating a good correlation.\u003c/p\u003e\n \u003cp\u003eEvaluated by the series formed by each month, the monthly data were divided into twelve series for each station. The series was assembled month by month. That is 120 series analysed. Nine series did not approve: three in independence teste (July series from Ponte Serafim \u0026ndash; Montante station, July series from Fazenda Joh\u0026aacute; station and June from Fazenda Prainha station); two in randomness test (May and June series from Cabeceiras station); and four in stationarity test (June series of Ponte Serafim \u0026ndash; Montante station, and October series of Cavalcante, Alto Para\u0026iacute;so de Goi\u0026aacute;s, and S\u0026atilde;o Jo\u0026atilde;o d\u0026apos;Alian\u0026ccedil;a stations).\u003c/p\u003e\n \u003cp\u003eThe homogeneity test as not evaluated due to the small size of the series. However, even passing the criteria of independence, randomness, and stationarity, all series from May to September were not accepted by the Anderson-Darling (AD) adherence test, at the 5% significance level, to be modelled due to the Log-Normal distribution, this occurs precisely due to the occurrence of the dry season in the region, where rainfall is not frequent.\u003c/p\u003e\n \u003cp\u003eIn May and September, which are the months of the beginning of the descent and rise of precipitated volumes, it was possible to adjust Gumbel extremes\u0026apos; distribution, being accepted by the Anderson-Darling (AD) adhesion test, to the level of 5% of significance. For the series of November, December, January, and February, the null hypothesis was accepted by the AD test for 5% of significance, showing that the Log-Normal distribution can model the series of these months.\u003c/p\u003e\n \u003cp\u003eAlso, analyzing the series of mean data on the UAS, month by month, it was observed that the Log-Normal distribution could also model the series for December, January, February, March, and April since the null hypothesis was accepted by the AD adherence, at the 5% level. The data of the descriptive statistics of the monthly precipitation series are shown in Figure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The higher monthly rainfall totals range from 524 mm to 780 mm. However, most of the observed precipitations are less than 250 mm.\u003c/p\u003e\n \u003cp\u003eThe total annual precipitation data series of each station on the UAS, in addition to the series of regional annual total precipitation, over the period of 33 years, were also evaluated.\u003c/p\u003e\n \u003cp\u003eIt is concluded that the series are independent and stationary, except for the \u0026ldquo;Fazenda Planalto\u0026rdquo; station (code: 01346006), which presented dependent values and presented non-stationary values on \u0026ldquo;S\u0026atilde;o Jo\u0026atilde;o d\u0026apos;Alian\u0026ccedil;a\u0026rdquo; station series (code: 01447002) and regional annual totals series (Figure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of the ten series of annual rainfall data for stations installed over the Urucuia Aquifer System area and the series of regional annual total precipitation.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"10\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003eCode Station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eName Station\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eInitials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 25.1118%;\"\u003e\n \u003cp\u003eTest statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 20.856%;\"\u003e\n \u003cp\u003eTest result\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStationary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndependence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStationary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01145013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003ePONTE SERAFIM - MONTANTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01245014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eFAZENDA JOH\u0026Aacute;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eFJH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e-1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01245015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eRODA VELHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eRVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01346006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eFAZENDA PLANALTO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eFPL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e-2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eDependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01346007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eFAZENDA PRAINHA (FAZ.ANTAS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01347000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eCAVALCANTE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eCAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01445000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eCAJUEIRO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eCAJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e-0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01447000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eALTO PARA\u0026Iacute;SO DE GOI\u0026Aacute;S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eAPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01447002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eS\u0026Atilde;O JO\u0026Atilde;O D\u0026apos;ALIAN\u0026Ccedil;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eSJA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eNo Stationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 9.7804%;\"\u003e\n \u003cp\u003e01546005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 21.1468%;\"\u003e\n \u003cp\u003eCABECEIRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.8727%;\"\u003e\n \u003cp\u003eCAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eStationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\" style=\"width: 37.7999%;\"\u003e\n \u003cp\u003eANNUAL MEAN REGIONAL SERIES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.8377%;\"\u003e\n \u003cp\u003e-2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 14.2741%;\"\u003e\n \u003cp\u003eIndependent Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 12.8203%;\"\u003e\n \u003cp\u003eNo Stationary Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eNevertheless, all the annual totals indicated a linear downward trend, represented by the hyetogram of the series of regional annual total precipitation (Figure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). The Mann-Kendall test was applied to define whether there is a statistically significant time trend, where the p-value of the test less than the level of significance (0.10, 0.05 and 0.01), indicates statistically significant evidence to reject the null hypothesis H0, which indicates an increasing trend in the data (positive S) or decreasing (negative S) (WMO \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Chiew and Siriwardena \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). And results indicated that there is and that a large part of the differences between the values in the time series was negative, pointing to a downward trend over time (S = -172 and p-value = 0.004). So, the series of annual totals indicated a linear downward trend, represented by the hyetogram of the series of regional annual total precipitation. This downward trend was also observed in the precipitation series between 1980 and 2015 (Costa et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFrom the total annual rainfall series evaluated by the Anderson-Darling (AD) adherence test, at the 5% level of significance, eight accepted to modelled by the log-normal distribution(Naghettini and Pinto \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), with the parameters of position (\u0026beta;) and form (\u0026alpha;) calculated by the L-moment method (Hosking and Wallis \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThis information makes it possible to calculate the quartiles associated with different return times or calculate the return time for each annual quartile, as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLocal frequency analysis results of the annual rainfall series from the stations installed over the Urucuia Aquifer System area and of the regional annual total precipitation series.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eCode Station\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eInitials\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eParameters of the log-normal distribution model\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAnnual Precipitation for Return Time Equal 100 Years\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eAverage Annual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eMaximum Annual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eMinimum Annual\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026xi;\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Tr = 100 years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTr (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTr (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTr (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01145013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1937.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1186.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e762.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01245014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFJH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1870.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1247.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e824.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01245015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2172.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1258.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e660.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01346007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1930.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1311.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e907.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01347000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.4922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2628.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1817.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1311.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01445000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAJ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.0428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1904.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1170.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e765.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01447000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2339.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1485.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e964.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e01546005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCAB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.2315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2339.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1415.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e921.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFailure in the independence and stationarity tests implies the non-acceptance to model by the log-normal distribution; therefore, \u0026ldquo;Fazenda Planalto\u0026rdquo; and \u0026ldquo;S\u0026atilde;o Jo\u0026atilde;o d\u0026rsquo;Alian\u0026ccedil;a\u0026rdquo; stations do not appear in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The statistical tests showed that the annual precipitation series is independent and stationary.\u003c/p\u003e\n \u003cp\u003eNevertheless, when dividing the series half, with 1989/1990 hydrological year of as the series center, the analysis shows that up to 1989/1990, 73% of the rainfall had a return time of up to 5 years and 27% greater than six years. After this hydrological year, 92% of the rainfall had a return time of up to five years and only 8% greater than six years. That represents an 18% decrease.\u003c/p\u003e\n \u003cp\u003eAnalyzing also the average of all annual rainfall, it is observed that there is a significant difference between the period up to the hydrological year 1989/1990 and the subsequent period, which leads to the assumption that the existence of external climatic phenomena may influence some of the precipitation characteristics, mainly volume and intensity, such as, for example, meteorological systems and teleconnections, which would imply considerable changes in water availability, even so, the issue of aquifer recharge.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eComparison of total regional precipitations (annual and monthly) with Climate Indexes\u003c/h2\u003e\n \u003cp\u003eAnalyzing the North Atlantic SST anomaly time series, Enfield et al. (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e) found that the positive (warm) AMO phase occurred during the periods 1860-1885 and 1925-1965 and the negative (cold) AMO phase, during the periods 1895-1924 and 1970-1990. And the downward trend, shown in the hyetogram in Figure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, shows that the greatest amounts of rainfall occurred between 1973 and 1990, a period in which the negative (cold) phase of the AMO occurred, according to other studies (Raa et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Oldenborgh et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), and for this reason, this was the first teleconnection, whose index was compared with regional precipitation data.\u003c/p\u003e\n \u003cp\u003eHowever, analysis of the AMO index series showed that the negative AMO phase occurred until 1993 and, only in 1994, a positive AMO phase started, a fact confirmed in other studies too (Vald\u0026eacute;s-Pineda et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Thus, and considering that the series are independent and stationary, the analyzes used the complete series to compare the indexes without links to the hydrological year of 1989/1990.\u003c/p\u003e\n \u003cp\u003eThe analysis compared the precipitation annual with the average AMO index by means of correlation analysis, and the results showed statistical significance (p-value \u0026le; 0.03), revealing the potential teleconectivity of the climatic circulation patterns with average annual precipitation over the UAS. Correlating also the monthly precipitation average values in the region with the AMO indices values (Figure \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e), a p-value less than 0.001 is obtained for the regional data series and less than 0.025 for the series of monthly data of the stations, proving a strong correlation between this teleconnection and precipitation in the region. Therefore, these results suggest a correlation between AMO and total annual and monthly rainfall in the region where the Urucuia aquifer occurs.\u003c/p\u003e\n \u003cp\u003eSearching for more evidence of this correlation, the series of monthly rainfall that occurred when the AMO index was positive was compared with the series of rainfall that occurred when this index was negative, using the t-test at the 0.05 level of significance. In this case, it is concluded that there is a significant difference between the averages of precipitation that occur when the oscillation index is positive or negative (p-value \u0026lt; 0.008).\u003c/p\u003e\n \u003cp\u003eBased on this evidence, the boxplot graph was proposed (Figure \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e), where the differences between the means (123.3 and 94.9 mm), the 3rd Quartiles (207.5 and 166.9 mm), and the 1st Quartiles (15.3 and 2.7 mm) can be observed, that suggest interference of AMO in the negative and positive phase of the dipole. The medians (99.5 and 51.9 mm) also show this difference. This means that there is, in the positive phase of the dipole, inhibition of cloud formation, decreasing precipitation, and causing dry periods. This is because the North Tropical Atlantic waters become warmer, and the Equatorial Atlantic and Tropical South waters become colder. In contrast, in the negative phase of the dipole, the North Tropical Atlantic waters become colder and the waters of the South Tropical Atlantic warmer, generating an increase in upward movements over these regions, intensifying the formation of clouds and increasing rainfall.\u003c/p\u003e\n \u003cp\u003eRegarding the PDO phenomenon, the mean precipitation series over the UAS did not show any correlation (Figure \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e), as the results were not statistically significant, neither with the annual data, where p-values greater than 0.7 were observed nor with the data monthly, whose lowest p-value was greater than 0.1, since p-values \u0026ge; 0.10 indicate little or no real evidence of correlation (Arsham \u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e). Still looking for some correlation, the series analysis was performed according to the hot or cold phase of the PDO.\u003c/p\u003e\n \u003cp\u003eAs several authors (Mantua et al. \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e; Molion \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kayano and Andreoli \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Alves \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) stated that the cold phase of the PDO occurred between 1947 and 1976 and the warm phase between 1977 and 1998, it was correlated monthly and annual rainfall with the PDO index for the period between 1947 and 1976, then for the period between 1977 and 1998, but no correlation was also obtained, resulting in a p-value \u0026ge; 0.8.\u003c/p\u003e\n \u003cp\u003eThe regional monthly and annual precipitation series also did not correlate with the Southern Oscillation Index, as the results were also not statistically significant, with p-value \u0026ge; 0.5 for monthly data and p-value \u0026ge; 0.3 for the annual data (Figure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e). The Multivariate ENSO Index application also did not show any correlation since the p-values were greater than 0.7. That is, they were not statistically significant.\u003c/p\u003e\n \u003cp\u003eThis is a noteworthy point since no correlation was detected that indicated ENSO and PDOs influence on the precipitation of the studied area. However, some of the studies mentioned above indicate that the negative phase of these indices is favour precipitation over the Northeast of Brazil because there is an increase in rainfall intensity along the rainy season.\u003c/p\u003e\n \u003cp\u003eIn their positive phase, the PDOs and ENSO there seems to occur a greater number of El Ni\u0026ntilde;o episodes and more intensely, and a lower number of La Ni\u0026ntilde;a events, which occur less intensely. The opposite occurs in the negative phase, where there is a higher occurrence of La Ni\u0026ntilde;a episodes, which tend to be more intense, and a lower frequency of El Ni\u0026ntilde;os, which tend to be short and fast.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eComparison of the UAS total, annual, and monthly rainfall patterns with meteorological systems\u003c/h2\u003e\n \u003cp\u003eSome of the studies previously mentioned have shown that the ENSO and PDO teleconnections also greatly influence meteorological systems, as they affect the longitudinal positioning of the ascending branches of the Walker Cell (Wang \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e). This is because, in the hot phase, its main ascending branch is positioned over the warm waters of the Pacific and causes subsidence and high pressure over the North of South America, blocking the Intertropical Convergence Zone further north and the cold Fronts of the Southern Hemisphere and the South Atlantic Convergence Zone (ITCZ) further south, which causes severe droughts in the region. In the cold phase, there is an intensification of the Walker Cell\u0026apos;s ascending branches on the continents, increasing the rainfall totals. Therefore, if it was not possible to detect an influence between the ENSO and the PDOs with the precipitation series, it came up that it may be possible to define a relationship between the series and the meteorological systems that act on the UAS, which was investigated through simulation based on the HySplit model.\u003c/p\u003e\n \u003cp\u003eHowever, it is important to note that the HySplit model informs rain trajectories according to time, day, and location, so it was necessary to evaluate the daily rainfall in the stations to detect the greatest daily rainfall and where it occurred. In the assessment of the daily rain series, 12,053 days were analysed in 10 stations and found 24% of failures in the data series and 56.3% of the days have no rain occurrences (precipitation equal to zero).\u003c/p\u003e\n \u003cp\u003eFor the other 19.7% of the data, on 54.7% of them precipitation were less than 10 mm, on 42.7% were greater than 10 mm and less than 60 mm and only 2.6% were greater than 60 mm. In other words, only 618 rain records greater than 60 mm verified on the UAS. In the period analysed from September 1973 to August 2006, there are only 49 rain daily records greater than 100 mm, equivalent to only 0.2% of observation records in the region (Figure \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo define from which precipitation value the daily data would be analyzed by the HySplit model, the series of maximum annual precipitation of the stations was evaluated, and it was found that for the recurrence time of 1 year, the rain would be 74 mm. Thus, all precipitations equal to or greater than 70 mm were analysed. That is, 22,400 trajectories were analyzed (350 dates * 16 times * 4 times).\u003c/p\u003e\n \u003cp\u003eWith this assessment, it was possible to observe that the specific humidity of the air and the pressure decrease exponentially with the altitude, but in the period from 1973 to 1990 (negative AMO) the specific humidity was 26% higher than in the period from 1990 to 2006 (positive AMO), which shows a greater amount of water vapor in the atmosphere in the period of negative AMO concerning the period of positive AMO. The trajectory data showed no variation between the values of specific air humidity and atmospheric pressure with the increase or decrease in the total precipitation, as the results were not statistically significant, with a p-value \u0026ge; 0.5.\u003c/p\u003e\n \u003cp\u003eOn average, the trajectories along the negative AMO phase were at higher latitudes closer to the ITCZ, showing that this atmospheric system may have an influence on precipitation during the negative phase of the AMO. However, in the phase of positive AMO, on average, the trajectories latitudes were lower, closer to the Tropic of Capricorn, suggesting that the rainfall received little influence from ITCZ in the phase of positive AMO, and perhaps more influence from the Atlantic Subtropical Anticyclone Sul (ASAS), which transport heat and humidity from the Atlantic Ocean to the interior of Brazil.\u003c/p\u003e\n \u003cp\u003eIn the positive AMO phase, the trajectories presented anticlockwise snail aspects around the initial coordinates, mainly at higher altitudes, suggesting the presence of anticyclonic vortexes, which are high-pressure regions formed by a descending dry air mass, which due to its weight, force to descend. This dry air causes low relative humidity levels and prevents the formation of clouds and precipitation (Kousky and Alonso Gan \u003cspan class=\"CitationRef\"\u003e1981\u003c/span\u003e; Jury and Engert \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e). This type of trajectory suggests the influence of ASAS in its formation.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e shows the sets of trajectories estimated for the 15 largest daily precipitation events in the area over the UAS. It is important to highlight that the color scale used is associated with each moment of the trajectories\u0026apos; specific moisture content. A more bluish coloration represents the trajectories that present the highest moisture content and are generally below 2000 m of altitude, corroborating with (Wallace and Hobbs \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The opposite occurs with the reddest trajectories, which have less moisture content and are generally at higher atmospheres levels.\u003c/p\u003e\n \u003cp\u003eThais is, the color scale used is associated with the specific moisture content of each trajectories moment. A more bluish coloration represents the highest moisture content trajectories and is generally below 2000 m of altitude. The opposite occurs with the reddest trajectories, which have less moisture content and are generally at higher atmospheres levels. Moreover, it is possible to observe different types of storms generated by different atmospheric systems, but all coming from the South Tropical Atlantic Ocean. About the systems, observed indication a direct influence from the South Atlantic Convergence Zone, the predominance of the Amazon systems with a strong contribution from systems in the Equatorial Atlantic Ocean, the performance of High-Level Cyclonic Vortexes, and the performance of cold fronts from the Southern or Indian Ocean.\u003c/p\u003e\n \u003cp\u003eAlso, in Figure \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e, it is possible to observe different storms generated by different atmospheric systems. However, all coming from the South Tropical Atlantic Ocean, corroborating the hypothesis that AMO, in its cold (negative) phase, influences precipitation generated in the region.\u003c/p\u003e\n \u003cp\u003eConcerning the systems, it can be seen that the period from 1977 and 1988 had a direct influence from the South Atlantic Convergence Zone, but in 1978 one can assume the predominance of systems from the Amazon with a strong contribution from systems in the Equatorial Atlantic Ocean. In 1982, it suggested the performance of High-Level Cyclonic Vortexes. In 1976, there is an indication of cold fronts\u0026apos; performance from the Southern or Indian Ocean. Therefore, it was not possible to correlate the high daily rainfall with a predominant atmospheric system.\u003c/p\u003e\n \u003cp\u003eAs the rain pattern distribution shows a decreasing trend in the amount of precipitation in the last 45 years in the Urucuia Aquifer area it is expected a proportional reduction in groundwater recharge. This reduction is more important to the confined aquifer subsystem, but also may also affect the unconfined subsystems and must be considered when developing the management actions of the regional water resources.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this work, ten series of total annual rainfall evaluated by the Anderson-Darling (AD) adherence test, at the 5% level of significance, but eight were accepted to be modeled by the log-normal distribution, with the position parameters (β) and shape (α) calculated by the L-moments method, namely: Ponte Serafim Montante (01145013), Fazenda Joh\u0026aacute; (01245014), Roda Velha (01245015), Fazenda Prainha (01346007), Cavalcante (01347000), Cajueiro (01445000), Alto Para\u0026iacute;so de Goi\u0026aacute;s (01447000) and Cabeceiras (01546005). It showed that rainfall values are independent and stationary, with no indication of abrupt changes to suggest interference of climate change.\u003c/p\u003e \u003cp\u003eHowever, analyzing the average of all annual rainfall, it is observed that there is a significant difference between the series\u0026rsquo; first 16 years and the series\u0026rsquo; last 16 years, which leads to considering that external climatic phenomena may influence mainly the volume and intensity of the precipitation.\u003c/p\u003e \u003cp\u003eAnd it was possible to obseve that the total annual rainfall da series over the UAS indicates a downward linear trend, confirmed by the Mann-Kendall test (p-value = 0.004), which justify a decrease in the UAS recharge and, consequently, a decrease in the base flow from the aquifer.\u003c/p\u003e \u003cp\u003eThis decrease in annual precipitation can be explained by the AMO phase change, because the correlation of the monthly and annual rainfall with the AMO index, were statistically significant, revealing the potential teleconnection of the sea surface temperature of the North Atlantic Ocean with precipitation over the Urucuia Aquifer System.\u003c/p\u003e \u003cp\u003eFurthermore, the comparison between the monthly rainfall series that occurred when the AMO index was positive with the series of rainfall that occurred when this index was negative showed a significant difference between the average precipitation in the dipole's negative and positive phases.\u003c/p\u003e \u003cp\u003eTherefore, in the positive phase of the dipole, there is the inhibition of cloud formation, a decrease in precipitation, and, consequently, causing dry periods because the waters of the North Tropical Atlantic become warmer and the waters of the Equatorial Atlantic and Tropical South become colder. On the other hand, in the negative phase of the dipole, the North Tropical Atlantic waters become colder and the South Tropical Atlantic warmer waters, generating an increase in upward movements over these regions, intensifying the formation of clouds and increasing rainfall.\u003c/p\u003e \u003cp\u003eNo correlation was found between precipitation over UAS and climate indices: Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), El Ni\u0026ntilde;o Multivariate Southern Oscillation Index (ENSO). However, the correlation between precipitation and PDO and ENSO teleconnections may not have occurred due to the indexes chosen to represent these phenomena, as there are several indexes that can be used in the analysis. Therefore, we indicate a more detailed exploratory analysis to conclude if the phenomena that occur in the Equatorial Pacific Ocean influence precipitation over the Urucuia Aquifer System.\u003c/p\u003e \u003cp\u003eRegarding the evaluation of the precipitation trajectories, it was observed that the specific humidity of the air and the pressure decrease exponentially with the altitude, but that the specific humidity in the period from 1973 to 1990 (negative AMO) was 26% higher than the specific humidity in the period of 1990 to 2006 (positive AMO), which shows a greater amount of water vapor in the atmosphere during the negative AMO period compared to the positive AMO period. However, the trajectory data show no variation between the values of specific air humidity and atmospheric pressure with the increase or decrease in the total precipitation, as the results were not statistically significant.\u003c/p\u003e \u003cp\u003eOn average, the trajectories along the negative AMO phase, on average, occurred at greater latitudes and closer to the Intertropical Convergence Zone (ITCZ), showing that this atmospheric system can influence the precipitation during the negative phase of the AMO.\u003c/p\u003e \u003cp\u003eIn contrast, in the positive AMO phase, on average, the latitudes of the trajectories were lower and closer to the Capricorn Tropic, suggesting that the rainfall received little influence from ITCZ in the phase of positive AMO, and perhaps more influence from the South Atlantic Subtropical Anticyclone (SASA).\u003c/p\u003e \u003cp\u003eIn the phase of positive AMO, it was observed that the trajectories had snail aspects around the initial coordinates, mainly at higher altitudes, which may suggest the presence of high-level cyclonic vortex, as these are closed cyclonic circulations in the upper troposphere as shown by the atmospheric waves, whose intensity is higher in the upper troposphere and decreases towards the surface (Reboita et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was also observed that different atmospheric systems generate the storms, but all coming from the South Tropical Atlantic Ocean, corroborating the hypothesis that AMO influences the precipitation in the region.\u003c/p\u003e \u003cp\u003eAnd, concerning the systems, it is possible to identify a possible performance of the South Atlantic Convergence Zone, of systems coming from the Amazon with a strong contribution from equatorial Atlantic Ocean systems, High-Level Cyclonic Vortexes, and cold fronts coming from the Antarctic or the Indian Ocean. But, it was not possible to correlate the high daily rainfall with a predominant atmospheric system.\u003c/p\u003e \u003cp\u003eTherefore, we conclude that as the precipitation pattern shows a decrease trend in the last decades it is expected a reduction in the aquifer recharge and in the water availability in the near future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eThe authors would like to thank the Geological Service of Brazil (SGB/CPRM) for the financial and technical support for this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlves AS, Ara\u0026uacute;jo LE de (2015) Avalia\u0026ccedil;\u0026atilde;o clim\u0026aacute;tica da precipita\u0026ccedil;\u0026atilde;o da bacia hidrogr\u0026aacute;fica do rio Camaratuba \u0026ndash; PB. 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Clim Dyn 49:3123\u0026ndash;3139. https://doi.org/10.1007/s00382-016-3500-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urucuia. Teleconexões. Precipitation. Atlantic Multidecadal Oscillation (AMO). ","lastPublishedDoi":"10.21203/rs.3.rs-806881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-806881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates and detects links between the precipitation characteristics with meteorological systems and teleconnections around the Urucuia Aquifer System (UAS). Several studies show the influence of meteorological systems and teleconnections on the volume and intensity of precipitation in South America, mainly the Atlantic Multidecadal Oscillation (AMO), the El Ni\u0026ntilde;o South Oscillation (ENSO), and the Pacific Decadal Oscillations (PDO). Then, the precipitation series\u0026rsquo; statistical characterization impacted the aquifer system\u0026rsquo;s recharge from 1973 to 2006. Monthly and annual series were analyzed and tested the correlation analysis with the indexes of the AMO, PDO, and ENSO. Finally, the series of maximum daily rainfall on the UAS was determined, and the 15 largest events were chosen to analyze the retroactive trajectories of air masses and thus try to estimate which atmospheric systems was acting and their origin. It concluded that the total annual precipitation data indicated a decreasing linear trend and that external climatic phenomena can influence precipitation characteristics. The correlation with the AMO index revealed a potential teleconectivity between climate circulation patterns with average annual precipitation over the UAS (p-value \u0026le; 0.03). Moreover, the analysing of precipitation trajectories observed a greater amount of specific humidity in the atmosphere during the AMO negative period concerning the AMO positive period. Also, the negative AMO phase\u0026rsquo;s trajectories had higher latitudes closer to the Intertropical Convergence Zone, as opposed to the positive AMO phase, where the trajectory altitudes were lower and closer to the Capricorn tropic.\u003c/p\u003e","manuscriptTitle":"The Relationship Between The Atlantic Multidecadal Oscillation and The Urucuia Aquifer System Recharge","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-11-15 16:43:02","doi":"10.21203/rs.3.rs-806881/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-11-12T19:42:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-11-12T19:18:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-11-12T10:14:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2021-08-11T19:00:01+00:00","index":"","fulltext":""},{"type":"decision","content":"Major Revision","date":"2021-05-06T11:32:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5cd6876b-b593-4438-a37a-ee6171e88c2a","owner":[],"postedDate":"November 15th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":8514772,"name":"Atmospheric Sciences"}],"tags":[],"updatedAt":"2022-06-27T12:14:27+00:00","versionOfRecord":[],"versionCreatedAt":"2021-11-15 16:43:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-806881","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-806881","identity":"rs-806881","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}