Open access peer-reviewed chapter - ONLINE FIRST

Rainfall Trends in Humid Temperate Climate in South America: Possible Effects in Ecosystems of Espinal Ecoregion

By Julian Alberto Sabattini and Rafael Alberto Sabattini

Submitted: February 8th 2021Reviewed: June 25th 2021Published: July 28th 2021

DOI: 10.5772/intechopen.99080

Downloaded: 24

Abstract

In central Argentina, the annual rainfall regime shows increasing since the 2nd half of the 20th century. The aim of this work was to evaluate the long-term changes in the intensity of rainfall in the central-north region of Entre Ríos between 1945 and 2019, based only on daily precipitation records aggregated at yearly, monthly and seasonal levels. We used monthly rainfall data for the period 1945–2019 from 6 localities in Province of Entre Rios, Argentina. The change detection analysis has been conceded using Pettitt’s test, von Neumann ratio test, Buishand’s range test and standard normal homogeneity (SNH) test, while non-parametric tests including linear regression, Mann-Kendall and Spearman rho tests have been applied for trend analysis. Like the regional results, this study observed a sustained increase in monthly rainfall to the breaking point in the 1970s, but then the annual rate of increase was even higher. The average annual rainfall in the region prior to that date was 946 mm, while after the same 1150 mm, equivalent to 21.5% higher than the 1945–1977 average and 8.5% higher according to the historical average 1945–2019.

Keywords

  • nonparametric trend tests
  • climate change
  • natural ecosystems
  • biodiversity

1. Introduction

There was a general increase in air temperature worldwide during the twentieth century, albeit with some differences between the hemispheres, corresponding to global warming. Global warming affects the hydrological cycle over land, resulting in observed changes to precipitation frequency, intensity, duration and amount [1, 2]. Although significant attention is paid to how changes in seasonal and annual precipitation sums affect ecosystems, relatively less is known about the ecological impacts of heavy rainfall events [3]. The evaluation of past trends of meteorological parameters at various spatial and temporal scales plays a crucial role in understanding climate change and its impact on food security, energy security, natural resource management, and sustainable development [4, 5]. Detailed analysis of rainfall trend is useful to rainfall forecasting, planning water resources development and management, designing water storage structures, irrigation practices and crop choices, drinking water supply, industrial development, and disaster management for current and future climatic conditions [6, 7].

The analysis of different global rainfall databases shows a change in an anomaly that was positive between 1950 and 1980 and became negative later [8]. While some studies show increasing rainfall, in other regions the evaluations show the opposite results. For instance, in Europe, the rain series show an increase in annual precipitation between 1940 and 1990 [9]. The climate of Italy, in turn, seems to be warmer and drier at the moment with a decrease in rainfall attributed to a reduction in the number of days of rain, as rainfall intensity shows a positive trend [10]. In different regions of South America somethings similar happens and has been studied by several authors. More recently [11] studied summer precipitation variability over Southeastern South America in a global warming scenario.

In central Argentina, the annual rainfall regime shows increasing rates from approximately the 1940s until the end of the century [12, 13, 14] with statistical and spectral analysis show that there is significant evidence that rainfall has increased in central Argentina since the 2nd half of the 20th century [15] analyzed breakpoints in annual rainfall trends in Córdoba, Argentina in the period 1930–2006, they observed from negative to positive in the 1950s in the north area of the region, while in the other areas the opposite change occurs in the 1970s. From the mid-1970s, a sharp increase in rainfall regime provided most of the area with a supply of moisture higher than previously reported [16, 17, 18, 19]. Recently results in changes annual rainfall in five sub-regions of the Argentine Pampa Region indicate that the Western Pampas are more vulnerable to abrupt changes than the Eastern Pampas [20]. While different indicators in central Argentina reflect a change for precipitation at some sites, the intensity and variability of rainfall show significant long-term trends [21]. The rainfall cycle hypothesis has been supported by recent studies showing an abrupt negative change in the water regime of Pampas Region in recent years [17, 18] as well as by studies linking changes in rainfall with regular or recurring oceanic indices [19, 20, 21].

A strong increase in agricultural activity in central of Argentina [22] is a possible cause that would explain the climate change. The central-north region of Entre Ríos (Argentina) had a strong fragmentation of the landscape due to deforestation [23]. These changes are environmentally and economically important, as they have a direct impact on hydrological and soil resources, as well as on the agricultural potential of the region. The central-north of Entre Ríos has a humid temperature climate, Cf in the Koppen-Geiger classification, as revised by [24]. In this way, the Pampa Region (where the province of Entre Rios is located) receives sea winds throughout the year, with a moisture gradient decreasing from east to west [20].

The statistical trend detection in climatic variables and precipitation time series is one of the interesting research areas in climatology and hydrology as it impacts spatial and temporal distribution of water availability across the globe [25]. The parametric or non-parametric method under statistical approach is used to detect if either a data of a given set follows a distribution or has a trend on a fixed level of significance. Various non-parametric tests, including Mann-Kendall test and Pettit’s test, are widely used to detect trend and change point in historical series of climatic and hydrological variables [26, 27, 28]. To understand the magnitude of trends many techniques have been proposed in the past, including t-tests [29, 30], Mann–Whitney and Pettitt’s tests [31] and standard normal homogeneity test [32, 33].

The aim of this work was to evaluate the long-term changes in the intensity of rainfall in the central-north region of Entre Ríos between 1945 and 2019, based only on daily precipitation records aggregated at yearly, monthly and seasonal levels. In more specific terms, the quality of the rainfall series is first analyzed in terms of its homogeneity to assess the reliability of the meteorological information used. Secondly, the existence of a trend in the indicators of the intensity and variability of rainfall is evaluated during a period showing a generalized increase in atmospheric temperature. Finally, the occurrence of a breakpoint that expresses a long-term trend change in the annual rainfall series in the region is assessed.

Advertisement

2. Material and methods

2.1 Region of study and rainfall data

We used monthly rainfall data for the period 1945–2019 from 6 localities (Figure 1 and Table 1) in the southern of department La Paz (Province of Entre Rios, Argentina): Hasenkamp (HAS), Las Garzas (LGA), Alcaraz Norte (ALN), Bovril (BOV), Hernandarias (HER), El Solar (ELS). This data were collected with conventional rain gauges, from the official records of Hydraulic Directorate (Direccion Hidraulica de Entre Rios, in spanish) and Cereal Bag (Bolsa de Cereales de Entre Rios, in spanish) of the Province of Entre Rios.

Figure 1.

Location map of southern of department La Paz (province of Entre Rios, Argentina) with localities analyzed.

Meteorological stationLatitudeLongitudeAltitude (m a.s.l)Period and Entirety (%)
HasenkampHAS31°30′32.94”S59°50′9.37”W881945–2019 (93.8%)
Las GarzasLGA31°25′43.54”S59°44′36.09”W821945–2019 (100%)
Alcaraz NorteALN31°19′37.49”S59°45′15.88”W681945–2019 (98.3%)
BovrilBOV31°20′26.89”S59°26′30.97”W791945–2019 (94.5%)
HernandariasHER31°13′51.34”S59°59′10.35”W521945–2019 (96.5%)
El SolarELS31°10′32.96”S59°43′56.73”W501945–2019 (99.2%)

Table 1.

Meteorological station used and the period analyzed.

The data from the 6 locations was subjected to a process of quality control for possible errors. All data above the third quartile plus three times the interquartile range and located more than five standard deviations from the mean was treated as outliers. These outliers were then contrasted climatographically with readings from nearby stations. If the same reading was labeled as out of range for more than two seasons, the value was correct. Months classed as outliers and those without data were treated as gaps. Both types of gaps were filled but no missing data was completed if there were more than three gaps in one year.

Stations with missing data techniques linear regression were used. The filling of missing data by the linear regression technique consisted in using data from neighboring stations that presented coefficients of significant linear correlations with the station to be used in the study [34, 35],

Px=ao+i=1naiPiE1

where aoand aiare the coeffcients of adjustment of the linear model, obtained in the processing of correlation. In this case, stations that presented an R2 greater than 0.90 were included. The two techniques are widely used to fill gaps in historical series and present low average deviations suitable for climatic studies on monthly and seasonal scales [36].

After the treatment of the time series, the monthly values of all rainfall stations were grouped into scales, according to the following definitions: a) autumn (March, April, and May), b) winter (June, July, and August), c) spring (September, October, and November), and d) summer (December, January, and February). For selecting the change point for a particular parameter, the method presented below has been used [37]: a) no change point or homogeneous (HG), series may be considered as homogeneous, if no or one test out of four tests rejects the null hypothesis at 5% significant level; b) doubtful series (DF), series may be considered as inhomogeneous and critically evaluated before further analysis if two out of four tests reject the null hypothesis at 5% significant level; and c) change point or inhomogeneous (CP) when series may has change point or be inhomogeneous in nature, if more than two tests reject the null hypothesis at 5% significant level.

2.2 Homogeneity tests for change point detection

Homogeneity testing is very crucial in climatological studies to represent the real variations in weather and climate. Inhomogeneity occurs in climate data due to several reasons including instrumentation error, changes in the adjacent areas of the instrument, and mishandling of the human. If the homogeneity is not tested prior to trend analysis, the results will indicate erroneous trends. In this study, the absolute homogeneity tests were performed on individual station records and calculating the ratio of observed series to the reference series. Four widely used statistical tests mentioned below were applied to the data to test for homogeneity. All the following four tests used in this study assume the null hypothesis of data being homogeneous. The change point detection is an important aspect to assess the period from where significant change has occurred in a time series. Pettitt’s test, von Neumann ratio test, Buishand range test and standard normal homogeneity tests have been applied for change point detection in climatic series. The details of various change point tests applied in the study are presented here.

2.2.1 Pettitt’s test

The Pettitt’s test for change detection, developed by [38], is a non-parametric test, which is useful for evaluating the occurrence of abrupt changes in climatic records [39, 40] because its sensitivity. According to Pettitt’s test, if x1, x2, x3, … xnis a series of observed data which has a change point at tin such a way that x1, x2 …, xthas a distribution function F1(x) which is different from the distribution function F2(x) of the second part of the series xt+1, xt+2, xt+3 …, xn. The non-parametric test statistics Utfor this test may be described as follows:

Ut=i=1tj=t+1nsignxtxjE2
signxtxj=1,ifxixj>00,ifxixj=01,ifxixj<0E3

The test statistic Kand the associated confidence level (ρ) for the sample length (n) may be described as:

K=MaxUtE4
ρ=expKn2+n3E5

When ρis smaller than the specific confidence level, the null hypothesis is rejected. The approximate significance probability (p) for a change-point is defined as given below:

p=1ρE6

The test statistic Kcan also be compared with standard values at different confidence level for detection of change point in a series. The critical values of Kat 1 and 5% confidence levels for different tests used in the analysis has been presented in Table 2 [37].

Number of observationCritical values for test statistic at different significance level
Pettit TestSNHTBuishand Range testVon Neumann Ratio Test
1%5%1%5%1%5%1%5%
5029323511.388.451.781.551.361.54
7048839311.898.801.811.591.451.61
10084167712.329.151.861.621.541.67

Table 2.

Critical values of test statistics for different change point detections tests.

2.2.2 von Neumann ratio test

The von Neumann ratio test has been described by [41, 42] and others. The test statistics for change point detection in a series of observations x1, x2, x3xncan be described as:

N=i=1n1xixi12i=1n1xix¯2E7

According to this test, if the sample or series is homogeneous, then the expected value E(N) = 2 under the null hypothesis with constant mean. When the sample has a break, then the value of Nmust be lower than 2, otherwise we can imply that the sample has rapid variation in the mean. The critical values of Nat 1 and 5% confidence levels given in Table 2 can be used for identification of non-homogeneous series with change point.

2.2.3 Buishand’s range test

The adjusted partial sum (Sk), that is the cumulative deviation from mean for kth observation of a series x1, x2, x3.…xk.…xnwith mean (x¯) can be computed using following equation:

Sk=i=1kxix¯E8

A series may be homogeneous without any change point if Sk∼0, because in random series, the deviation from mean will be distributed on both sides of the mean of the series. The significance of shift can be evaluated by computing rescales adjusted range (R) using the following equation:

R=MaxSkMinSkx¯E9

The computed value of R/nis compared with critical values given by [37, 41] and has been used for detection of possible change (Table 2).

2.2.4 Standard normal homogeneity (SNH) test

The test statistic (Tk) is used to compare the mean of first nobservations with the mean of the remaining (n-k) observations with ndata points [32].

Tk=kZ12+nkZ22E10

Z1and Z2can be computed as:

Z1=1ki=1kxix¯σxE11
Z2=1nki=k+1kxix¯σxE12

where, x¯and σxare the mean and standard deviation of the series. The year kcan be considered as change point and consist a break where the value of Tkattains the maximum value. To reject the null hypothesis, the test statistic should be greater than the critical value, which depends on the sample size (n) given in Table 2.

2.3 Test for trend analysis

All the trend tests in this section assume the null hypothesis of no trend and the alternative hypothesis of monotonic increasing or decreasing trend existence. When the time series are serially independent, the Mann–Kendall test [43, 44] and Spearman’s Rho test [45, 46] were applied to test for trends. The magnitude of the trend was estimated using Sen’s slope method [47]. Always suggested to apply various statistical tests to analyze the trends in serially correlated data.

2.3.1 Mann-Kendall test

The Mann–Kendall test is a nonparametric test for monotonic trend detection. It does not assume the data to be normally distributed and is flexible to outliers in the data. The test assumes a null hypothesis, H0, of no trend and alternate hypothesis, Ha, of increasing or decreasing monotonic trend. For a time series Xi=x1,x2,,xn, the Mann–Kendall test statistic Sis calculated as

S=i=1n1j=i+1nsignxjxiE13

where nis the number of data points, xiand xjare the data values in timeseries iand j(j> i), respectively, and signxjxiis the sign function as

signxixj=1,ifxjxi>00,ifxjxi=01,ifxjxi<0E14

Statistics Sis normally distributed with parameters E(S) and variance V(S) as given below:

ES=0E15
VS=nn12n+5k=1mtkkk12k+518E16

where nis the number of data points, mis the number of tied groups, and tkdenotes the number of ties of extent k. Standardized test statistic Zis calculated using the formula below.

Z=S1varSifS>00ifS=0S+1varSifS<0E17

To test for a monotonic trend at an α significance level, the alternate hypothesis of trend is accepted if the absolute value of standardized test statistic Z is greater than the Z1α/2value obtained from the standard normal cumulative distribution Tables. A positive sign of the test statistic indicates an increasing trend and a negative sign indicates a decreasing trend.

2.3.2 Spearman’s rho test

The Spearman’s rho test is a non-parametric widely used for studying populations that take on a ranked order. If there is no trend and all observations are independent, then all rank orderings are equally likely. In this test, the difference between order and rank (di) for all observations x1,x2,x3,xncan be used to compute and Spearman’s ρ, variance Varρand test statistic Zusing following equations. The null hypothesis is tested in this test considering the statistic is normally distributed.

ρ=16di2nn1E18
Varρ=1n1E19
Z=ρVarρE20
Advertisement

3. Results and discussion

Tables 38 show the results of the statistical analyzes carried out to know the point of change in monthly, seasonal and annual rainfall in each locality. A marked variability was observed in the months that changed significantly between the localities, fundamentally from January to May, even though the proximity between them does not exceed 40 km. This means, a priori and in subjective terms, that the climatic changes reported worldwide have a direct influence on a microspatial scale, as well as on the temporal window. However, in the region there was no heterogeneity in the breaking point between the localities evaluated for the months of November and December during the study period analyzed.

PeriodStandard Normal Homogeneity TestPettitt’s testBuishand Range testvon Neumann’s test
TpsigkU*psigkR/sqrt(n)psigkStatisticpsig
JAN13.940.003**20182410.885ns19581.0470.591ns19581.3740.170ns
FEB7.550.104ns19765040.057**19761.5130.082***1976−0.3730.795ns
MAR4.440.427ns19492181.026ns19900.7130.969ns20070.7040.482ns
APR5.230.309ns19785000.060**19781.2330.319ns1978−0.4570.648ns
MAY15.320.002**20094480.120ns19801.5650.061***19800.3110.756ns
JUN2.420.843ns20063160.493ns19860.8440.871ns1974−1.0980.272ns
JUL1.590.968ns19682380.903ns19870.8760.833ns1968−0.5890.556ns
AUG11.010.018**20141981.154ns20140.8730.845ns20141.2080.227ns
SEP1.770.949ns19882570.792ns19881.0360.608ns1985−0.0390.969ns
OCT3.490.613ns19555260.041**19820.9530.737ns1983−1.4140.157ns
NOV13.170.005**19767400.001**19761.9640.003**1976−1.9930.046**
DEC8.730.056*19884940.065**19861.4550.116ns1988−0.1160.908ns
Summer10.780.020*19765640.023**19761.6240.044*1976−0.5920.554ns
Autumn6.370.186ns19974650.096ns19741.3480.196ns19741.5190.129ns
Winter2.740.775ns20161721.320ns19860.8960.814ns1986−0.2190.826ns
Spring4.400.438ns19775200.045**19771.1060.498ns1977−0.4040.686ns
Annual12.920.006**19776680.004**19761.7840.016*1977−0.9590.338ns

Table 3.

Results of change point analysis with all test used in Las Garzas location.

References: k: year to shift, sig: * 0.05%, ** 0.01%, *** 0,1%, ns: no signification.

PeriodStandard Normal Homogeneity TestPettitt’s testBuishand Range testvon Neumann’s test
TpsigkU*psigkR/sqrt(n)psigkStatisticpsig
JAN16.420.000**20181921.192ns19591.0090.646ns19941.1210.262ns
FEB5.420.282ns20064160.176ns20021.2080.349ns19800.7090.478ns
MAR4.110.487ns20192211.008ns19930.6330.993ns20072.0810.037*
APR5.070.333ns20155240.042**19780.9650.713ns1978−0.5260.599ns
MAY10.370.002**20174600.103ns19731.4520.119ns19820.8490.396ns
JUN3.110.695ns20063200.475ns19861.0590.572ns1975−1.3200.187ns
JUL1.740.949ns19483570.334ns19880.9030.805ns1987−0.5680.570ns
AUG10.540.020**20141721.320ns20140.8680.847ns20140.6820.495ns
SEP2.080.901ns19882670.730ns20061.0360.610ns19880.3650.715ns
OCT2.850.754ns20103310.430ns19991.2730.269ns2000−0.1350.892ns
NOV14.390.003**19767180.001**19762.0980.001**1976−1.9830.047*
DEC7.650.101ns19764530.112ns19761.3680.179ns1976−1.8020.072***
Summer8.850.532*20045180.046**19761.4640.110ns1976−0.3570.721ns
Autumn4.730.386ns19793910.234ns19791.1430.442ns19791.8330.067***
Winter2.920.732ns19482001.141ns19550.8800.831ns1955−1.3390.181ns
Spring6.930.143ns20104000.212ns19771.0490.591ns1999−0.2730.785ns
Annual10.050.003**19775820.017**19771.5740.057*1977−0.3730.709ns

Table 4.

Results of change point analysis with all test used in Alcaraz Norte location.

References: k: year to shift, sig: * 0.05%, ** 0.01%, *** 0,1%, ns: no signification.

PeriodStandard Normal Homogeneity TestPettitt’s testBuishand Range testvon Neumann’s test
TpsigkU*psigkR/sqrt(n)psigkStatisticpsig
JAN11.300.015**20161991.147ns19580.9580.721ns19582.0390.041**
FEB7.170.125ns20094770.082***19991.2910.248ns1980−0.5770.564ns
MAR3.800.547ns19491991.147ns19800.7210.968ns19492.2460.025*
APR3.560.595ns19984420.128**19781.1380.447ns1978−1.1010.271ns
MAY10.850.019**20173920.231ns19791.1950.366ns19790.0610.952ns
JUN2.660.792ns20062530.815ns20060.9590.723ns1973−1.3210.186ns
JUL4.190.468ns19784000.212ns19871.2650.283ns1978−0.3390.735ns
AUG12.680.006**20142480.844ns19820.9270.765ns20131.7560.079***
SEP2.270.868ns19563580.331ns19851.1920.366ns1985−0.9260.354ns
OCT3.190.678ns19894470.121ns19831.2530.291ns1989−1.2700.204ns
NOV9.080.051**19925860.015**19921.5920.051**1977−1.7450.081***
DEC8.090.080***19894960.063***19891.3940.155ns1989−0.7650.444ns
Summer7.580.103*20085160.048**19951.2360.319ns1989−0.2480.808ns
Autumn4.040.510ns19974520.114ns19891.0400.596ns19890.2750.784ns
Winter3.080.711ns19682340.927ns19681.2500.302ns1970−0.2590.796ns
Spring5.610.258ns19994800.079***19921.3050.232ns1922−1.2770.202ns
Annual10.240.026**19995980.013**19971.4530.118ns1997−0.7660.443ns

Table 5.

Results of change point analysis with all test used in Bovril location.

References: k: year to shift, sig: * 0.05%, ** 0.01%, *** 0,1%, ns: no signification.

PeriodStandard Normal Homogeneity TestPettitt’s testBuishand Range testvon Neumann’s test
TpsigkU*psigkR/sqrt(n)psigkStatisticpsig
JAN11.120.015**20182540.809ns19611.0850.532ns19581.5010.133ns
FEB6.490.176ns19764660.095ns19761.4220.138ns1976−0.2910.771ns
MAR3.770.559ns20142730.703ns19800.8460.875ns20071.0470.295ns
APR5.160.319ns19804810.078***19781.3020.237ns1980−0.1120.911ns
MAY14.090.003**20123180.484ns19731.1540.421ns20090.1800.858ns
JUN2.550.815ns20064160.176ns19820.9390.750ns1982−0.3780.705ns
JUL1.340.985ns19581951.173ns19880.9840.692ns19680.1600.873ns
AUG9.860.037*20141771.288ns20140.8410.877ns20141.4100.159ns
SEP1.680.955ns19852201.014ns19880.9450.746ns19850.1900.849ns
OCT3.900.537ns19885040.057***19881.0230.623ns1988−1.5500.121ns
NOV8.980.049*19755810.018**19751.7100.024*1976−1.8420.065*
DEC7.580.102ns20014030.205ns19861.3530.197ns19880.2810.779ns
Summer7.740.098***20044920.067*19951.2520.295ns19800.3020.763ns
Autumn5.240.309ns19694350.141ns19691.1620.412ns19690.9430.346ns
Winter2.210.880ns20161771.289ns19860.9010.808ns1986−0.1930.847ns
Spring4.180.480ns19554680.092***19881.0010.664ns1976−0.8740.382ns
Annual12.830.006**19976520.005**19971.6350.041*1997−1.0630.288ns

Table 6.

Results of change point analysis with all test used in Hasenkamp location.

References: k: year to shift, sig: * 0.05%, ** 0.01%, *** 0,1%, ns: no signification.

PeriodStandard Normal Homogeneity TestPettitt’s testBuishand Range testvon Neumann’s test
TpsigkU*psigkR/sqrt(n)psigkStatisticpsig
JAN19.160.000**20182141.052ns20061.1480.440ns20060.6430.520ns
FEB9.300.044*20094010.209ns20041.2000.368ns2004−0.1870.852ns
MAR3.850.539ns19491901.205ns19490.8510.866ns20070.6880.491ns
APR8.290.071***19806100.011**19781.5010.087***1980−0.8320.405ns
MAY12.210.010**20174180.172ns19731.2790.264ns19730.8950.371ns
JUN2.490.827ns20062550.803ns20061.0360.612ns2006−1.6260.104ns
JUL2.890.738ns20022910.609ns19871.0450.596ns2002−0.2340.815ns
AUG13.440.004**20142340.927ns19841.0960.509ns19990.5610.575ns
SEP2.360.853ns19862950.590ns19881.0970.509ns1986−1.2320.218ns
OCT3.980.516ns20004220.164ns19821.1790.387ns2000−0.7190.472ns
NOV8.750.059*19855600.025*19851.6570.034*1985−1.4150.157ns
DEC15.810.001**19896890.003**19891.9480.004**1989−1.2640.206ns
Summer15.530.001**20045280.040*19951.6360.042*2004−0.6840.494ns
Autumn8.140.081***19795680.022*19791.4530.114ns19790.1380.890ns
Winter2.650.794ns19972490.838ns19970.9770.699ns1997−0.8410.400ns
Spring5.890.232ns19994810.078ns19821.1980.356ns19830.2040.839ns
Annual18.450.000**19997670.001**19821.9760.002**1982−2.0870.037*

Table 7.

Results of change point analysis with all test used in El solar location.

References: k: year to shift, sig: * 0.05%, ** 0.01%, *** 0,1%, ns: no signification.

PeriodStandard Normal Homogeneity TestPettitt’s testBuishand Range testvon Neumann’s test
TpsigkU*psigkR/sqrt(n)psigkStatisticpsig
JAN23.280.001**20181861.231ns19851.0360.604ns20180.5770.564ns
FEB7.110.129ns20093260.450ns20041.1010.506ns2006−0.2530.800ns
MAR4.030.505ns19491981.154ns19490.9430.739ns19491.0600.289ns
APR8.360.071***19786140.010**19781.4740.103ns1978−1.7440.081***
MAY13.670.004**20174880.071***19731.4210.136ns1980−0.2740.784ns
JUN2.750.773ns20063410.391ns19860.9140.776ns1974−1.5710.116ns
JUL2.180.086***19483310.430ns19881.0240.624ns1988−0.4250.671ns
AUG9.980.028*20142410.885ns19660.9000.804ns19660.4470.655ns
SEP1.240.989ns19883070.533ns19990.9820.683ns1988−1.1910.234ns
OCT4.580.407ns19835270.041*19821.3430.201ns1983−1.7000.089***
NOV13.090.004**19777120.002**19772.0010.002**1977−2.8780.004**
DEC10.000.030*19965250.042*19891.5420.068***1989−0.5500.583ns
Summer10.850.018*20184080.193ns19951.1930.377ns19750.2120.832ns
Autumn9.160.048*19856290.008**19851.5350.073***1985−0.3570.721ns
Winter2.610.803ns19482191.020ns19920.8700.842ns19920.0090.993ns
Spring7.360.117ns19995620.024*19771.3840.165ns1977−1.4590.145ns
Annual13.770.004**19996360.007**19891.6990.024*1977−1.4760.140ns

Table 8.

Results of change point analysis with all test used in Hernandarias location.

References: k: year to shift, sig: * 0.05%, ** 0.01%, *** 0,1%, ns: no signification.

In relation to the statistical tests used, it is possible to conclude that the Von Neumman’s test is more robust when establishing the heterogeneity of the time series, while the Standard Normal Homogeneity test a priori would require less demand from the variability of the time series. to set a breaking point. Based on the results of the SNH Test, it is observed that the month of May presents marked heterogeneity in all localities, but the year that defines the point of change differs significantly. When comparing and analyzing all the tests for each period of time, only Las Garzas and Hernandarias present a significant, but doubtful point of change in the year that followed.

In seasonal analysis, summer is the season of the year that presented marked heterogeneity in the time series in all localities. The year of break point was different by location. However, El Solar and Hernandarias presented significant modifications in the heterogeneity of the time series with breaking points during the 1970s and 1980s, respectively. Both locations are adjacent to the Middle Paraná River, a situation that could be influenced by local atmospheric conditions [48]. There is even greater concern today about the future of rivers worldwide due to a multitude of stressors that impact running waters including climate change [49]. We draw on the growing literature related to climate change to illustrate potential impacts rivers may experience and management options for protecting riverine ecosystems and the goods and services they provide. Regional patterns in precipitation and temperature are predicted to change and these changes have the potential to alter natural flow regimes. One of the key ways in which climate change or other stressors affect river ecosystems is by causing changes in river flow. Rivers vary geographically with respect to their natural flow regime and this variation is critical to the ecological integrity and health of streams and rivers and thus a great deal has been written on the topic [50, 51]. The ecological consequences and the required management responses for any given river will depend not only on the direct impacts of increased temperature. Otherwise how extensively the magnitude, frequency, timing, and duration of runoff events change relative to the historical and recent flow regime for that river, and how adaptable the aquatic and riparian species are to different degrees of alteration.

The results resume depicting the homogeneity state of different series have been presented in Table 9 (See Supplementary Appendix with results of Test’s trend). The change point analysis on long-term series in all localities has indicated that a significant change point in the annual rainfall. The breaking point occurred in 1977 for the LGA, ALC and HER locations; year 1997 for BOV and HAS; and 1982 for the ELS locality. Figure 2 shows the average annual precipitation of all the localities evaluated in each year for the region, as well as the historical annual during the period. On the other hand, since the breaking point occurred in 1977 for most of the localities, it was established that the average annual rainfall in the region prior to that date was 946 mm, while after the same 1150 mm, equivalent to 21.5% higher than the 1945–1977 average and 8.5% higher according to the historical average 1945–2019. In addition, an important piece of information results from the linear model that made it possible to establish that the region’s average rainfall increased 4.9 mm per year from 1945 to 2019.

PeriodLGAALNBOVHASELSHER
abcabcabcabcabcabc
JANHGHGDF2016HGHGHG
FEBDF1976HGHGHGHGHG
MARHGHGHGHGHGHG
APRHG¿?HGHGHGCP1978
1980
CP1978
MAYDF1980 2009HGHGHGHGDF1973
2017
JUNHGHGHGHGHGHG
JULHGHGHGHGHGHG
AUGHGHGDF2014HGHGHG
SEPHGHGHGHGHGHG
OCTHGHGHGHGHGDF1982
NOVCP1976CP1976CP1992
1997
CP1975
1976
CP1985CP1977
DECDF1986 1988HGDF1989HGCP1989CP1989 1996
SummerCP1976DF2004 1976DF1995 2008DF1995 2004CP1995 2004HG
AutumnHGHGHG¿?HGDF1979CP1985
WinterHGHGHGHGHGHG
SpringHGHGHG¿?HGHGHG
AnnualCP1977CP1977DF1997
1999
CP1997CP1982CP1977 1989 1999

Table 9.

Results of change point detection analysis and trends of rainfall for all localities.

Reference: homogeneous series (HG), change point (CP), doubtful point (DF). Trends: ∼ none, ↑ increase, ↓ decrease, ¿? Doubtful.

Reference: a- Nature Serie, b- Year shift, c- Trend, LGA- Las Garzas, ALN- Alcaraz Norte, BOV- Bovril, HAS- Hasenkamp ELS- El Solar, HER- Hernandarias.

Figure 2.

Variation in the average annual rainfall of all the localities of the analyzed region.

These results are consistent with those obtained in the north of the country where the rainfall change was concentrated in a step change during the 1970s [52]. In this region, half or more of the annual rainfall trend occurred in the months of El Niño phase, with less contribution from La Niña and the neutral phases. However, in the rest of subtropical Argentina and especially south of 30°S, increased precipitation occurred mostly during months of the neutral phase of El Niño/Southern Oscillation (ENSO), with only small trends during months of El Niño and La Niña phases [53]. Accordingly, most of the annual precipitation trends since 1960 in subtropical Argentina can be accounted for by two modes. The first mode, which is positively correlated with precipitation in northern Argentina and with ENSO indices, had a steep increase in precipitation at the end of the 1970s. The second mode, which has a maximum positive correlation with annual precipitation between 30 and 40°S, had a regular positive trend starting in the early 1960s and it is correlated with the southward displacement of the South Atlantic high [53, 54]. In addition, several researchers analyzed the changes in the isohyets, showing that the rainfall regime in Argentina is subject to a positive fluctuation in the 1950s and that it reached maximum values in the 1970s [55], data that coincide with this manuscript.

Average rainfall increased, favoring the expansion of agriculture [16, 22]. This conclusion is obtained primarily because the studies of the time have been hampered by the low significance shown by statistical tests when applied to climatic data, especially precipitation. In the study region mention that one of the factors of change in precipitation is agrarian transformation and claim that the technological innovation of the sector was accompanied by a process of change in the water regime [16]. Furthermore, confirm that the expansion of agricultural structure of Entre Rios, is favored by increased precipitation, generating crops of the marginal territory.

The behavior of historical series of monthly rainfall confirm that November and December, as and summer season, have significant change point in all localities. The annual rainfall in all localities showed a significant increase such as summer season (Table 9). November and December showed and significant rise in contrast to the rest of months.

In the last decade, a substantial change in the average climate conditions was observed in many regions of Argentina, particularly in the southern region of Mesopotamian Pampa that showed two abrupt shifts [20]. The first of these was positive, with annual average rainfall increasing from 1062.9 mm during the 1941–1999 sub-period to 1568.9 mm during a short sub-period between 2000 and 2003. The second abrupt change, which began in 2004, was negative, with average annual rainfall dropping to 1108.0 mm, only slightly higher than what it had been in the initial 1941–1999 sub-period (Figure 3).

Figure 3.

Variation in the average annual rainfall in each locality of the analyzed region. Reference: Black dash line (−−) show historical rainfall (1945–2019), black solid line (—) the average rainfall before and after the break point and gray dash line (−−) show a linear model annual rainfall.

Like the regional results, this study observed a sustained increase in monthly rainfall to the breaking point in the 1970s, but then the annual rate of increase was even higher. In South America [56], observed increasing trends in total annual precipitation values in Ecuador, Paraguay, Uruguay, northern Peru, southern Brazil, and northern and central Argentina. Qualitatively there was a change that indicated a significant increase in summer precipitation, and a decrease in the number of annual frosts, concentrating the winter season (July and August), assuming a “tropicalization of the region”. Rainfall tropicalization can be understood as local and regional processes and impacts of climate change, which can be observed mainly by changes in the precipitation regime and the intensification of tropical climatic characteristics [57]. This process is not exclusive of Espinal Ecorregion. It has been observed in other contexts and scales in tropical and subtropical regions that show an important increase in precipitation during the rainy season in tropical regions [58, 59].

Climate change can also indirectly affect organisms by altering biotic interactions, which can have profound consequences for populations, community composition and ecosystem functions [60]. Other aspects of biodiversity management will be affected by global change and will need adapting, including wildlife exploitation, e.g. forestry [61], pest and invasive species control [62] or human and wildlife disease management [63]. Indirect effects may occur: (i) via generation of new biotic interactions, as range-shifted species appear for the first time in naive communities [64]; (ii) by removing existing interactions when species shift out of their existing range [65]; or (iii) by modulating key behavioral, physiological or other traits that mediate species interactions [66]. When climate-driven changes in biotic interactions involve keystone or foundation species, impacts can cascade through the associated community [61]. In this region, studies that have not yet been published for the province of Entre Ríos are showing indications of changes in the productivity of natural grasslands in native forests. Recently reports show that change the growth cycle has change in this ecosystem [67, 68], and mainly attributed to changes in precipitation regimes. These observations are like yields changes of the main crops, were the frequency of extreme weather events constitutes a growing risk.

Advertisement

Acknowledgments

This study was carried out in the framework of research and development projects UNER-PID No. 2196 “Ecological succession of a native forest intervened in the Spinal Ecorregion” and UNER-PID No. 2238 “Evaluation of the current and potential state of the native forests of Entre Rios in its productive and conservation aspect”.

Advertisement

Conflict of interest

The authors declare no conflict of interest.

Advertisement

PeriodAverage rainfallSpearman’s Rank Rho TestMann-Kendall Test
Srhopz-valueSen’s slopeSptau
JAN12170352−0.0010.9950.0410.0081.0000.9670.004
FEB131504190.2830.014*2.4701.000541.0000.014*0.195
MAR14276056−0.0810.485−0.686−0.245−0.0150.493−0.054
APR110541320.2300.047*1.8800.7610.0410.0600.148
MAY68512350.2710.019*2.3600.5760.0570.018*0.186
JUN4976397−0.0870.459−0.778−0.117−0.0170.436−0.062
JUL3572477−0.0310.792−0.357−0.024−0.7900.721−0.028
AUG42663690.0560.6340.3570.0500.7900.7210.028
SEP6973918−0.0510.661−0.439−0.108−0.9700.661−0.349
OCT108525790.2520.029*2.1400.8000.0470.033*0.169
NOV109461050.3440.002*2.9460.8000.0650.032*0.232
DEC106504390.2830.014*2.4020.8910.5260.016*0.190
Summer358487430.3070.007*2.6572.2850.0580.008*0.209
Autumn320515930.2660.021*2.2601.7300.0500.024*0.178
Winter126695140.0110.9240.0690.0381.6000.9450.006
Spring286500590.2880.012*2.4521.3550.0540.014*0.193
Annual1091406380.4220.000*3.4495.1330.0760.001*0.272

Table 1A.

Result of trend analysis rainfall at Las Garzas locality.

References: (*) test with significant differences of 0.05%.

PeriodAverage rainfallSpearman’s Rank Rho TestMann-Kendall Test
Srhopz-valueSen’s slopeSptau
JAN112687750.0220.8530.2970.1540.6600.7660.024
FEB119562230.2000.0851.7290.6840.0380.0840.137
MAR13977544−0.1030.379−0.883−0.391−0.0190.377−0.699
APR102549870.2180.0601.8660.650409.0000.0620.148
MAY61508320.2770.016*2.3840.557522.0000.017*0.189
JUN4178357−0.1150.328−0.915−0.120−0.0200.360−0.073
JUL3676383−0.0870.460−0.679−0.056−0.0150.497−0.054
AUG45659500.0140.5980.5450.0820.0120.5860.044
SEP63693200.1030.9060.0870.0070.2000.9310.007
OCT101630280.1030.3770.8830.3130.0190.3770.071
NOV108480560.3160.006*2.7130.9310.0590.007*0.214
DEC99520130.2600.024*2.0930.8330.0460.036*0.166
Summer330510740.2730.018*2.2232.0080.0490.026*0.175
Autumn302561650.2010.0841.6701.2160.0370.0950.132
Winter123692880.0140.902−0.091−0.040−0.2100.927−0.008
Spring273551830.2150.0641.7151.1070.0380.0860.136
Annual1029453570.3550.002*2.8874.322632.0000.004*0.228

Table 2A.

Result of trend analysis rainfall at Alcaraz Norte locality.

PeriodAverage rainfallSpearman’s Rank Rho TestMann-Kendall Test
Srhopz-valueSen’s slopeSptau
JAN113696500.0090.9370.1280.0430.2900.8980.010
FEB119519620.2610.024*2.3470.9200.0510.019*0.186
MAR11775068−0.0680.563−0.677−0.284−0.0150.498−0.054
APR136583500.1700.1451.5330.7290.0340.1250.121
MAY61581230.1730.1371.4910.3500.0330.1360.118
JUN4376302−0.0850.466−0.750−0.118−0.0170.453−0.060
JUL4482282−0.1700.144−1.611−0.263−3530.107−0.128
AUG50617180.1220.2971.1310.1840.0250.2580.090
SEP7077711−0.1050.368−0.860−0.226−0.0190.390−0.068
OCT114561160.2020.0831.7520.6550.0380.0800.139
NOV105499860.2890.012*2.2650.8040.0500.024*0.179
DEC107534770.2390.039*2.0910.9230.0460.037*0.165
Summer339513980.2690.020*2.3062.0540.0510.021*0.182
Autumn314549020.2190.059*1.8761.4310.0410.0610.141
Winter14874374−0.0580.621−0.572−0.204−0.0130.568−0.045
Spring289550440.2170.061*1.7151.2150.0380.0860.136
Annual1090491950.3000.009*2.4384.4510.0530.015*0.192

Table 3A.

Result of trend analysis rainfall at Bovril locality.

PeriodAverage rainfallSpearman’s Rank Rho TestMann-Kendall Test
Srhopz-valueSen’s slopeSptau
JAN11471802−0.0210.856−0.156−0.010−0.3500.876−0.013
FEB129512020.2720.018*2.4241.0290.0530.015*0.191
MAR14177861−0.1080.258−0.910−0.380−0.0200.363−0.072
APR114553610.2120.0671.7840.7940.0390.0740.141
MAY72589260.1620.1661.4090.4310.0310.1590.116
JUN4382218−0.1690.146−1.281−0.184−2810.200−0.102
JUL3371809−0.0210.856−0.188−0.013−0.4200.851−1.523
AUG41672340.0440.7100.3340.0480.7400.7380.027
SEP6573176−0.0410.727−0.371−0.098−0.8200.711−0.030
OCT113527310.2500.031*2.1000.8220.0460.036*0.166
NOV110503200.2840.013*2.4470.8400.0540.014*0.192
DEC97547420.2210.056*1.8750.7240.0410.061*0.148
Summer340504820.2820.014*2.5022.0320.0580.012*0.198
Autumn327547750.2210.059*1.8441.2790.0400.063*0.146
Winter11771460−0.0170.883−0.238−0.075−0.5300.812−0.019
Spring287511150.2730.018*2.2501.2720.0490.024*0.178
Annual1071411840.4140.000*3.5314.6250.0770.000*0.279

Table 4A.

Result of trend analysis rainfall at Hasenkamp locality.

PeriodAverage rainfallSpearman’s Rank Rho TestMann-Kendall Test
Srhopz-valueSen’s slopeSptau
JAN119668040.0500.6720.4350.2070.9600.6640.035
FEB118584920.1680.1501.5650.5480.0340.1180.124
MAR12872232−0.0240.815−0.165−0.083−0.3700.8690.013
APR107521210.2590.025*2.3420.9290.0510.019*0.185
MAY56539170.2330.044*1.9820.4620.0430.048*0.158
JUN5274096−0.0540.645−0.224−0.030−10.823−0.018
JUL3577632−0.1040.373−0.865−0.103−0.0190.387−0.069
AUG39631080.1020.3820.9480.1460.0210.3430.076
SEP5871122−0.0120.921−0.160−0.026−0.3600.873−0.013
OCT105593350.1560.1821.3220.4600.0290.1860.105
NOV98526460.2510.030*2.3190.7600.0510.020*0.184
DEC95437040.3780.001*3.2081.1050.0700.001*0.253
Summer332502880.2840.013*2.5752.3440.0560.010*0.203
Autumn291508520.2770.017*2.2781.5410.0500.023*0.180
Winter126662500.0580.6240.5260.2500.0120.5990.042
Spring260541600.2300.048*1.9721.1950.0430.049*0.156
Annual1009389600.4460.000*3.9256.1260.0860.000*0.309

Table 5A.

Result of trend analysis rainfall at El solar locality.

PeriodAverage rainfallSpearman’s Rank Rho TestMann-Kendall Test
Srhopz-valueSen’s slopeSptau
JAN125607020.0090.9420.1020.0500.2400.9160.009
FEB130618840.1200.3061.2400.5470.0270.2150.098
MAR13572915−0.0370.751−0.421−0.179−93.0000.674−0.034
APR112505040.2820.014*2.3140.9570.0510.031*0.183
MAY60493930.2970.010*2.5710.593563.0000.010*0.203
JUN4779615−0.1330.257−1.075−0.125−0.0240.282−0.085
JUL3473537−0.0460.695−0.412−0.041−0.9100.680−0.033
AUG42658970.0630.5940.4900.0660.0110.6240.039
SEP62655970.0670.5690.5900.1430.0130.5550.047
OCT104563270.1990.087*1.7660.6080.0390.077*0.140
NOV114459540.3460.002*2.9781.0670.0650.003*0.235
DEC108500900.2870.012*2.2800.9390.0500.023*0.181
Summer363540280.2310.046*1.9851.8880.0440.047*0.157
Autumn307471580.3290.004*2.7631.8050.0610.006*0.219
Winter12370554−0.0040.9760.0090.0003.0000.9930.001
Spring280482970.3130.006*2.7221.700596.0000.006*0.215
Annual1073406940.4210.000*3.7786.420827.0000.000*0.298

Table 6A.

Result of trend analysis rainfall at Hernandarias locality.

DOWNLOAD FOR FREE

chapter PDF

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite and reference

Link to this chapter Copy to clipboard

Cite this chapter Copy to clipboard

Julian Alberto Sabattini and Rafael Alberto Sabattini (July 28th 2021). Rainfall Trends in Humid Temperate Climate in South America: Possible Effects in Ecosystems of Espinal Ecoregion [Online First], IntechOpen, DOI: 10.5772/intechopen.99080. Available from:

chapter statistics

24total chapter downloads

More statistics for editors and authors

Login to your personal dashboard for more detailed statistics on your publications.

Access personal reporting

We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. We share our knowledge and peer-reveiwed research papers with libraries, scientific and engineering societies, and also work with corporate R&D departments and government entities.

More About Us