Rainfall Erosivity over Brazil: A Large National Database

Abstract

Rainfall erosivity (RE) represents the potential of rainfall to cause soil erosion, and understanding its impact is essential for the adoption of soil and water conservation practices. Although several studies have estimated RE for Brazil, currently, no single reliable and easily accessible database exists for the country. To fill this gap, this work aimed to review the research and generate a rainfall erosivity database for Brazil. Data were gathered from studies that determined rainfall erosivity from observed rainfall records and synthetic rainfall series. Monthly and annual rainfall erosivity values were organized on a spreadsheet and in the shapefile format. In total, 54 studies from 1990 to 2023 were analyzed, resulting in the compilation of 5516 erosivity values for Brazil, of which 6.3% were pluviographic, and 93.7% were synthetic. The regions with the highest availability of information were the Northeast (35.6%), Southeast (30.1%), South (19.9%), Central-West (7.7%), and North (6.7%). The database, which can be accessed on the Mendeley Data platform, can aid professionals and researchers in adopting public policies and carrying out studies aimed at environmental conservation and management basin development.

Dataset: https://data.mendeley.com/datasets/gkzyh8mwj9/1

Dataset License: CC BY 4.0

1. Introduction

Soil is a vital resource for food production and is particularly vulnerable to erosion caused by human activities such as intensive farming [1], urbanization [2], and deforestation [3]. Erosion caused by rainwater hinders the physical integrity and fertility of the soil [4], adds to problems such as loss of agricultural productivity [5], reduces water quality [6], and also increases the silting of water bodies and consequently, flooding [7].

The adequate estimation of water erosion in response to changes in land use and management and/or climate change is essential for environmental planning and the management of watersheds as a way to reduce the impact of these forces [8]. Among the tools for estimating erosion, the Universal Soil Loss Equation (USLE) and its revised version, RUSLE, stand out for their simplicity, efficiency [9], and in some countries, the availability of input data in regards to both quantity and quality [10].

In the USLE/RUSLE, erosion is estimated considering several factors, including the potential of rainfall to cause soil loss, called rainfall erosivity (RE) [11]. RE plays a key role in soil and water conservation planning. It is also essential for detecting the effects of climate on erosion over time [12,13] and for identifying areas with high erosion potential [5,14]. Thus, the availability of erosivity data is essential for several fields of study such as hydrology, climate projections, and territorial planning, being used for identifying erosion-prone regions and aiding in soil and water conservation studies [15].

RE is determined individually for each location based on accurate and continuous rainfall records over time [11]. In Brazil, however, there is a shortage of studies on erosivity data, and the few that do exist, in general, are either obsolete or incomplete because they deal with erosivity on a local or regional, rather than a national, scale [16]. Hence, gathering updated data and making this information available is required to fill this gap. In this sense, this study aimed to review the research and generate an RE database for Brazil to facilitate the easy access and application of this information.

2. Material and Methods

2.1. Data Description

This study compiled research conducted in several regions of Brazil that determined the RE for one or more meteorological stations (pluviographic stations). It considered studies that used pluviograms recorded on a sub-daily scale (PD) and studies that used synthetic series generated by weather generators (SS) of rainfall on the same time scale. The inclusion of studies regarding SS is due to the previous validation of this product to estimate the rainfall erosivity index in Brazil [17], with excellent agreement between RE calculated using PD and SS. More information about the usage of SS to calculate RE can be found in Ref. [18].

This study did not include studies that relied solely on monthly rainfall data to calculate RE. They are often used inappropriately in empirical equations that are not relevant to the region of application, as demonstrated in some studies for Brazil [17,18,19]. Empirical equations are location-dependent, which makes them unsuitable for larger areas in most situations; also, their inability to identify high rainfall intensities significantly impacts average erosivity [17].

2.2. The Method

Using the most recent review of studies on erosivity published in Brazil [16], the RE database expansion was initiated by searching other studies on the Web of Science, Science Direct, Scopus, SciELO, and Google Scholar platforms, as well as theses and dissertations not published in journals. The keywords “rainfall erosivity” and “Brazil” were used to search for articles, as well as their Portuguese translations (“erosividade da chuva” and “Brasil”). There was no filter for publication time. Therefore, all studies on erosivity data for Brazil were initially considered. For each study found, information was collected such as station name, station code, operating agency, municipality, state, region, altitude, latitude, longitude, length of data, measurement period, method used (PD or SS), kinetic energy equation used, average monthly and annual rainfall erosivity, year of publication, publication title, authors, journal, DOI, and language.

The compilation, processing, and descriptive analysis of the data were performed using a spreadsheet. Subsequently, the compiled data were converted into vector layers of points to be used in any geographic information system software.

Average annual RE was classified into five classes to present the results, as shown in Table 1 [19]. The political regional divisions of Brazil, which are North, Northeast, Central-West, Southeast, and South, were used for a better descriptive analysis of the results.

3. Results and Discussion

A total of 54 studies from 1990 to 2023 were gathered. A total of 53 of these calculated rainfall erosivity using PD. Only one study, the most recent and comprehensive, used SS to estimate RE values [18] using the same stations employed in previous studies that also utilized SS in Brazil [17,20,21,22,23]; however, this information was not included in the results to avoid duplication of data. Of the 5516 RE values obtained in Brazil (Figure 1), 6.3% (350 values) come from PD, while 93.7% (5166 values) come from SS (Figure 2a).

Previous studies that gathered information on RE through PD in Brazil [15,19,24] did not include a thorough data inventory, often lacking information such as the method for obtaining data and the period covered by the time series, and did not always provide easy access to the compiled information. Furthermore, compared to the present study, they covered a smaller number of locations (between 80 and 225) and mostly presented only the annual average RE values. For the RE values obtained through PD, the current study represents an increase of more than 50% for the availability of this type of information.

Regarding the regions (Figure 2b), the Northeast contains 35.6% of the RE values (1961 values), followed by the Southeast (30.1%), the South (19.9%), Central-West (7.7%), and North (6.7%). This distribution is largely made up of SS data, which are most often available in the Northeast (37.3% of SS information). However, when considering only RE from PD, the Southeast region supplies 48.6% of the information, followed by the South (26.9%), Northeast (10.0%), North (7.4%), and Central-West (7.1%) regions. The data density, expressed in km² per station, is approximately 10,351 for the North region, 526 for the South, 557 for the Southeast, 792 for the Northeast, and 3762 for the Central-West. The Brazilian average is 1543 km² station⁻¹.

The scarcity of studies in the North and Central-West regions may be a result of the small number of meteorological stations in these areas, showing the need for maintenance and densification of these monitoring networks in these regions. This has also motivated the search for alternatives to estimate rainfall erosivity, such as the use of remote sensing data [25,26] or spatial interpolation techniques [24,27,28]. Another approach most commonly used to obtain rainfall erosivity is the use of regression models established from rainfall data [29,30,31]. However, as previously mentioned, it is common for these models to be used inappropriately.

A total of 59.3% (32 studies) of the studies calculated monthly and annual erosivity (EDMA), while 40.7% (22 studies) calculated only annual erosivity (EDA) (Figure 2c). At the municipality level, the presence of erosivity data was identified in 2805 municipalities, which corresponds to approximately 50% of the municipalities in Brazil. Of these municipalities, 94.4% provide monthly erosivity information (MDA), while 5.6% (156 municipalities) offer only annual data (MDMA) (Figure 2d). Rio de Janeiro, in the Southeast region of the country, stands out as the city with the largest amount of erosivity data, with 22 annual erosivity records.

Regarding the erosivity classification shown in Table 1, Figure 2e shows that overall, 26.9% of the annual erosivity data are categorized as indicating low erosivity, 8.7% as medium, 35.2% as medium-strong, 17.6% as strong, and 11.7% as very strong. It can also be inferred that most of the stations with low erosivity data are concentrated in the Northeast, characterized mostly by a semiarid climate, while medium erosivity predominates in the Southeast region. On the other hand, strong and very strong erosivity data are distributed in the North, Central-West, and South regions of the country (Figure 1).

Regarding the length of the series used, all RE values from SS (93.7% of the data) covered 100 years. Among the 350 RE values determined by PD (Figure 2f), 42.3% of these do not have information for the data period. Among the remaining values, approximately 24.9% used time series lasting between 11 and 20 years, representing the largest proportion. Approximately 17.7% of the data came from series with less than 10 years (8.3% with less than 5), while only 15.1% used series longer than 20 years.

Regarding the rainfall kinetic energy equations used, 98.5% of the results were obtained using the Wischmeier and Smith equation, as adapted by Foster et al. [32,33], whereas only 0.02% used the Wischmeier and Smith equation modified by Cabeda [34,35]. The others used the Wischmeier and Smith equation revised by other authors.

Of the studies analyzed, 68.5% were published in Portuguese, while only 31.5% were published in English. This disparity highlights the importance of internationalizing these data [16] to boost the expansion of global databases [15].

It is important to mention some limitations of this study, as follows:

1. Some studies showed local coordinates instead of the coordinates of the station [36,37,38,39,40,41], which raises doubts about which station was used. In these cases, which represented only eight locations (0.14% of the total), the average coordinates of the locations were used.

2. In certain studies, essential information was missing, such as the period used, raising uncertainties about the duration of the data series analyzed, since the use of longer data series is ideal. However, all information was presented, and it is up to the user to decide whether or not to consider the information pertinent to such studies/stations.

3. There is a large difference between the amount of RE values coming from PD and SS. If the user of this database chooses to separate the two databases, for whatever reason, information regarding the method of obtaining them (PD or SS) was provided.

4. Data Availability

The final result of this study was the creation of a comprehensive database, made publicly available in spreadsheet format and point vector layers (SHP, GPKG, and GEOJSON formats), containing data on rainfall erosivity in Brazil, with information for each location studied. Table 2 shows the types of data collected and in which columns of the spreadsheet and vector layers they are located, along with their descriptions and the units adopted.

The complete database developed in the present study has open access and is available on the Mendeley Data platform, https://doi.org/10.17632/gkzyh8mwj9.1 [42].

5. Conclusions

The data analysis revealed significant patterns in the distribution of rainfall erosivity across the country, highlighting areas with greater susceptibility to the erosive action of rainfall. The importance of these studies is expressed by the high erosivity observed in regions affected by intense rainfall. To advance in this field, it is essential to expand the network of meteorological stations, ensuring robust historical series and eliminating the technical limitations present in current studies.

Furthermore, the internationalization of data is a crucial measure, considering the predominance of studies in Portuguese. This would not only enrich global databases but also support environmental conservation and basin management policies and practices, especially through the use of models such as the USLE/RUSLE. These efforts are fundamental for the development of effective erosion mitigation strategies and for environmental sustainability in Brazil.

Author Contributions

R.A.C., S.S.Z. and M.C.M. performed the funding acquisition and contextualization of this manuscript. R.A.C., A.C.X., M.P.O.-R. and D.B.S.T. performed the data curation. M.P.O.-R. then performed the formal data analysis, and R.A.C., A.Q.A., A.C.X., M.C.M., S.S.Z. and D.B.S.T. performed the validation and visualization. M.P.O.-R. prepared the manuscript, with the collaboration of all co-authors. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are openly available in Mendeley Data platform at https://doi.org/10.17632/gkzyh8mwj9.1.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Classification, regional distribution, and extreme values of rainfall erosivity in Brazil.

Figure 2. Quantitative analysis of rainfall erosivity data for Brazil. (a) Percentage of pluviographic and synthetic data on rainfall erosivity in Brazil. (b) Percentage of pluviographic and synthetic data on rainfall erosivity in different regions of Brazil. (c) Comparative analysis of erosivity studies with monthly and annual data (EDMA) and studies with only annual data (EDA). (d) Comparative analysis of municipalities with monthly and annual data (MDMA) and municipalities with only annual data (MDA). (e) Frequency of data in different erosivity classes. (f) Analysis of data frequency by year.

Table 1

Classes of annual rainfall erosivity. Source: Ref. [19].

Erosivity Class	RE Values (MJ mm ha−1 h−1 year−1)
Low	RE ≤ 2452
Medium	2452 < RE ≤ 4905
Medium-strong	4905 < RE ≤ 7357
Strong	7357 < RE ≤ 9810
Very Strong	RE > 9810

Table 2

Identification column, name, description, and units used in the database.

Columns	Name	Description	Unit
A	Name	Name of the station	-
B	Code_Station	Code of the station from which the study was made	-
C	Agency	Institution or agency responsible for the station	-
D	Municipality Or Basin	Municipality or basin in which the station is located	-
E	State	Brazilian state in which the station is located	-
F	Region	Brazilian region in which the station is located	-
G	Altitude	Altitude of the station or place of study	m (meters)
H	Latitude	Latitude of the station or place of study	degrees
I	Longitude	Longitude of the station or the place of study	degrees
J	Years of data	Number of years in the historical series	-
K	Period	Date of start and end of the data series	-
L	Method	Origin of the rainfall data	-
M	Equation of EC	The equation used by the study to calculate the kinetic energy	-
N	RE_Jan	Monthly erosivity for January	MJ mm ha−1 h−1 month−1
O	RE_Feb	Monthly erosivity for February	MJ mm ha−1 h−1 month−1
P	RE_Mar	Monthly erosivity for March	MJ mm ha−1 h−1 month−1
Q	RE_Apr	Monthly erosivity for April	MJ mm ha−1 h−1 month−1
R	RE_May	Monthly erosivity for May	MJ mm ha−1 h−1 month−1
S	RE_Jun	Monthly erosivity for June	MJ mm ha−1 h−1 month−1
T	RE_Jul	Monthly erosivity for July	MJ mm ha−1 h−1 month−1
U	RE_Aug	Monthly erosivity for August	MJ mm ha−1 h−1 month−1
V	RE_Sep	Monthly erosivity for September	MJ mm ha−1 h−1 month−1
W	RE_Oct	Monthly erosivity for October	MJ mm ha−1 h−1 month−1
X	RE_Nov	Monthly erosivity for November	MJ mm ha−1 h−1 month−1
Y	RE_Dec	Monthly erosivity for December	MJ mm ha−1 h−1 month−1
Z	RE_Annual	Annual rainfall erosivity	MJ mm ha−1 h−1 year−1
AA	Year of publication	Year of publication of the study	-
AB	Title	Publication’s title	-
AC	Author(s)	Publication’s authors	-
AD	Journal	The journal in which the study was published	-
AE	DOI or Fulltext URL	Publication’s web address	-
AF	Language	Orignal language of the study	-

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