2.1. Study Area
The Guiana plateau, also called the Guiana Shield, is a region in South America located north of the Amazon River and east of the Orinoco River. This area is over 2 million km². It spans seven countries: Colombia, Venezuela, Guyana, Suriname, French Guiana and northern Brazil (Amapá, Roraima and Pará). The Guiana Shield can be described as an old rock area, whose soil is poor, with an extensive river system and dense primary rainforest [
35]. This area accounts for 13% of the surface of the South American continent. In this study, the area taken into account lies between 2° S and 6° N latitude and 62° W and 45° W longitude (
Figure 1).
The main factors controlling the South American climate are the subtropical high pressure zones in the South Atlantic (Saint Helena anticyclone) and the South Pacific (Easter Island anticyclone), as well as their seasonal oscillations [
36]. However, the semi-stationary North Atlantic high pressure zone, which is located north of the equator (Azores High), also impacts the study area. These high pressure areas determine the location of winds and clouds, hence the oscillation of the inter-tropical convergence zone (ITCZ). The ITCZ is the meeting area of trade winds. Air masses weakened by trade winds will become saturated with water vapor over the oceans [
37]. These hot and humid cloud masses expand under the influence of heat and are pushed aloft by convective activity. They then undergo a cooling process and lose their water vapor via condensation, thus creating heavy precipitation [
38]. When the South Atlantic is warmer (colder) than the North Atlantic during the austral summer (winter), the ITCZ shifts to a more southern (northern) latitude. Marengo
et al. [
39,
40] show that the intensification of northeast trade winds may result in improved moisture transport in the tropical North Atlantic boundary layer toward the Amazon Basin. Moreover, Satyamurty
et al. [
41] showed that an evapotranspiration rate of approximately 3.0 mm/day is responsible for over 30% of regional precipitation in the Amazon Basin. Many studies have been conducted in the Amazon [
30,
42,
43,
44,
45,
46], especially in connection with deforestation [
47], wind, humidity circulation [
46] and river breeze circulation [
30,
48]. During the rainy season in the Amazon Basin, high amounts of precipitation are due to more frequent rather than higher intensity rain [
30]. Only one study focused on climate variability in the Guiana Shield . It utilized ground data and re-analyses to identify seasonal rainfall and river isotopic chemistry patterns in part of Guyana using TRMM and ERA-Interim [
49]. The results suggested that re-analyses provide a consistent and precise spatial temperature distribution, but a less accurate precipitation distribution.
Figure 2 shows the daily rainfall averages from 2001–2012 for the four products. The
in situ data were spatially interpolated. The triangulation-based linear interpolation [
50] method was used with a regular grid of 25 km° × 25 km°. The coastline data come from the GSHHS (Global Self-consistent, Hierarchical, High-resolution Geography Database). Data over the ocean are not considered.
In situ stations exhibit a greater average daily intensity (10–12 mm/d) from the East Guyanese coast to the mouth of the Amazon. Relatively high intensities also exist on Marajó Island (~9 mm/day), as well as intermediate intensities NW of Pará (~7 mm/day). The two TRMM-TMPA products exhibit daily intensity patterns that differ from the PERSIANN and CMORPH products. The TMPA product shows spatial intensities that generally agree with
in situ data, particularly TMPA V7. However, TMPA RT tends to overestimate intensities in the NW regions of Pará and North Amazonas. CMORPH and especially PERSIANN exhibit the highest daily intensities in the NE regions of the Amazon, Marajó Island and Marajó Bay. However, they greatly underestimate coastal precipitation. This may be due to poor rainfall detection over coastal areas, as was previously noted by Huffman
et al. [
21].
Figure 1.
An elevation map of the Guiana Shield. The SRTM30 (Shuttle Radar Topography Mission) is available at
http://www.diva-gis.org/gdata. The dots represent the
in situ gauges available in French Guiana and northern Brazil. The six different colors stand for the six hydro-climatic regimes identified according to ascending hierarchical clustering. For each of the areas, the annual precipitation (mm/month) is indicated. The six areas are: Z1
East French Guiana; Z2
Amapá; Z3
Amazon; Z4
Marajó; Z5
South Roraima+West French Guiana; and Z6
North Roraima.
Figure 1.
An elevation map of the Guiana Shield. The SRTM30 (Shuttle Radar Topography Mission) is available at
http://www.diva-gis.org/gdata. The dots represent the
in situ gauges available in French Guiana and northern Brazil. The six different colors stand for the six hydro-climatic regimes identified according to ascending hierarchical clustering. For each of the areas, the annual precipitation (mm/month) is indicated. The six areas are: Z1
East French Guiana; Z2
Amapá; Z3
Amazon; Z4
Marajó; Z5
South Roraima+West French Guiana; and Z6
North Roraima.
Figure 2.
Average daily precipitation (mm/d) from 2001–2012 for all four satellite products and triangulation-based linear interpolation for in situ stations. TMPA V7, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis Version 7; RT, real time.
Figure 2.
Average daily precipitation (mm/d) from 2001–2012 for all four satellite products and triangulation-based linear interpolation for in situ stations. TMPA V7, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis Version 7; RT, real time.
We are interested in product performance based on different rainfall patterns (
Figure 1). Six areas, in different hydro-climatic regimes, were identified: Z1
East French Guiana; Z2
Amapá; Z3
Amazon; Z4
Marajó; Z5
South Roraima+West French Guiana; and Z6
North Roraima. These six areas were selected using a hierarchical ascendant classification [
51,
52] based on monthly average precipitation totals (
Figure 1). This classification proceeds via successive steps, which converge objects into a group. At the end of each stage, we recalculated the Euclidean distances between the newly-created group and the rest of the objects. The process is repeated until all objects have converged into one group. Our choice for the dissimilarity index will be the Euclidean distance, and the choice of index aggregation is Ward’s method. Areas Z1
East French Guiana and Z5
South Roraima+West French Guiana exhibit a bimodal regime with two dry seasons and two rainy seasons. The other areas are subject to a unimodal system, with a dry season and a rainy season. The rainy season occurs at different times in all of the areas. However, the onset of rains occurs along a NW-SE gradient, which is likely related to ITCZ movements. The areas south of the Guiana Shield, Z3
Amazon and Z4
Marajó, are subject to maximum rainfall intensities in March, when the ITCZ reaches its lowest position. At this time, we observe a small dry period in Z1
East French Guiana, further north. When the ITCZ moves back to the north, Z2
Amapá is subject to maximum rainfall intensities, mainly during the month of April. These maximum rainfall intensities start in May for Z1
East French Guiana and Z5
South Roraima+West French Guiana, then in June for Z6
North Roraima. The coastline is subject to significant precipitation, with average monthly station measurements reaching 600 mm in Z1
East French Guiana and 750 mm in Z2
Amapá.
Comparing the two most rainy regions (Z1East French Guiana and Z2Amapá), we see that it rains more often in Eastern Guiana than in Amapá, but average intensities in Eastern Guiana are lower, resulting in a lower yearly total to date.
Table 1 shows the hydro-climatic zones in descending order of average annual total rainfall (mm/year). For each zone, we have included the value of the simple day intensity index (SDII), which represents the daily average of rainy days and the percentage of rainy days. The Amapá coast (Z2
Amapá) is the wettest area, with 4101 mm/year on average. Northern Roraima (Z6
North Roraima) receives the least amount of precipitation, with an average rainfall of 1809 mm/year. Note that the SDII does not decrease with the yearly aggregate. For instance, South Roraima (Z5
South Roraima+West French Guiana) and North Roraima (Z6
North Roraima) exhibit an annual rainfall difference of approximately 630 mm. However, North Roraima’s SDII is larger (15 mm) than that of South Roraima (12.6 mm). Therefore, although Northern Roraima is the least rainy region (34% rainy days), it is subject to medium intensity rainfall that is longer and more intense than in the south.
Table 1.
The average annual total simple day intensity index (SDII) (mm/year) accounting for the daily average of rainy days and the percentage of rainy days. Areas are ranked in descending order of the average annual aggregate.
Table 1.
The average annual total simple day intensity index (SDII) (mm/year) accounting for the daily average of rainy days and the percentage of rainy days. Areas are ranked in descending order of the average annual aggregate.
Zones | Average Yearly Aggregate (mm/y) | SDII (mm/d) | Rainy (%) |
---|
Z2Amapá | 4101 | 19.6 | 59 |
Z1East French Guiana | 3581 | 13.5 | 74 |
Z4Marajó | 2855 | 15.3 | 54 |
Z5South Roraima + West French Guiana | 2446 | 12.6 | 55 |
Z3Amazon | 2153 | 13.5 | 46 |
Z6North Roraima | 1809 | 15 | 34 |
2.4. Data Quality Control
The satellite product data were downloaded in files with three-hour time steps (00H, 03H, 06H, 09H, 12H, 15H, 18H, 21H), based on the GMT 0:00 time zone. Daily totals were generated by summing the tri-hourly files. The study area falls within the −03H00 GMT time zone. Therefore, it was necessary to sum the tri-hourly totals to best represent the daily aggregate of the study area. If a tri-hourly file were missing for a particular day, that day was not taken into account.
A data quality control analysis was conducted on both satellite and in situ data series for the study period between 1 January 2001 and 30 December 2012. Negative values in the in situ and satellite datasets have been deleted. Values greater than the 99.999th percentile + 1.5 × (Q3 − Q1), where Q3 is the third quartile and Q1 is the first quartile, were considered outliers and removed from both series. We also conducted homogeneity tests between the rain gauge records and the co-located satellite grid values.