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Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia
Atmospheric Research 238 (2020) 104879
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Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia
T
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Rosane Barbosa Lopes Cavalcantea, , Douglas Batista da Silva Ferreiraa, Paulo Rógenes Monteiro Pontesa, Renata Gonçalves Tedeschia, Cláudia Priscila Wanzeler da Costaa, Everaldo Barreiros de Souzab a b
Instituto Tecnológico Vale – Desenvolvimento Sustentável, Brazil Universidade Federal do Pará, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: CHIRPS Extreme rainfall Rainfall trends Amazon
Since several datasets are available with marked differences, the assessment of precipitation data is a key aspect to support the choice of the most adequate precipitation product for a certain research or operational application. In the present study, we evaluated the use of the daily rainfall dataset from CHIRPS with spatial resolution of 0.05° for different purposes in the states of the Brazilian Legal Amazon. We compared monthly rainfall, annual rainfall indices and their trends calculated using CHIRPS data and rain gauge observations with a point-to-pixel analysis. The use of daily CHIRPS data provided mean monthly rainfall similar to that obtained using data from the rain gauge stations, but CHIRPS data tend to underestimate the values for the rainiest months. The correlation was usually lower in the western Amazon, especially during its rainy season. The same underestimation was observed for extreme rainfall indices. CHIRPS product produces more similar results to rain gauge data for the indices PRCPTOT, nP, and R95pad, while strong underestimate the most extreme rainfall indices (R50mm, Rx1day, Rx5days). For the 45 stations and 15 rainfall indices analysed, 63 significant trends were detected using rain gauge data, of which only 13 were detected using CHIRPS product. Therefore, the use of CHIRPS data does not well represent the trends in rainfall indices.
1. Introduction Rainfall information is an important input for different applications, such as agriculture, hydrology, climate, management of water resources, design of hydraulic structures, drought and flood risk assessment and forecasting, and ecological modelling. In recent decades, several gridded precipitation datasets have been developed with different design objectives, data sources, spatial resolutions, spatial coverage, published temporal resolutions, temporal spans, and latencies (Beck et al., 2017; Sun et al., 2018). Exclusively gauge-based datasets tend to show lower performances in regions with sparse rain gauge networks since precipitation features high spatial variation (Sun et al., 2018). The advancement of remote sensing instruments and precipitation retrieval algorithms has made available a series of satellite-based precipitation products. These products use information from visible/ infrared sensors on geostationary and low Earth orbit satellites, passive or active microwave sensors on low Earth orbit satellites, and combinations of both (Kidd and Levizzani, 2011; Sun et al., 2018). The
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satellite-based precipitation estimates have significant uncertainty (Tian and Peters-Lidard, 2010) because none of the satellite sensors detect rainfall as such, and further merging or blending with rain gauge information can improve precipitation products. Since several datasets are available, the assessment of precipitation data is a key aspect to support the choice of the most adequate precipitation product for a certain research or operational application (Paredes Trejo et al., 2016). Many studies evaluate the advantages and limitations of the available datasets and compare their results (see review by Karimi and Bastiaanssen, 2015), and marked differences have been found even among datasets employing the same data sources (Beck et al., 2017). The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) provides blended gauge-satellite precipitation spanning 50°S50°N from 1981 to near the present. CHIRPS are provided at spatial resolutions of 0.25° and 0.05° and daily to annual temporal resolutions with a short latency (Funk et al., 2015). According to the authors, this dataset can be used for trend analysis and seasonal drought monitoring.
Corresponding author at: R. Boaventura da Silva, 955 - Nazaré, Belém, PA, Brazil. E-mail address: [email protected] (R.B.L. Cavalcante).
https://doi.org/10.1016/j.atmosres.2020.104879 Received 25 October 2019; Received in revised form 27 January 2020; Accepted 29 January 2020 Available online 30 January 2020 0169-8095/ © 2020 Elsevier B.V. All rights reserved.
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associated primarily with the quasi-stationary atmospheric patterns of the South Atlantic Convergence Zone (Figueroa and Nobre, 1990; de Souza et al., 2000; Souza and Ambrizzi, 2003; Carvalho et al., 2004). On the other hand, the configuration of the maximum rainfall observed during the fall months (March to May) shows a zonal elongated band distributed across the northern portion of the region. Precipitation of this maximum is mainly induced by the Intertropical Convergence Zone, which reaches its southern position during the months of March/ April (Hastenrath and Heller, 1977; Nobre and Srukla, 1996; Souza et al., 1998; Xavier et al., 2000; Souza et al., 2004). Interannual variability is related to the El Niño-Southern Oscillation and to the sea surface temperature gradient over the tropical Atlantic (Marengo et al., 1993; Yoon and Zeng, 2010). Negative rainfall trends have been identified for the entire Amazon basin after 1975, while at the regional level, the trend is negative in northern Amazonia and positive in southern Amazonia (Marengo, 2004; Espinoza Villar et al., 2009). Chagnon (2004) suggest that current deforestation in the Amazon has already altered the regional climate of deforested areas. Espinoza et al. (2019) used CHIRPS and HOP (interpolated HYBAN observed precipitation) to show that dry-day frequency has significantly increased in the southern Amazon, Bolivian Amazon, central Peruvian Amazon and southern Brazilian Amazon. Similarly, the wet-day frequency has significantly increased in the northern Amazon.
Validation studies have been conducted in different regions of the globe, e.g., Cyprus (Katsanos et al., 2016), Argentina (Rivera et al., 2018), Nepal (Shrestha et al., 2017), Italy (Duan et al., 2016), Chile (Zambrano et al., 2017), China (Bai et al., 2018; Gao et al., 2018; Zhong et al., 2019; Lai et al., 2019), Burkina Faso (Dembélé and Zwart, 2016), Mozambique (Toté et al., 2015), the Tibetan Plateau (Wu et al., 2019), India (Prakash, 2019), Ethiopia (Duan et al., 2019; Bayissa et al., 2017), Myanmar (Sirisena et al., 2018), Venezuela (Paredes Trejo et al., 2016), and in other regions of South America (Baez-Villanueva et al., 2018; Funk et al., 2015). In these studies, the evaluation of CHIRPS products is usually accomplished by comparing rainfall indices with other datasets and with in situ observations at different temporal scales using different performance metrics. The most common purposes are drought monitoring, rainfall detection, and streamflow simulation. The results show that CHIRPS performs satisfactorily in almost all studies and better than other datasets in many cases. In Brazil, Beck et al. (2017), using hydrological modelling to evaluate the accuracy of precipitation datasets, found that CHIRPS V2.0 tended to perform better than the other tested datasets in central and eastern Brazil and is a viable choice for daily temporal resolution in tropical regions if the peak magnitude underestimation and spurious drizzle are not critical. Funk et al. (2015) showed that the wet season correlations between the CHIRPS and the Global Precipitation Climatology Center (GPCC) are lower in western Amazonia than in other regions of Brazil. For northeastern Brazil, CHIRPS correlates well with monthly rain gauge data but tends to overestimate low and underestimate high rainfall values, and rain detection is poor in semiarid areas (Paredes-Trejo et al., 2017). (Costa et al., 2019), when comparing monthly precipitation data from CHIRPS 2.0 with rain station data from 1998 to 2010, found the largest spatial differences in precipitation in northwestern Amazonas and southwestern Pará. Correa et al. (2017) used hydrological modelling and rainfall datasets as input data to a hydrological model in order to analyse past floods and droughts in the Amazon River Basin. Using different performance metrics, the authors showed that CHIRPS V2.0 is one of the best rainfall datasets for the region, probably because this dataset not only is based on atmospheric models but also uses in situ rainfall information. The aim of this study is to evaluate the use of a rainfall dataset from CHIRPS for different purposes in the states of the Brazilian Legal Amazon. We compared monthly rainfall, annual rainfall indices and their trends calculated using CHIRPS and gauge observations. Although Beck et al. (2017) criticize studies that re-use gauge observations already incorporated into rainfall datasets to determine their accuracy, in the case of CHIRPS data, even in the presence of co-located stations, the CHIRPS have some influence from the CHIRP (exclusively satellitebased data) (Funk et al., 2015). The results of this study will provide bases for decision makers on the applicability of these data products for different meteorological studies and the probability of occurrence of extreme events.
3. Material and methods We used a point-to-pixel analysis to compare the rainfall data from rain gauge stations in the BLA and CHIRPS data. This methodology has been widely used in assessing rainfall estimated by remote sensing (e.g., Baez-Villanueva et al., 2018; Paredes-Trejo et al., 2017) and avoids errors due to the spatial interpolation of sparsely located and unevenly distributed rain gauges. The CHIRPS rainfall data series was created by extracting the daily rainfall estimates over the pixel in which each selected rain gauge station is located. The days with missing data in the rain gauge stations were also considered as missing data in the CHIRPS data series for comparison. Although the data from rain gauge stations were already incorporated into the CHIRPS dataset, in order to adjust the bias of CHIRP data, the final CHIRPS estimate is a combination of unadjusted and bias-adjusted CHIRP data (Funk et al., 2015). We analysed the spatial variations in the differences in monthly rainfall and annual extreme rainfall indices between the two data series (rain gauge stations and CHIRPS). The monthly scale was selected instead of the daily scale due to the different periods of 24 h used to calculate daily rainfall of the two data types. The performance of the CHIRPS product in estimating monthly rainfall and rainfall indices was evaluated by using the Pearson correlation coefficient (RHO), the mean error (ME), the mean absolute error (MAE) and the mean relative error (MRE) (Table 1). The rainfall indices (Table 2) were selected based on the core set of descriptive indices of extreme precipitation defined by the Expert Team on Climate Change Detection and Indices (Karl et al., 1999). The indices were calculated annually. We included the all-day percentile, in addition to the wet-day percentiles, to evaluate changes in frequency as suggested by Schär et al. (2016) and the number of days with rainfall (R) since it is used to calculate other indices. We also compared the detection of trends in the annual series of extreme rainfall indices using the CHIRPS data and the rain gauge data. Trend analyses are useful for planning studies and assessments of land cover and climate change impacts. The annual trends in extreme rainfall indices were analysed by using the modified nonparametric MannKendall test (Hamed and Rao, 1998; Kendall, 1955; Mann, 1945) to investigate the statistical significance at a significance level of 0.05. The magnitude of the change over time was estimated using the nonparametric Sen's slope estimator (Sen, 1968). The Mann-Kendall test and Sen's slope estimator have been widely used for to effectively identify
2. Study region The study area is the territory of the nine Brazilian states that contain areas in the Amazon biome and compose the Brazilian Legal Amazon (BLA), covering 5 million km2. Inside the BLA, the Arc of Deforestation is present, a region where most of the natural land cover (forest) has been replaced by pasturelands since 1970. To protect natural forest areas, the Brazilian government recognized several indigenous lands and created conservation units. Approximately 44% of the BLA territory is covered by these areas (Veríssimo et al., 2011). The Amazon region is characterized by high annual rainfall, with an average between 1400 mm and 3000 mm; the two regions with the highest values (precipitation above 2000 mm) are located distinctly over the northwestern and northeastern portions (Fig. 1). Analysing the contribution of precipitation during the summer months (December to February), the maximum rainfall is oriented northwest-southeast and is 2
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Fig. 1. Location of the National Institute of Meteorology (INMET) stations in the states of the Brazilian Legal Amazon and mean seasonal precipitation estimated by CHIRPS. Table 1 Equations for the statistical metrics used to evaluate CHIRPS monthly rainfall estimation and annual indices. The CHIRPS and rain gauge data are represented by x and y, respectively. The number of points is represented by n. Name Pearson correlation coefficient Mean error
Table 2 Description of the rainfall indices used.
Formula n (∑ xy ) − (∑ x )(∑ y )
RHO =
ME =
1 n
Mean absolute error
MAE =
Mean relative error
MRE =
Symbol
Description
nP Rx1day SDII
Number of days with R ≥ 1 mm Maximum 1-day precipitation Simple precipitation intensity index: mean daily precipitation intensity of days with R ≥ 1 mm Maximum consecutive 5-day precipitation Count of days when R ≥ 10 mm Count of days when R ≥ 20 mm Count of days when R ≥ 50 mm Maximum length of dry spell: maximum number of consecutive days with R < 1 mm Maximum length of wet spell: maximum number of consecutive days with R ≥ 1 mm Total precipitation when R > 95 percentile of days with R ≥ 1 mm Total precipitation when R > 99 percentile of days with R ≥ 1 mm Total precipitation when R > 95 percentile of all days Total precipitation when R > 99 percentile of all days Annual total precipitation Sum of precipitation of the 4 days with highest R
[n ∑ x 2 − (∑ x )2][n ∑ y 2 − (∑ y )2]
Rx5days R10mm R20mm R50mm CDD
∑ (x − y ) 1 n 1 n
∑ |x − y|
∑
(x − y ) y
CWD
monotonic trends in extreme rainfall time series worldwide (for example, Gocic and Trajkovic, 2013; Shahid, 2011; da Silva et al., 2015; Pingale et al., 2014; Madsen et al., 2014).
R95p R99p R95pad R99pad PRCPTOT RTop4
3.1. Rain gauge data Daily rainfall observations were provided by the Brazilian National Institute of Meteorology (INMET; available online at www.inmet.gov. br). All the stations for the states of the BLA are listed in Supplementary Material (Table S1). The average is 0.012 stations per 1000 km2, and
they are unevenly distributed (Fig. 1), which motivated the evaluation of satellite-based precipitation products in the region. The stations are concentrated in the eastern portion of BLA, where deforestation is older 3
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Fig. 2. Mean monthly precipitation from 1981 to 2017 at the 45 selected rain gauge stations.
in the analysis. 3.2. CHIRPS data We used the latest version of CHIRPS (CHIRPS V.2) obtained from the website (http://chg.geog.ucsb.edu/data/chirps/) for the years 1981 to 2017. The data used were the daily rainfall with a resampled spatial resolution of 0.05°. The satellite-based CHIRP data are produced by the infrared cold cloud duration (CCD) data calibrated with tropical rainfall measuring mission (TRMM) 3B42 data to generate the pentad precipitation estimates. The result is expressed as a fraction of the normal and multiplied by the climatological normal to remove the systematic bias. The CHIRP product is blended with rain gauge station data using a modified inverse distance weighting algorithm to produce the CHIRPS. Daily disaggregation is produced using daily CCD and daily coupled forecast system (CFS) data with a simple proportional method (Funk et al., 2015).
Fig. 3. Percentage of missing data per month from 1981 to 2017 at the 45 selected rain gauge stations.
4. Results and discussion and the population is higher. In the central and western BLA, the stations are mostly distributed along the main rivers. We visually checked the daily and monthly data series to identify obviously incorrect values. Outliers were identified as the mean value of observations for the month plus or minus four times the standard deviation of the value for the month in the entire series. This method was used for accumulated monthly rainfall, maximum daily rainfall, and consecutive numbers of days without rain. The monthly values outside these thresholds were manually checked by comparison with other stations or with registered extreme events. Months with more than one day of missing daily data, including values that were found to be suspect or incorrect after undergoing quality control, were considered missing data. This is less strict than the recommendation of WMO (2017) for the calculation of climate normals, which defines that total precipitation can only be calculated if there are complete data over the month. The stations with less than 20% missing monthly data from 1981 to 2017 (period with CHIRPS data) were used
4.1. Monthly rainfall and annual indices from rain gauges A total of 45 of the 62 INMET's stations located in BLA presented less than 20% of missing monthly rainfall data from 1981 to 2017 and were used in the study. The majority of the stations are located in the Amazon biome, but 12 stations in the eastern and southeastern portions of the study area are located in Cerrado (tropical savanna). The mean monthly rainfall of each INMET stations are shown in Fig. 2. The missing monthly values were well distributed between the months varying from 7.5% of the total missing monthly occurring in May to 10.2% in February. For the 37 years of the analysed period, the maximum standard deviation of the monthly total missing data calculated for each station was 2.7 data. In general, the stations with higher percentages of missing data are located in the western Amazon (Fig. 3). The mean annual rainfall indices calculated for rain gauge observations are shown in Fig. 4. The stations in the eastern BLA presented lower annual total precipitation (PRCPTOT) and higher 4
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Fig. 4. Mean annual rainfall indices calculated for rain gauge observations from 1981 to 2017 at the 45 selected rain gauge stations.
Fig. 5. Monthly rainfall for rain gauge observations and CHIRPS estimates (N = 18,769) from 1981 to 2017 at the locations of the 45 selected rain gauge stations. The dashed line indicates 1:1.
correlates (p-value < .01) with monthly rainfall observed in the rain gauge stations. The minimum RHO for the stations in the Amazon biome was 0.66, and for the Cerrado biome was 0.91. However, CHIRPS data underestimate the high values of monthly precipitation, especially the most extreme monthly precipitation registered by the rain gauge observations (Fig. 5). This pattern can be seen in the cumulative curves presented in Fig. 6, which show a higher frequency for precipitation estimated by CHIRPS data than by rain gauge data for monthly rainfall greater than 240 mm and a lower frequency of monthly precipitation lower than 10 mm. The underestimation of the higher values of monthly rainfall from CHIRPS observed in the present study was also observed by Paredes-Trejo et al. (2017), who used 21 rain gauge stations in northeastern Brazil, 12 of which were included in the present study and located in the state of Maranhão.
consecutive dry days (CDD), indicating the presence of a strong dry season, especially in the south-eastern. They also presented a lower number of days with rain in total (nP), and number of days with rainfall higher than 10 mm, 20 mm, 50 mm, and 95 and 99 percentile of days with rain (R10mm, R20mm, R50mm, R95p, and R99p). The mean number of maximum consecutive wet days (CWD) are higher in the northern locations, especially close to the Amazon delta. No spatial pattern was observed for the indexes R95pad, R99pad, and SDII, which presented the lowest ranges of variation throughout the BLA weather stations. 4.2. Monthly rainfall Monthly rainfall calculated with CHIRPS product significantly 5
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Fig. 6. Empirical cumulative distribution of monthly rainfall for rain gauge observations and corresponding CHIRPS estimates (N = 18,769) from 1981 to 2017 for all stations and separated by biome.
Fig. 7. Box-plots of mean monthly rainfall at the 45 selected rain gauge stations (top), at the 33 stations in Amazon biome (middle) and at the 12 stations in Cerrado biome (bottom) for monthly rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017. The red crosses indicate the outliers. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
low correlation in western Amazonia for the wet season. This pattern can also be noted in the analyse of the correlation between RHO and longitude (positive values) and rainfall (negative values) (Table 3). The highest values are concentrated in the eastern BLA in the beginning (December and January) and end (April and May) of the rainy season. As result, when analysing by biomes, the monthly mean RHO for the Amazon stations were greater than for Cerrado, except for July, when it is 1% lower. The greatest differences are in March and May, when mean RHO are 20% and 19% greater for Amazon biome than for Cerrado. However, lower values are observed in the northeastern portion at the station closest to the Atlantic Ocean (Figure) in the driest months in this region (July to September). A reduced performance in the dry
Considering the mean monthly precipitation from 1981 to 2017 for all pixels with rain gauge stations, the CHIRPS monthly mean value and variability for the BLA were similar to those for rain gauge stations for the entire BLA and for both biomes (Figs. 7 and 8). Fig. 9 shows the monthly values of RHO for each rain gauge station. The maps for ME and MAE are presented in the Supplemental Material (Figs. S1 and S2). ME is predominantly negative from February to June. Due to the months with precipitation equal to zero, MRE is not calculated. The RHO tends to decrease with increased R. RHO is usually lower in the western portion of the study area, especially in the rainy season in the region: from December to May. Funk et al. (2015) also showed a 6
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Fig. 8. Annual mean monthly rainfall at the 45 selected rain gauge stations (top), at the 33 stations in Amazon biome (middle), and at the 12 stations in Cerrado biome (bottom) for monthly rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017.
Fig. 9. Spatial distribution of Pearson's linear correlation coefficient for monthly rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017 at the 45 selected rain gauge stations.
season was also observed by Paredes Trejo et al. (2016) in Venezuela. According to Tian and Peters-Lidard (2010), the satellite-based precipitation uncertainty is greater in southern and eastern areas for
June–July August and in eastern areas and those closer to the ocean for September–October-November. Additionally, during the dry season in the Amazon, precipitation is due to local factors associated with high 7
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Table 3 Correlation coefficients between statistical measures (RHO, MAE, and ME) for each month and rain gauge station characteristics: latitude (Lat), longitude (Lon), elevation (Ele), and annual mean rainfall (R). The colours highlight the values from −1 (red) to +1 (blue).
RHO Lat
Lon
Ele
MAE R
Lat
Lon
Ele
ME R
Lat
Lon
Ele
R
Jan
0.22 0.43 -0.11 -0.25 -0.08 -0.30 -0.04 0.53 -0.17 -0.09
0.27 -0.02
Feb
0.23 0.38 -0.10 -0.27 -0.07 -0.18 -0.05 0.45 -0.20
0.22 -0.17
Mar
0.26 0.51 -0.02 -0.25
0.07 -0.19 -0.14 0.49 -0.15 -0.01
0.19
Apr
0.11 0.62
0.54 -0.14 -0.54 0.68 -0.22
0.26 -0.09
0.02 -0.41
0.19 0.06
0.03
May -0.07 0.57 -0.01 -0.47
0.80 -0.24 -0.71 0.63 -0.08 -0.19
0.02
0.35
Jun
0.00 0.37 -0.05 -0.36
0.76 -0.43 -0.65 0.76
0.02 -0.09
0.00
0.30
Jul
0.02 0.22 -0.18 -0.26
0.74 -0.43 -0.65 0.79
0.07 -0.19 -0.04
0.40
Aug
-0.05 0.28 -0.02 -0.35
0.63 -0.60 -0.55 0.73
0.19 -0.15 -0.13
0.49
Sep
-0.17 0.19
0.13 -0.67 -0.06 0.55
0.10 -0.02 -0.17
0.31
Oct
0.04 0.49
Nov Dec
0.14 -0.41
0.03 -0.31 -0.21 -0.68
0.15 0.29 -0.09 -0.16
0.07
0.20
0.17 0.36 -0.20 -0.10 -0.31 -0.58
0.24 0.15 -0.10
0.11
0.06 -0.06
0.23 0.63 -0.19 -0.30 -0.12 -0.31
0.10 0.22 -0.15
0.11
0.23 -0.05
which present higher mean values. As result, the mean RHO for the stations located in the Cerrado biome was greater than for the Amazon Biome for all the indices. The lowest RHO values were observed for CWD and for the most extreme rainfall indices: Rx1day and R99p. However, some stations with good correlations presented high ME (Fig. 12), MAE, and MRE (Figs. S5 and S6). In some cases, one station presented errors much higher than the other stations: a MAE of 546.5 m for PRCPTOT, 54.3 days for nP, and 10 days for CWD were observed. In the first two cited cases, the referenced station presented a good correlation between the indices obtained with CHIRPS and INMET. At almost all stations, CHIRPS data overestimate (positive ME) R10mm, but underestimate (negative ME and MRE) the most extreme precipitations (R50mm, RTop4, Rx1day, and Rx5days) (Fig. 12), as previously observed for monthly rainfall. This dataset also tends to underestimate CWD and nP at the northern stations, especially in the state of Pará, and overestimate nP in southeastern Amazonia. In general, lower ME and MRE for CWD were observed in the stations with higher mean CWD. In total, the mean nP and CWD were underestimated at 23 and 24 of the 45 stations, respectively. The nP error was reflected in the indices calculated using the days with rain (R95p, R99p and SDII). The ME for CDD was greater (but negative) at the eastern stations, which presented the highest mean CDDs. A few studies analysed the performance of CHIRPS data to estimate rainfall indices. For Ghana (Larbi et al., 2018) and Cyprus (Katsanos et al., 2016), CHIRPS data correlate very well with the available station data. For Venezuela, Paredes Trejo et al. (2016) also observed underestimation of more extreme rainfall, and the authors highlighted that the CHIRPS V.2 product had a low capability of rain detection, especially during the rainy season, due to its tendency to misclassify rainfall events. CHIRPS data always use CHIRP original data, and extreme values at gauge stations are considered standardized anomalies; very large absolute values and large ratios in the CHIRP original data are not used in the CHIRPS blending process (Funk et al., 2015), which may explain the underestimation of extreme events. Similar results were found by Beck et al. (2017), who used hydrological modelling to evaluate the accuracy of R datasets and determined that CHIRPS tended to perform better than the other tested datasets in central and eastern Brazil, but it
humidity, increased air temperature and daytime convection (Fitzjarrald et al., 2008; Petersen et al., 2002). This type of precipitation generally occurs at scales on the order of a few kilometres, so they may not be well represented by CHIRPS because of the database grid. As can be observed in Table 3, MAE is usually lower in locations with lower rainfall. The lowest MAEs are observed from June to August in the southern stations and from September to November in the eastern stations, when and where the mean monthly rainfall is lower than 300 mm. From April to August, the BLA exhibits a rainfall distribution with higher values in the north of the region than in the south, as shown in Fig. 1. Therefore, MAE exhibits a strong correlation with both latitude and rainfall. The MAE is greater in the months with higher precipitation, especially in March, which presents mean monthly precipitation of approximately 320 mm and MAE of approximately 60 mm. Tian and Peters-Lidard (2010) observed higher uncertainties of the satellite-based precipitation over water bodies, which is important in the context of BLA, due to the presence of the flood plain and Amazon river delta. This result can explain the high values of MAE observed in months with high precipitation (February to May) at the stations in low Amazonas and near Marajó Bay. 4.3. Annual rainfall indices The annual time series of mean annual rainfall indices for all the selected stations in BLA are shown in Fig. 10. CHIRPS product produces more similar results to rain gauge data for the indices PRCPTOT, nP and R95pad, while strong underestimate the most extreme rainfall indices (R50mm, Rx1day, Rx5days). When analysing both biomes separated, CHIRPS product represents better the time series nP and R20mm in the Amazon biome than in Cerrado, where it overestimates and underestimates these indices, respectively (Figs. S3 and S4 in the Supplemental File). The greatest correlations between INMET and CHIRPS data were observed for PRCPTOT, nP, R10mm, R20mm and R95pad (Fig. 11). The correlations for these indices were higher than 0.75 in 34, 11, 19, 10 and 9 of the 45 stations, respectively. In general, the RHOs were greater at the stations in eastern Amazonia, including the stations with lower mean values for these indices but also the stations near the ocean, 8
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Fig. 10. Annual mean annual rainfall indices of the 45 selected stations calculated from rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017. 9
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Fig. 11. Spatial distribution of Pearson's linear correlation coefficient for annual rainfall indices calculated for rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017 at the 45 selected rain gauge stations.
Fig. 12. As in Fig. 11 but for mean error.
days with R less than 1 mm) appeared in Tocantins and Maranhão states, on the eastern part of the BLA (Fig. 13). Espinoza et al. (2019) showed a significant increase in CDD in the Bolivian Amazon and central Peruvian Amazon (not assessed in this paper), and a significant increase (p < .01) in the southern Brazilian Amazon, which was not observed in our results. Regarding CWD (the maximum number of consecutive days with R greater than 1 mm), our results showed a significant trend (p < .05) in Tocantins and Maranhão states (i.e., the eastern BLA) but also in western Pará state (i.e., the northern BLA). The result in the northern BLA was similar to that in Espinoza et al. (2019), which also indicated a significant increase in the maximum number of consecutive days with R greater than 1 mm in the northern Brazilian
underestimate the peak magnitude and present spurious drizzle for daily temporal resolution in tropical regions. The difference in the 24-h interval of daily rainfall between the datasets can also influence the results. The underestimation of the extreme rainfall indices indicate that the use of CHIRPS data is not recommended to determinate the design rainfall intensity for dimensioning hydraulic structures in the region.
4.4. Trends in rainfall indices Regarding the trends observed with the rain gauge data, a significant trend (p < .05) in CDD (the maximum number of consecutive 10
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Fig. 13. Location of the stations with significant trends (p < .05) detected for rain gauge (x) and CHIRPS (+) data for the annual rainfall indices obtained using the data from rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017. Yellow circles indicate stations with no significant trend. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
rainfall indices observed using rain gauge data, with exception for CDD, although CDD determined using CHIRPS data did not correlate well with the values obtained using rain gauge stations. Therefore, the use of CHIRPS product is not indicated to analyse annual trends in extreme rainfall indices in BLA.
Amazon, especially north of 5°S. Although the CHIRPS product did not represent well most of the extreme precipitation indexes, we also analysed the trends in rainfall indices, since CHIRPS data are not designed to provide the best instantaneous accuracy but rather to achieve the most temporally homogeneous record (Funk et al., 2015). When compared to the use of rain gauge data, the use of CHIRPS data could not detect significant trends at the same stations for most of the rainfall indices (Fig. 12). However, for sub-Saharan Africa, Harrison et al. (2019) found that CHIRPS V.2, along with TRMM 3B42, ranked highest among the products tested for identifying changes in precipitation extremes. The best results were obtained for CDD and for Rx5days. For CDD, the use of CHIRPS data indicates significant trends in 2 of the 3 stations with trends according to the use of rain gauge data and with a similar magnitude (Supplementary Material, Fig. S7). This is in line with the indication of using CHIRPS for drought analysis (Paredes-Trejo et al., 2017). For Rx5days, CHIRPS indicate significant trends in 2 of 4 stations. However, for the annual total precipitation (PRCPTOT), the analysis with CHIRPS data did not detect significant trends in 10 locations that exhibited significant trends with rain gauge data, although the trends present low magnitude.
Data availability The data supporting the conclusions can be obtained from the references, tables, figures and specified sites. CHIRPS data were obtained from http://chg.geog.ucsb.edu/data/chirps/. The meteorological data from INMET are available at www.inmet.gov.br. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001.
5. Conclusion The use of daily CHIRPS data with a 0.05° grid provides mean monthly rainfall similar to that obtained using data from the rain gauge stations situated inside the BLA for the same locations, but CHIRPS data tend to underestimate the values for the rainiest months. The correlations between monthly precipitation were considered good, with the exception of the western states (Acre and Amazonas), which also presented the higher percentage of missing monthly data. The lower RHO observed near the coastline during the dry season may be due to a higher uncertainty of the satellite estimations in this region and over water bodies. CHIRPS product also underestimate the extreme rainfall indices. The more extreme the index was, the lower the correlation. Therefore, the use of CHIRPS product is not recommended for studies of extreme rainfall events in the BLA or for design hydraulic structures or flood studies. The use of CHIRPS data does not well represent the trends in
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosres.2020.104879. References Baez-Villanueva, O.M., Zambrano-Bigiarini, M., Ribbe, L., Nauditt, A., Giraldo-Osorio, J.D., Thinh, N.X., 2018. Temporal and spatial evaluation of satellite rainfall estimates over different regions in Latin-America. Atmos. Res. 213. https://doi.org/10.1016/j. atmosres.2018.05.011. Bai, L., Shi, C., Li, L., Yang, Y., Wu, J., 2018. Accuracy of CHIRPS satellite-rainfall products over mainland China. Remote Sens. 10, 362. https://doi.org/10.3390/ rs10030362. Bayissa, Y., Tadesse, T., Demisse, G., Shiferaw, A., 2017. Evaluation of satellite-based rainfall estimates and application to monitor meteorological drought for the upper
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Evaluation of extreme rainfall indices from CHIRPS precipitation estimates over the Brazilian Amazonia
T
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Rosane Barbosa Lopes Cavalcantea, , Douglas Batista da Silva Ferreiraa, Paulo Rógenes Monteiro Pontesa, Renata Gonçalves Tedeschia, Cláudia Priscila Wanzeler da Costaa, Everaldo Barreiros de Souzab a b
Instituto Tecnológico Vale – Desenvolvimento Sustentável, Brazil Universidade Federal do Pará, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: CHIRPS Extreme rainfall Rainfall trends Amazon
Since several datasets are available with marked differences, the assessment of precipitation data is a key aspect to support the choice of the most adequate precipitation product for a certain research or operational application. In the present study, we evaluated the use of the daily rainfall dataset from CHIRPS with spatial resolution of 0.05° for different purposes in the states of the Brazilian Legal Amazon. We compared monthly rainfall, annual rainfall indices and their trends calculated using CHIRPS data and rain gauge observations with a point-to-pixel analysis. The use of daily CHIRPS data provided mean monthly rainfall similar to that obtained using data from the rain gauge stations, but CHIRPS data tend to underestimate the values for the rainiest months. The correlation was usually lower in the western Amazon, especially during its rainy season. The same underestimation was observed for extreme rainfall indices. CHIRPS product produces more similar results to rain gauge data for the indices PRCPTOT, nP, and R95pad, while strong underestimate the most extreme rainfall indices (R50mm, Rx1day, Rx5days). For the 45 stations and 15 rainfall indices analysed, 63 significant trends were detected using rain gauge data, of which only 13 were detected using CHIRPS product. Therefore, the use of CHIRPS data does not well represent the trends in rainfall indices.
1. Introduction Rainfall information is an important input for different applications, such as agriculture, hydrology, climate, management of water resources, design of hydraulic structures, drought and flood risk assessment and forecasting, and ecological modelling. In recent decades, several gridded precipitation datasets have been developed with different design objectives, data sources, spatial resolutions, spatial coverage, published temporal resolutions, temporal spans, and latencies (Beck et al., 2017; Sun et al., 2018). Exclusively gauge-based datasets tend to show lower performances in regions with sparse rain gauge networks since precipitation features high spatial variation (Sun et al., 2018). The advancement of remote sensing instruments and precipitation retrieval algorithms has made available a series of satellite-based precipitation products. These products use information from visible/ infrared sensors on geostationary and low Earth orbit satellites, passive or active microwave sensors on low Earth orbit satellites, and combinations of both (Kidd and Levizzani, 2011; Sun et al., 2018). The
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satellite-based precipitation estimates have significant uncertainty (Tian and Peters-Lidard, 2010) because none of the satellite sensors detect rainfall as such, and further merging or blending with rain gauge information can improve precipitation products. Since several datasets are available, the assessment of precipitation data is a key aspect to support the choice of the most adequate precipitation product for a certain research or operational application (Paredes Trejo et al., 2016). Many studies evaluate the advantages and limitations of the available datasets and compare their results (see review by Karimi and Bastiaanssen, 2015), and marked differences have been found even among datasets employing the same data sources (Beck et al., 2017). The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) provides blended gauge-satellite precipitation spanning 50°S50°N from 1981 to near the present. CHIRPS are provided at spatial resolutions of 0.25° and 0.05° and daily to annual temporal resolutions with a short latency (Funk et al., 2015). According to the authors, this dataset can be used for trend analysis and seasonal drought monitoring.
Corresponding author at: R. Boaventura da Silva, 955 - Nazaré, Belém, PA, Brazil. E-mail address: [email protected] (R.B.L. Cavalcante).
https://doi.org/10.1016/j.atmosres.2020.104879 Received 25 October 2019; Received in revised form 27 January 2020; Accepted 29 January 2020 Available online 30 January 2020 0169-8095/ © 2020 Elsevier B.V. All rights reserved.
Atmospheric Research 238 (2020) 104879
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associated primarily with the quasi-stationary atmospheric patterns of the South Atlantic Convergence Zone (Figueroa and Nobre, 1990; de Souza et al., 2000; Souza and Ambrizzi, 2003; Carvalho et al., 2004). On the other hand, the configuration of the maximum rainfall observed during the fall months (March to May) shows a zonal elongated band distributed across the northern portion of the region. Precipitation of this maximum is mainly induced by the Intertropical Convergence Zone, which reaches its southern position during the months of March/ April (Hastenrath and Heller, 1977; Nobre and Srukla, 1996; Souza et al., 1998; Xavier et al., 2000; Souza et al., 2004). Interannual variability is related to the El Niño-Southern Oscillation and to the sea surface temperature gradient over the tropical Atlantic (Marengo et al., 1993; Yoon and Zeng, 2010). Negative rainfall trends have been identified for the entire Amazon basin after 1975, while at the regional level, the trend is negative in northern Amazonia and positive in southern Amazonia (Marengo, 2004; Espinoza Villar et al., 2009). Chagnon (2004) suggest that current deforestation in the Amazon has already altered the regional climate of deforested areas. Espinoza et al. (2019) used CHIRPS and HOP (interpolated HYBAN observed precipitation) to show that dry-day frequency has significantly increased in the southern Amazon, Bolivian Amazon, central Peruvian Amazon and southern Brazilian Amazon. Similarly, the wet-day frequency has significantly increased in the northern Amazon.
Validation studies have been conducted in different regions of the globe, e.g., Cyprus (Katsanos et al., 2016), Argentina (Rivera et al., 2018), Nepal (Shrestha et al., 2017), Italy (Duan et al., 2016), Chile (Zambrano et al., 2017), China (Bai et al., 2018; Gao et al., 2018; Zhong et al., 2019; Lai et al., 2019), Burkina Faso (Dembélé and Zwart, 2016), Mozambique (Toté et al., 2015), the Tibetan Plateau (Wu et al., 2019), India (Prakash, 2019), Ethiopia (Duan et al., 2019; Bayissa et al., 2017), Myanmar (Sirisena et al., 2018), Venezuela (Paredes Trejo et al., 2016), and in other regions of South America (Baez-Villanueva et al., 2018; Funk et al., 2015). In these studies, the evaluation of CHIRPS products is usually accomplished by comparing rainfall indices with other datasets and with in situ observations at different temporal scales using different performance metrics. The most common purposes are drought monitoring, rainfall detection, and streamflow simulation. The results show that CHIRPS performs satisfactorily in almost all studies and better than other datasets in many cases. In Brazil, Beck et al. (2017), using hydrological modelling to evaluate the accuracy of precipitation datasets, found that CHIRPS V2.0 tended to perform better than the other tested datasets in central and eastern Brazil and is a viable choice for daily temporal resolution in tropical regions if the peak magnitude underestimation and spurious drizzle are not critical. Funk et al. (2015) showed that the wet season correlations between the CHIRPS and the Global Precipitation Climatology Center (GPCC) are lower in western Amazonia than in other regions of Brazil. For northeastern Brazil, CHIRPS correlates well with monthly rain gauge data but tends to overestimate low and underestimate high rainfall values, and rain detection is poor in semiarid areas (Paredes-Trejo et al., 2017). (Costa et al., 2019), when comparing monthly precipitation data from CHIRPS 2.0 with rain station data from 1998 to 2010, found the largest spatial differences in precipitation in northwestern Amazonas and southwestern Pará. Correa et al. (2017) used hydrological modelling and rainfall datasets as input data to a hydrological model in order to analyse past floods and droughts in the Amazon River Basin. Using different performance metrics, the authors showed that CHIRPS V2.0 is one of the best rainfall datasets for the region, probably because this dataset not only is based on atmospheric models but also uses in situ rainfall information. The aim of this study is to evaluate the use of a rainfall dataset from CHIRPS for different purposes in the states of the Brazilian Legal Amazon. We compared monthly rainfall, annual rainfall indices and their trends calculated using CHIRPS and gauge observations. Although Beck et al. (2017) criticize studies that re-use gauge observations already incorporated into rainfall datasets to determine their accuracy, in the case of CHIRPS data, even in the presence of co-located stations, the CHIRPS have some influence from the CHIRP (exclusively satellitebased data) (Funk et al., 2015). The results of this study will provide bases for decision makers on the applicability of these data products for different meteorological studies and the probability of occurrence of extreme events.
3. Material and methods We used a point-to-pixel analysis to compare the rainfall data from rain gauge stations in the BLA and CHIRPS data. This methodology has been widely used in assessing rainfall estimated by remote sensing (e.g., Baez-Villanueva et al., 2018; Paredes-Trejo et al., 2017) and avoids errors due to the spatial interpolation of sparsely located and unevenly distributed rain gauges. The CHIRPS rainfall data series was created by extracting the daily rainfall estimates over the pixel in which each selected rain gauge station is located. The days with missing data in the rain gauge stations were also considered as missing data in the CHIRPS data series for comparison. Although the data from rain gauge stations were already incorporated into the CHIRPS dataset, in order to adjust the bias of CHIRP data, the final CHIRPS estimate is a combination of unadjusted and bias-adjusted CHIRP data (Funk et al., 2015). We analysed the spatial variations in the differences in monthly rainfall and annual extreme rainfall indices between the two data series (rain gauge stations and CHIRPS). The monthly scale was selected instead of the daily scale due to the different periods of 24 h used to calculate daily rainfall of the two data types. The performance of the CHIRPS product in estimating monthly rainfall and rainfall indices was evaluated by using the Pearson correlation coefficient (RHO), the mean error (ME), the mean absolute error (MAE) and the mean relative error (MRE) (Table 1). The rainfall indices (Table 2) were selected based on the core set of descriptive indices of extreme precipitation defined by the Expert Team on Climate Change Detection and Indices (Karl et al., 1999). The indices were calculated annually. We included the all-day percentile, in addition to the wet-day percentiles, to evaluate changes in frequency as suggested by Schär et al. (2016) and the number of days with rainfall (R) since it is used to calculate other indices. We also compared the detection of trends in the annual series of extreme rainfall indices using the CHIRPS data and the rain gauge data. Trend analyses are useful for planning studies and assessments of land cover and climate change impacts. The annual trends in extreme rainfall indices were analysed by using the modified nonparametric MannKendall test (Hamed and Rao, 1998; Kendall, 1955; Mann, 1945) to investigate the statistical significance at a significance level of 0.05. The magnitude of the change over time was estimated using the nonparametric Sen's slope estimator (Sen, 1968). The Mann-Kendall test and Sen's slope estimator have been widely used for to effectively identify
2. Study region The study area is the territory of the nine Brazilian states that contain areas in the Amazon biome and compose the Brazilian Legal Amazon (BLA), covering 5 million km2. Inside the BLA, the Arc of Deforestation is present, a region where most of the natural land cover (forest) has been replaced by pasturelands since 1970. To protect natural forest areas, the Brazilian government recognized several indigenous lands and created conservation units. Approximately 44% of the BLA territory is covered by these areas (Veríssimo et al., 2011). The Amazon region is characterized by high annual rainfall, with an average between 1400 mm and 3000 mm; the two regions with the highest values (precipitation above 2000 mm) are located distinctly over the northwestern and northeastern portions (Fig. 1). Analysing the contribution of precipitation during the summer months (December to February), the maximum rainfall is oriented northwest-southeast and is 2
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Fig. 1. Location of the National Institute of Meteorology (INMET) stations in the states of the Brazilian Legal Amazon and mean seasonal precipitation estimated by CHIRPS. Table 1 Equations for the statistical metrics used to evaluate CHIRPS monthly rainfall estimation and annual indices. The CHIRPS and rain gauge data are represented by x and y, respectively. The number of points is represented by n. Name Pearson correlation coefficient Mean error
Table 2 Description of the rainfall indices used.
Formula n (∑ xy ) − (∑ x )(∑ y )
RHO =
ME =
1 n
Mean absolute error
MAE =
Mean relative error
MRE =
Symbol
Description
nP Rx1day SDII
Number of days with R ≥ 1 mm Maximum 1-day precipitation Simple precipitation intensity index: mean daily precipitation intensity of days with R ≥ 1 mm Maximum consecutive 5-day precipitation Count of days when R ≥ 10 mm Count of days when R ≥ 20 mm Count of days when R ≥ 50 mm Maximum length of dry spell: maximum number of consecutive days with R < 1 mm Maximum length of wet spell: maximum number of consecutive days with R ≥ 1 mm Total precipitation when R > 95 percentile of days with R ≥ 1 mm Total precipitation when R > 99 percentile of days with R ≥ 1 mm Total precipitation when R > 95 percentile of all days Total precipitation when R > 99 percentile of all days Annual total precipitation Sum of precipitation of the 4 days with highest R
[n ∑ x 2 − (∑ x )2][n ∑ y 2 − (∑ y )2]
Rx5days R10mm R20mm R50mm CDD
∑ (x − y ) 1 n 1 n
∑ |x − y|
∑
(x − y ) y
CWD
monotonic trends in extreme rainfall time series worldwide (for example, Gocic and Trajkovic, 2013; Shahid, 2011; da Silva et al., 2015; Pingale et al., 2014; Madsen et al., 2014).
R95p R99p R95pad R99pad PRCPTOT RTop4
3.1. Rain gauge data Daily rainfall observations were provided by the Brazilian National Institute of Meteorology (INMET; available online at www.inmet.gov. br). All the stations for the states of the BLA are listed in Supplementary Material (Table S1). The average is 0.012 stations per 1000 km2, and
they are unevenly distributed (Fig. 1), which motivated the evaluation of satellite-based precipitation products in the region. The stations are concentrated in the eastern portion of BLA, where deforestation is older 3
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Fig. 2. Mean monthly precipitation from 1981 to 2017 at the 45 selected rain gauge stations.
in the analysis. 3.2. CHIRPS data We used the latest version of CHIRPS (CHIRPS V.2) obtained from the website (http://chg.geog.ucsb.edu/data/chirps/) for the years 1981 to 2017. The data used were the daily rainfall with a resampled spatial resolution of 0.05°. The satellite-based CHIRP data are produced by the infrared cold cloud duration (CCD) data calibrated with tropical rainfall measuring mission (TRMM) 3B42 data to generate the pentad precipitation estimates. The result is expressed as a fraction of the normal and multiplied by the climatological normal to remove the systematic bias. The CHIRP product is blended with rain gauge station data using a modified inverse distance weighting algorithm to produce the CHIRPS. Daily disaggregation is produced using daily CCD and daily coupled forecast system (CFS) data with a simple proportional method (Funk et al., 2015).
Fig. 3. Percentage of missing data per month from 1981 to 2017 at the 45 selected rain gauge stations.
4. Results and discussion and the population is higher. In the central and western BLA, the stations are mostly distributed along the main rivers. We visually checked the daily and monthly data series to identify obviously incorrect values. Outliers were identified as the mean value of observations for the month plus or minus four times the standard deviation of the value for the month in the entire series. This method was used for accumulated monthly rainfall, maximum daily rainfall, and consecutive numbers of days without rain. The monthly values outside these thresholds were manually checked by comparison with other stations or with registered extreme events. Months with more than one day of missing daily data, including values that were found to be suspect or incorrect after undergoing quality control, were considered missing data. This is less strict than the recommendation of WMO (2017) for the calculation of climate normals, which defines that total precipitation can only be calculated if there are complete data over the month. The stations with less than 20% missing monthly data from 1981 to 2017 (period with CHIRPS data) were used
4.1. Monthly rainfall and annual indices from rain gauges A total of 45 of the 62 INMET's stations located in BLA presented less than 20% of missing monthly rainfall data from 1981 to 2017 and were used in the study. The majority of the stations are located in the Amazon biome, but 12 stations in the eastern and southeastern portions of the study area are located in Cerrado (tropical savanna). The mean monthly rainfall of each INMET stations are shown in Fig. 2. The missing monthly values were well distributed between the months varying from 7.5% of the total missing monthly occurring in May to 10.2% in February. For the 37 years of the analysed period, the maximum standard deviation of the monthly total missing data calculated for each station was 2.7 data. In general, the stations with higher percentages of missing data are located in the western Amazon (Fig. 3). The mean annual rainfall indices calculated for rain gauge observations are shown in Fig. 4. The stations in the eastern BLA presented lower annual total precipitation (PRCPTOT) and higher 4
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Fig. 4. Mean annual rainfall indices calculated for rain gauge observations from 1981 to 2017 at the 45 selected rain gauge stations.
Fig. 5. Monthly rainfall for rain gauge observations and CHIRPS estimates (N = 18,769) from 1981 to 2017 at the locations of the 45 selected rain gauge stations. The dashed line indicates 1:1.
correlates (p-value < .01) with monthly rainfall observed in the rain gauge stations. The minimum RHO for the stations in the Amazon biome was 0.66, and for the Cerrado biome was 0.91. However, CHIRPS data underestimate the high values of monthly precipitation, especially the most extreme monthly precipitation registered by the rain gauge observations (Fig. 5). This pattern can be seen in the cumulative curves presented in Fig. 6, which show a higher frequency for precipitation estimated by CHIRPS data than by rain gauge data for monthly rainfall greater than 240 mm and a lower frequency of monthly precipitation lower than 10 mm. The underestimation of the higher values of monthly rainfall from CHIRPS observed in the present study was also observed by Paredes-Trejo et al. (2017), who used 21 rain gauge stations in northeastern Brazil, 12 of which were included in the present study and located in the state of Maranhão.
consecutive dry days (CDD), indicating the presence of a strong dry season, especially in the south-eastern. They also presented a lower number of days with rain in total (nP), and number of days with rainfall higher than 10 mm, 20 mm, 50 mm, and 95 and 99 percentile of days with rain (R10mm, R20mm, R50mm, R95p, and R99p). The mean number of maximum consecutive wet days (CWD) are higher in the northern locations, especially close to the Amazon delta. No spatial pattern was observed for the indexes R95pad, R99pad, and SDII, which presented the lowest ranges of variation throughout the BLA weather stations. 4.2. Monthly rainfall Monthly rainfall calculated with CHIRPS product significantly 5
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Fig. 6. Empirical cumulative distribution of monthly rainfall for rain gauge observations and corresponding CHIRPS estimates (N = 18,769) from 1981 to 2017 for all stations and separated by biome.
Fig. 7. Box-plots of mean monthly rainfall at the 45 selected rain gauge stations (top), at the 33 stations in Amazon biome (middle) and at the 12 stations in Cerrado biome (bottom) for monthly rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017. The red crosses indicate the outliers. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
low correlation in western Amazonia for the wet season. This pattern can also be noted in the analyse of the correlation between RHO and longitude (positive values) and rainfall (negative values) (Table 3). The highest values are concentrated in the eastern BLA in the beginning (December and January) and end (April and May) of the rainy season. As result, when analysing by biomes, the monthly mean RHO for the Amazon stations were greater than for Cerrado, except for July, when it is 1% lower. The greatest differences are in March and May, when mean RHO are 20% and 19% greater for Amazon biome than for Cerrado. However, lower values are observed in the northeastern portion at the station closest to the Atlantic Ocean (Figure) in the driest months in this region (July to September). A reduced performance in the dry
Considering the mean monthly precipitation from 1981 to 2017 for all pixels with rain gauge stations, the CHIRPS monthly mean value and variability for the BLA were similar to those for rain gauge stations for the entire BLA and for both biomes (Figs. 7 and 8). Fig. 9 shows the monthly values of RHO for each rain gauge station. The maps for ME and MAE are presented in the Supplemental Material (Figs. S1 and S2). ME is predominantly negative from February to June. Due to the months with precipitation equal to zero, MRE is not calculated. The RHO tends to decrease with increased R. RHO is usually lower in the western portion of the study area, especially in the rainy season in the region: from December to May. Funk et al. (2015) also showed a 6
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Fig. 8. Annual mean monthly rainfall at the 45 selected rain gauge stations (top), at the 33 stations in Amazon biome (middle), and at the 12 stations in Cerrado biome (bottom) for monthly rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017.
Fig. 9. Spatial distribution of Pearson's linear correlation coefficient for monthly rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017 at the 45 selected rain gauge stations.
season was also observed by Paredes Trejo et al. (2016) in Venezuela. According to Tian and Peters-Lidard (2010), the satellite-based precipitation uncertainty is greater in southern and eastern areas for
June–July August and in eastern areas and those closer to the ocean for September–October-November. Additionally, during the dry season in the Amazon, precipitation is due to local factors associated with high 7
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Table 3 Correlation coefficients between statistical measures (RHO, MAE, and ME) for each month and rain gauge station characteristics: latitude (Lat), longitude (Lon), elevation (Ele), and annual mean rainfall (R). The colours highlight the values from −1 (red) to +1 (blue).
RHO Lat
Lon
Ele
MAE R
Lat
Lon
Ele
ME R
Lat
Lon
Ele
R
Jan
0.22 0.43 -0.11 -0.25 -0.08 -0.30 -0.04 0.53 -0.17 -0.09
0.27 -0.02
Feb
0.23 0.38 -0.10 -0.27 -0.07 -0.18 -0.05 0.45 -0.20
0.22 -0.17
Mar
0.26 0.51 -0.02 -0.25
0.07 -0.19 -0.14 0.49 -0.15 -0.01
0.19
Apr
0.11 0.62
0.54 -0.14 -0.54 0.68 -0.22
0.26 -0.09
0.02 -0.41
0.19 0.06
0.03
May -0.07 0.57 -0.01 -0.47
0.80 -0.24 -0.71 0.63 -0.08 -0.19
0.02
0.35
Jun
0.00 0.37 -0.05 -0.36
0.76 -0.43 -0.65 0.76
0.02 -0.09
0.00
0.30
Jul
0.02 0.22 -0.18 -0.26
0.74 -0.43 -0.65 0.79
0.07 -0.19 -0.04
0.40
Aug
-0.05 0.28 -0.02 -0.35
0.63 -0.60 -0.55 0.73
0.19 -0.15 -0.13
0.49
Sep
-0.17 0.19
0.13 -0.67 -0.06 0.55
0.10 -0.02 -0.17
0.31
Oct
0.04 0.49
Nov Dec
0.14 -0.41
0.03 -0.31 -0.21 -0.68
0.15 0.29 -0.09 -0.16
0.07
0.20
0.17 0.36 -0.20 -0.10 -0.31 -0.58
0.24 0.15 -0.10
0.11
0.06 -0.06
0.23 0.63 -0.19 -0.30 -0.12 -0.31
0.10 0.22 -0.15
0.11
0.23 -0.05
which present higher mean values. As result, the mean RHO for the stations located in the Cerrado biome was greater than for the Amazon Biome for all the indices. The lowest RHO values were observed for CWD and for the most extreme rainfall indices: Rx1day and R99p. However, some stations with good correlations presented high ME (Fig. 12), MAE, and MRE (Figs. S5 and S6). In some cases, one station presented errors much higher than the other stations: a MAE of 546.5 m for PRCPTOT, 54.3 days for nP, and 10 days for CWD were observed. In the first two cited cases, the referenced station presented a good correlation between the indices obtained with CHIRPS and INMET. At almost all stations, CHIRPS data overestimate (positive ME) R10mm, but underestimate (negative ME and MRE) the most extreme precipitations (R50mm, RTop4, Rx1day, and Rx5days) (Fig. 12), as previously observed for monthly rainfall. This dataset also tends to underestimate CWD and nP at the northern stations, especially in the state of Pará, and overestimate nP in southeastern Amazonia. In general, lower ME and MRE for CWD were observed in the stations with higher mean CWD. In total, the mean nP and CWD were underestimated at 23 and 24 of the 45 stations, respectively. The nP error was reflected in the indices calculated using the days with rain (R95p, R99p and SDII). The ME for CDD was greater (but negative) at the eastern stations, which presented the highest mean CDDs. A few studies analysed the performance of CHIRPS data to estimate rainfall indices. For Ghana (Larbi et al., 2018) and Cyprus (Katsanos et al., 2016), CHIRPS data correlate very well with the available station data. For Venezuela, Paredes Trejo et al. (2016) also observed underestimation of more extreme rainfall, and the authors highlighted that the CHIRPS V.2 product had a low capability of rain detection, especially during the rainy season, due to its tendency to misclassify rainfall events. CHIRPS data always use CHIRP original data, and extreme values at gauge stations are considered standardized anomalies; very large absolute values and large ratios in the CHIRP original data are not used in the CHIRPS blending process (Funk et al., 2015), which may explain the underestimation of extreme events. Similar results were found by Beck et al. (2017), who used hydrological modelling to evaluate the accuracy of R datasets and determined that CHIRPS tended to perform better than the other tested datasets in central and eastern Brazil, but it
humidity, increased air temperature and daytime convection (Fitzjarrald et al., 2008; Petersen et al., 2002). This type of precipitation generally occurs at scales on the order of a few kilometres, so they may not be well represented by CHIRPS because of the database grid. As can be observed in Table 3, MAE is usually lower in locations with lower rainfall. The lowest MAEs are observed from June to August in the southern stations and from September to November in the eastern stations, when and where the mean monthly rainfall is lower than 300 mm. From April to August, the BLA exhibits a rainfall distribution with higher values in the north of the region than in the south, as shown in Fig. 1. Therefore, MAE exhibits a strong correlation with both latitude and rainfall. The MAE is greater in the months with higher precipitation, especially in March, which presents mean monthly precipitation of approximately 320 mm and MAE of approximately 60 mm. Tian and Peters-Lidard (2010) observed higher uncertainties of the satellite-based precipitation over water bodies, which is important in the context of BLA, due to the presence of the flood plain and Amazon river delta. This result can explain the high values of MAE observed in months with high precipitation (February to May) at the stations in low Amazonas and near Marajó Bay. 4.3. Annual rainfall indices The annual time series of mean annual rainfall indices for all the selected stations in BLA are shown in Fig. 10. CHIRPS product produces more similar results to rain gauge data for the indices PRCPTOT, nP and R95pad, while strong underestimate the most extreme rainfall indices (R50mm, Rx1day, Rx5days). When analysing both biomes separated, CHIRPS product represents better the time series nP and R20mm in the Amazon biome than in Cerrado, where it overestimates and underestimates these indices, respectively (Figs. S3 and S4 in the Supplemental File). The greatest correlations between INMET and CHIRPS data were observed for PRCPTOT, nP, R10mm, R20mm and R95pad (Fig. 11). The correlations for these indices were higher than 0.75 in 34, 11, 19, 10 and 9 of the 45 stations, respectively. In general, the RHOs were greater at the stations in eastern Amazonia, including the stations with lower mean values for these indices but also the stations near the ocean, 8
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Fig. 10. Annual mean annual rainfall indices of the 45 selected stations calculated from rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017. 9
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Fig. 11. Spatial distribution of Pearson's linear correlation coefficient for annual rainfall indices calculated for rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017 at the 45 selected rain gauge stations.
Fig. 12. As in Fig. 11 but for mean error.
days with R less than 1 mm) appeared in Tocantins and Maranhão states, on the eastern part of the BLA (Fig. 13). Espinoza et al. (2019) showed a significant increase in CDD in the Bolivian Amazon and central Peruvian Amazon (not assessed in this paper), and a significant increase (p < .01) in the southern Brazilian Amazon, which was not observed in our results. Regarding CWD (the maximum number of consecutive days with R greater than 1 mm), our results showed a significant trend (p < .05) in Tocantins and Maranhão states (i.e., the eastern BLA) but also in western Pará state (i.e., the northern BLA). The result in the northern BLA was similar to that in Espinoza et al. (2019), which also indicated a significant increase in the maximum number of consecutive days with R greater than 1 mm in the northern Brazilian
underestimate the peak magnitude and present spurious drizzle for daily temporal resolution in tropical regions. The difference in the 24-h interval of daily rainfall between the datasets can also influence the results. The underestimation of the extreme rainfall indices indicate that the use of CHIRPS data is not recommended to determinate the design rainfall intensity for dimensioning hydraulic structures in the region.
4.4. Trends in rainfall indices Regarding the trends observed with the rain gauge data, a significant trend (p < .05) in CDD (the maximum number of consecutive 10
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Fig. 13. Location of the stations with significant trends (p < .05) detected for rain gauge (x) and CHIRPS (+) data for the annual rainfall indices obtained using the data from rain gauge observations and corresponding CHIRPS estimates from 1981 to 2017. Yellow circles indicate stations with no significant trend. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
rainfall indices observed using rain gauge data, with exception for CDD, although CDD determined using CHIRPS data did not correlate well with the values obtained using rain gauge stations. Therefore, the use of CHIRPS product is not indicated to analyse annual trends in extreme rainfall indices in BLA.
Amazon, especially north of 5°S. Although the CHIRPS product did not represent well most of the extreme precipitation indexes, we also analysed the trends in rainfall indices, since CHIRPS data are not designed to provide the best instantaneous accuracy but rather to achieve the most temporally homogeneous record (Funk et al., 2015). When compared to the use of rain gauge data, the use of CHIRPS data could not detect significant trends at the same stations for most of the rainfall indices (Fig. 12). However, for sub-Saharan Africa, Harrison et al. (2019) found that CHIRPS V.2, along with TRMM 3B42, ranked highest among the products tested for identifying changes in precipitation extremes. The best results were obtained for CDD and for Rx5days. For CDD, the use of CHIRPS data indicates significant trends in 2 of the 3 stations with trends according to the use of rain gauge data and with a similar magnitude (Supplementary Material, Fig. S7). This is in line with the indication of using CHIRPS for drought analysis (Paredes-Trejo et al., 2017). For Rx5days, CHIRPS indicate significant trends in 2 of 4 stations. However, for the annual total precipitation (PRCPTOT), the analysis with CHIRPS data did not detect significant trends in 10 locations that exhibited significant trends with rain gauge data, although the trends present low magnitude.
Data availability The data supporting the conclusions can be obtained from the references, tables, figures and specified sites. CHIRPS data were obtained from http://chg.geog.ucsb.edu/data/chirps/. The meteorological data from INMET are available at www.inmet.gov.br. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) Finance Code 001.
5. Conclusion The use of daily CHIRPS data with a 0.05° grid provides mean monthly rainfall similar to that obtained using data from the rain gauge stations situated inside the BLA for the same locations, but CHIRPS data tend to underestimate the values for the rainiest months. The correlations between monthly precipitation were considered good, with the exception of the western states (Acre and Amazonas), which also presented the higher percentage of missing monthly data. The lower RHO observed near the coastline during the dry season may be due to a higher uncertainty of the satellite estimations in this region and over water bodies. CHIRPS product also underestimate the extreme rainfall indices. The more extreme the index was, the lower the correlation. Therefore, the use of CHIRPS product is not recommended for studies of extreme rainfall events in the BLA or for design hydraulic structures or flood studies. The use of CHIRPS data does not well represent the trends in
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.atmosres.2020.104879. References Baez-Villanueva, O.M., Zambrano-Bigiarini, M., Ribbe, L., Nauditt, A., Giraldo-Osorio, J.D., Thinh, N.X., 2018. Temporal and spatial evaluation of satellite rainfall estimates over different regions in Latin-America. Atmos. Res. 213. https://doi.org/10.1016/j. atmosres.2018.05.011. Bai, L., Shi, C., Li, L., Yang, Y., Wu, J., 2018. Accuracy of CHIRPS satellite-rainfall products over mainland China. Remote Sens. 10, 362. https://doi.org/10.3390/ rs10030362. Bayissa, Y., Tadesse, T., Demisse, G., Shiferaw, A., 2017. Evaluation of satellite-based rainfall estimates and application to monitor meteorological drought for the upper
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