Application of ERA5-Land reanalysis data in climate risk zoning for maize in the state of Bahia - Brazil

doi:10.21203/rs.3.rs-2433939/v1

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Research Article

Application of ERA5-Land reanalysis data in climate risk zoning for maize in the state of Bahia - Brazil

https://doi.org/10.21203/rs.3.rs-2433939/v1

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published 06 Oct, 2023

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Agricultural Zoning of Climate Risk (AZCR) is a fundamental tool for agricultural activities because it identifies regions and times of lower climate risk for planting and sowing crops, aiming to minimize the agricultural losses arising from climate hazards. In Brazil, AZCR has been used since 1996 as an official agricultural policy instrument. However, optimal AZCR representative of large areas require a network of meteorological stations with excellent spatial distribution, routine and accurate observations, and reliable data. Precipitation gauges that perform reliable measurements of precipitation have a good distribution in the Northeast region of Brazil. However, weather stations in this region are scarce. When weather stations are absent at a given location, input variables for AZCR are interpolated, which can lead to errors. Thus, seeking to solve the problem of the shortage of weather stations and improve the spatial distribution of information on meteorological variables, this study aimed to validate data from the ERA5-Land reanalysis of the European Center for Medium-Range Weather Forecasts and the CPC Precipitation Project. Once the data were validated, an AZCR was performed for the maize crop in the entire state of Bahia, in a 0.5° x 0.5° grid. It was observed that, with the exception of the far north mesoregion and a small area in the valley of Paraguaçu, the state of Bahia presented large areas suitable for planting of maize crops at different times of the year, with emphasis on areas in the southeast of the state that are suitable for planting all year round.

statistics

validation

climate

agricultural calendar

A surge of maize (Zea mays L.) production has been observed as a result of the diversification of its use in animal and human food and industrial applications, especially in the growing industry of ethanol production. In the scenario of global rising consumption of this cereal, the industrial sector was responsible for 29.8% of the domestic demand of many countries in the 2000/2001 harvest, while this figure grew to 38.5% in the 2019/2020 harvest, which corresponds to an increase of 30% in relation to industrial consumption and almost 10% considering all uses in a period of 20 years (CONAB 2019).

Maize is the grain most produced in Northeast Brazil (NEB), where it is largely explored in rainfed agriculture, and it is not only an important basic food for the human population but also for animals (Martins et al. 2019; de Carvalho et al. 2019). Marengo et al. (2022) conducted a review on the agricultural adaptation and food security policy in the NEB and noted that the state of Bahia is responsible for most of the annual crop production in that region, including of maize. Data provided by the Brazilian Institute of Geography and Statistics (IBGE) in the IBGE Automatic Recovery System (CIDRA) (IBGE 2021) indicate that among the Northeastern states, Bahia had the highest maize grain yield in the years 2003 to 2019, with records of production in all municipalities of the state, especially São Desidério, Formosa do Rio Preto, Barreiras, Correntina and Luís Eduardo Magalhães, where the highest production was observed. It is also noteworthy that, in Brazil, there are three harvest seasons over a year and Bahia is the only state that presents a relevant production in all of them (CONAB 2022).

In Brazil, Agricultural Zoning of Climate Risk (AZCR) allows more reliable indications of the most appropriate sites and dates to plant a crop. This method has a validated methodology and technical-scientific support from the Brazilian Agricultural Research Corporation - Embrapa (Brasil 2019), thus constituting an important tool in rural production. Agriculture has a fundamental role in the current economy of Brazil, representing 27.4% of the Brazilian Gross Domestic Product (GDP) according to the Center for Advanced Studies in Applied Economics – CEPEA of the University of São Paulo – USP, in partnership with the Confederation of Agriculture and Livestock of Brazil – CNA (CEPEA 2021).

Accurate meteorological data and continuous observations, without interruptions over time, are necessary for the elaboration of the AZCR. In this sense, authors such as Xavier et al. (2016) and Monteiro and Martins (2019) report that the unavailability of data in specific areas of Brazil represents one of the major recurring problems to build a consistent meteorological database necessary for more reliable forecasts and scientific research.

Estimates based on atmospheric reanalysis datasets are an alternative to observational data from weather stations. One of the objectives of reanalysis is to merge irregular observational data and models that encompass many physical and dynamic processes to generate a synthesized estimate of the state of the system through a uniform grid with spatial homogeneity, temporal continuity and a multidimensional hierarchy, becoming a valuable data source to study weather systems and climate variability. For this reason, a reanalysis system consists of an accurate forecast model and data assimilation routine (Bosilovich et al. 2008; Sun et al. 2018).

After several years of advances and development in data modeling and assimilation at the European Center for Medium-Range Weather Forecasts (ECMWF), a new model cycle of the Integrated Forecast System (IFS Cycle 41r2) has been introduced into operational reanalysis to ensure a significantly enhanced forecast accuracy and computational efficiency. The dataset, called ERA5, provides estimates of various atmospheric, land surface, and sea state parameters for each hour of the day in 0.25 ° × 0.25 ° latitude-longitude grids (Jiang et al. 2019; Hersbach et al. 2020; Bell et al. 2021).

ERA5-Land was produced by reproducing the ECMWF ERA5 climate reanalysis terrestrial component, presented with a more refined sub-grid with 0.1° x 0.1° spatial resolution, with a native resolution of 9 km. ERA5-Land uses ERA5 atmospheric variables such as air temperature and humidity as input to control the simulated land fields. This is called atmospheric forcing. Without the constraint of atmospheric forcing, model-based estimates can quickly deviate from reality. Thus, although observations are not used directly in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation (Muñoz-Sabater 2019).

The temporal and spatial resolutions of ERA5-Land make this dataset very useful for many types of land applications, even despite some degree of uncertainty, because it provides accurate information on global land surface meteorological variables. However, ERA5-Land does not provide satisfactory estimates for precipitation in tropical areas (Lavers et al. 2022; Gleixner et al. 2020). Thus, in the present study, we used the precipitation dataset of the CPC Global Unified Gauge-Based Analysis of Daily Precipitation project from the Climate Prediction Center/National Oceanic and Atmospheric Administration (CPC/NOAA) also know as CPC/NOAA Precipitation Project. This dataset presents a consistent quantity and constantly improved quality, combining all information sources available at CPC/NOAA, covering the entire globe with a horizontal resolution of 0.5° x 0.5° generated through objective interpolation analysis techniques (Chen et al. 2008; Sadeghi et al. 2019). Details of the CPC/NOAA Precipitation Project can be obtained from CPC Global Unified Gauge-Based Analysis of Daily Precipitation data provided by the NOAA PSL, Boulder, Colorado, USA, at https://psl.noaa.gov.

Considering that the Brazilian state of Bahia has only 27 conventional weather stations with data available for the period analyzed in the present study – and only five of these stations have good consistency with less than 25% of missing data – and the fact that reliable and accurate data are fundamental for agricultural planning, this study aimed to validate maximum and minimum air temperature, relative humidity, wind speed data, and reference evapotranspiration (ET₀) estimates from ECMWF ERA5-Land reanalysis and precipitation estimates from the CPC/NOAA Precipitation Project for application in the AZCR for the maize crop in the state of Bahia as a grid with horizontal spatial resolution of 0.5° x 0.5° covering the entire state.

The study was conducted in the state of Bahia, located in the NEB, between the latitudes 8° 32’ and 18° 21’ S and longitudes 37° 20’ and 46° 37’ W (Fig. 1). The following data were used:

Daily meteorological data of minimum and maximum air temperature, average relative humidity, insolation, and average wind speed at 10 m collected in weather stations during a 10-year period (January 1, 1993 to December 31, 2002) for the municipalities studied were downloaded from the Meteorological Database for Teaching and Research (BDMEP) of the National Institute of Meteorology (INMET).

ERA5-Land reanalysis data on air temperature at 2 m height, dew point temperature at 2 m height, solar and thermal surface radiation, u and v components of wind at an hourly frequency were obtained from the ECMWF website for the period between January 1, 1993, and December 31, 2002, with a horizontal resolution of 0.1° x 0.1°. As ERA5 reanalysis data do not provide good rainfall estimates for the tropics (Gleixner et al. 2020; Lavers et al. 2022), daily rainfall data for the period between January 1, 1993, and December 31, 2002, were retrieved from the CPC/NOAA Precipitation Project, with a spatial resolution of 0.5º x 0.5º (Chen et al. 2008), because this dataset has been a useful reference to improve other data sources (Cui et al. 2017; Sadeghi et al. 2019). Due to the different spatial resolutions, the ERA5-Land reanalysis data were scaled to a 0.5° x 0.5° grid.

The Python 3.8 programming language and the Visual Studio Code editor were used to process all reanalysis data and calculate the following variables: relative air humidity, wind magnitude, radiation balance, and reference evapotranspiration.

Potential evapotranspiration, which can be estimated by several methods, is one of the key variables to the application of AZCR based on water balance. In the case of agricultural crops, the Food and Agriculture Organization (FAO) recommends the use of reference evapotranspiration. Thus, in this study, ET₀ was estimated by the Penman-Monteith-FAO method proposed by Allen et al. (1998).

The program for crop water balance used in an EXCEL^™ environment was created by Rolim et al. (1998) and the method for estimating the reference evapotranspiration was proposed by Allen et al. (1998), enabling the use of the variables degree-days-based crop coefficient (Kc), as described in Table 1, and variable available water capacity (AWC) of the soil, all organized in ten-day periods.

Table 1

Crop coefficient (Kc) for the early, medium, and late cycles with 100, 120 and 140 days, respectively. Values represent ten-day averages adjusted for the state of Bahia.
Cycle	Crop coefficient (ten-day values)
Cycle	1	2	3	4	5	6	7	8	9	10	11	12	13	14
Early	0.40	0,42	0.63	0.90	1.12	1.15	1.15	1.14	0.95	0.67	-	-	-	-
Medium	0.40	0.40	0.50	0.73	0.96	1.14	1.15	1.15	1.15	1.09	0.87	0.65	-	-
Late	0.40	0.40	0.44	0.62	0.81	1.01	1.14	1.15	1.15	1.15	1.15	1.02	0.83	0.64

The mean AWC value in each quadrant of the study was calculated for the early, medium, and late cycles with 100, 120 and 140 days from the AWC values of each municipality of Bahia available at the National Water Agency, ANA (2021), as shown in Fig. 2:

In this study we analyzed the cycles classified as early (Group I), medium (Group II) and late (Group III) for the maize crop according to Ordinance number 170 of July, 2020, of the Ministry of Agriculture, Livestock and Supply of Brazil, which approves the AZCR for the 1st harvest of maize crop in the state of Bahia. To calculate the crop water balance, the maize cycle was divided into four phases, namely, germination/emergence (phase I), vegetative growth/development (phase II), flowering/grain filling (phase III), and physiological maturation/harvest (phase IV), respectively. The duration of each phase and the average size of the cycles are presented in Table 2.

Table 2

Average duration of cycles and respective phenological phases in days
Cycle	Phase I	Phase II	Phase III	Phase IV	Average cycle (days)
Early	15	35	30	20	100
Medium	15	45	40	20	120
Late	15	55	50	20	140

Source: Adapted. MAPA, 2020.

The water requirement satisfaction index (WRSI) developed by FAO (Frère and Popov, 1986) was used as a selection criterion for the start of the planting in each simulation of the crop water balance. This index consists of the ratio of actual to maximum evapotranspiration of the crop at a frequency of 80% established by Assad et al. (1998) and indicate a high (WRSI > 0.55), intermediate (WRSI between 0.45 and 0.55), or low (WRSI < 0.45) suitability of a site for planting maize, considering then that a site is suitable for planting maize when WRSI ≥ 0.45 (Santos et al., 2012).

Crop water balance simulations were carried out in ten-day periods. Thus a total of 36 periods per year were considered in the selection of dates suitable for sowing.

As described in the objectives, the study was divided into two stages: first, the validation of data from ERA5-Land reanalysis and the CPC/NOAA Precipitation Project by comparison with actual values from INMET weather stations; and second, the use of the validated data to elaborate the AZCR for maize cultivation in the state of Bahia as a grid with horizontal spatial resolution of 0.5° x 0.5° covering the entire state.

2.1 Validation

2.1.1 Weather stations

Five INMET weather stations in the state of Bahia were selected for the validation of ECMWF ERA5-Land reanalysis data and CPC/NOAA Precipitation Project because they were the ones that presented the lowest numbers of flaws among the stations of the state. The climatological aspects of the municipalities where the five stations are located regarding biomes and climate type according to Köppen-Geiger classification are shown in Table 3.

Table 3

Municipalities, average annual air temperature, annual precipitation, biome, climate classification according to Köppen-Geiger classification (As – tropical savanna climate with dry summer; Aw - tropical savanna climate with dry winter; BSh - hot semiarid climate; Cwb - subtropical highland climate), and mesoregion of the state of Bahia
City	Air temperature (ºC)	Precipitation (mm year^-1)	Biome/ Climate Classification	Mesoregion
Alagoinhas	25.41	1147.96	Atlantic Forest and Caatinga/As	Northeast Bahia
Bom Jesus da Lapa	26.55	814.55	Cerrado and Caatinga/Aw	São Francisco Valley
Cipó	26.69	578.17	Caatinga/BSh	Northeast Bahia
Correntina	23.17	935.37	Cerrado/Aw	Far west of Bahia
Vitória da Conquista	21.43	735.44	Atlantic Forest/Cwb	South-Central Bahia

As scarce and incomplete weather station data pose a problem for time series analysis and applications in several regions of Brazil, the few locations where reliable information is available are used as a basic tool to validate reanalysis data.

To create a calendar indicating the periods suitable for growing maize under rainfed conditions, simulations of the crop water balance were performed using INMET weather station data and reanalysis data separately. In addition, a simulation was performed with precipitation data from the weather stations and air temperature and reference evapotranspiration derived from reanalysis data. Reference evapotranspiration was calculated from the variables air temperature and relative air humidity (maximum, minimum and average), average wind speed, solar radiation, and radiation balance. This is a “hybrid” approach and it was adopted because the state of Bahia has a good spatial distribution of pluviometric gauges, with daily and routine observations of precipitation only, but a scarce spatial distribution of weather stations.

The spreadsheet with the BHCult generates results in graphs of reference evapotranspiration (ET₀), actual evapotranspiration (ETr), water storage, AWC, crop coefficient (Kc), and water balance history with the periods of surplus and deficit.

2.1.2 Parameters analyzed for validation

ERA5-Land reanalysis data and CPC/NOAA Precipitation Project a were validated and their reliability evaluated by comparing temperature (maximum and minimum), relative humidity, wind speed, and reference evapotranspiration estimates to weather station data. For this purpose, the statistical parameters root mean square error (RMSE), standard error of the estimate (SEE), mean absolute percentage error (MAPE), and mean absolute error (MAE) obtained in the comparisons of ERA5-Land reanalysis data and CPC/NOAA precipitation data with observational data were used in the analysis.

2.2 Agricultural zoning of climate risk for the state of Bahia

After the statistical validation of the data from ERA5-Land reanalysis and the CPC/NOAA Precipitation Project, a spatialization of agricultural zoning comprising the entire state of Bahia was proposed. For this, the ERA5-Land reanalysis data were organized in means grouped every 0.5º x 0.5º to equate the spatial resolution of the CPC/NOAA Precipitation Project throughout the territory of Bahia, within the extreme parallels of 8.25º S and 18.75º S and 37.25º W and 47.25º W, totaling an area with 232 grid points.

3.1 Validation

3.1.1 Statistical validation

Statistics were performed with the ten-day data used for the municipalities of Alagoinhas, Bom Jesus da Lapa, Cipó, Correntina and Vitória da Conquista over the 10 years observed (January 1, 1993, to December 31, 2002). Due to the large volume of results, the overall average statistics of each municipality will be presented.

The overall average statistics of the errors for the variables maximum and minimum air temperature, average wind speed, average relative air humidity, and reference evapotranspiration from ERA5-land reanalysis data and the variable precipitation from the CCPC/NOAA project obtained through comparisons with observational data from selected weather stations in Bahia are shown in Table 4. The statistics were not performed for the radiation variables because no observational data on radiation were available in the weather stations. Thus, the radiation balance, which is necessary to estimate the reference evapotranspiration, was obtained according to Allen et al. (1998).

Air temperature estimates correlated very well with surface observations. Maximum temperature values slightly underestimated the observed series and minimum air temperature values had a better correlation with observational data. Better performances were obtained in Vitória da Conquista for maximum air temperature and in Correntina for minimum air temperature.

Average wind speed had low error values in all the studied sites, but even lower in the one sample unit that had the lower average wind speed values in relation to the other variables and consequently lower error values. Also, lower root mean square error and standard error of the estimate values were found for Vitória da Conquista and lower mean absolute percentage error and mean absolute error values for Correntina. Following this line of reasoning for average relative humidity, there was a small increase in statistical error values due to the increase in the sample value, however the discrepancy decreased in the mean absolute percentage error, reflecting the approximation between estimated and actual values and showing the performance of the model, especially in the municipality of Alagoinhas.

Although reference evapotranspiration values were calculated through the other variables, the statistics showed their excellent performance, indicating their approximation to reality through the root mean square error and standard error of the estimate for Vitória da Conquista, and through the mean absolute percentage error and mean absolute error for Alagoinhas.

Precipitation is the most complex and difficult variable to predict. Precipitation estimates from ERA5-land reanalysis showed a high deviation from actual precipitation rates. Gleixner et al. (2020) concluded that both ERA5 and the precedent ERA-interim re-analysis products did not capture the precipitation trends observed in most of Africa. This is not surprising since precipitation trends are uncertain for most regions of the globe. To circumvent this problem, data from the CPC/NOAA Precipitation Project were used in the present study. They were closer to observational data but, according to the statistics, still fall short of an excellent performance for the study region. The lowest root mean square error, standard error of the estimate, and mean absolute error values were observed in Cipó.

The root mean square error, standard error of the estimate, mean absolute percentage error, and mean absolute error values described in Table 4 allow us to evaluate the reliability of the ERA5-land reanalysis and CPC/NOAA Precipitation Project when compared with weather station data. The reanalysis data were, thus, validated and acceptable in the absence of observational data, particularly data on maximum and minimum air temperature, relative humidity, and wind speed at 2 m height.

Therefore, with the exception of precipitation, ERA5-Land reanalysis data can be reliably used for Agricultural Zoning of Climate Risk in the state of Bahia.

Table 4

Root mean square error (RMSE), standard error of the estimate (SEE), mean absolute percentage error (MAPE), and mean absolute error (MAE) for relative air humidity, maximum and minimum air temperature, wind speed, reference evapotranspiration, and precipitation
Maximum temperature
Weather stations	RMSE (%)	SEE (%)	MAPE (%)	MAE (%)
Alagoinhas	1.60	1.68	0.48	1.47
Bom Jesus da Lapa	1.07	1.12	0.25	0.83
Cipó	2.28	2.42	0.62	1.73
Correntina	1.76	1.86	0.52	1.66
Vitória da Conquista	0.73	0.77	0.16	0.43
Minimum temperature
Weather stations	RMSE (%)	SEE (%)	MAPE (%)	MAE (%)
Alagoinhas	1.33	1.40	0.55	1.11
Bom Jesus da Lapa	2.01	2.12	0.95	1.87
Cipó	2.16	2.29	1.18	1.83
Correntina	0.61	0.64	0.16	0.33
Vitória da Conquista	0.82	0.87	0.29	0.45
Wind speed
Weather stations	RMSE (%)	SEE (%)	MAPE (%)	MAE (%)
Alagoinhas	0.46	0.49	2.06	0.35
Bom Jesus da Lapa	0.51	0.54	3.02	0.36
Cipó	0.73	0.78	3.60	0.57
Correntina	0.57	0.60	0.58	0.17
Vitória da Conquista	0.51	0.53	1.13	0.31
Relative humidity
Weather stations	RMSE (%)	SEE (%)	MAPE (%)	MAE (%)
Alagoinhas	4.30	4.54	0.34	2.72
Bom Jesus da Lapa	8.60	9.06	1.17	7.08
Cipó	9.51	10.04	1.09	6.57
Correntina	5.63	5.94	0.62	3.98
Vitória da Conquista	4.38	4.62	0.37	2.95
Reference evapotranspiration
Weather stations	RMSE (%)	SEE (%)	MAPE (%)	MAE (%)
Alagoinhas	3.31	3.5	0.38	1.2
Bom Jesus da Lapa	5.03	5.32	0.78	3.23
Cipó	4.69	5.07	0.71	2.31
Correntina	5.22	5.51	0.60	3.15
Vitória da Conquista	3.28	3.48	0.43	1.34
Precipitation
Weather stations	RMSE (%)	SEE (%)	MAPE (%)	MAE (%)
Alagoinhas	15.74	16.59	2.45	6.58
Bom Jesus da Lapa	10.18	10.73	11.40	2.88
Cipó	9.63	10.16	5.66	2.45
Correntina	11.58	12.21	2.60	3.51
Vitória da Conquista	10.01	10.55	1.60	2.65

3.1.2 Agricultural zoning of climate risk for Alagoinhas

Data from five weather stations that presented the lowest number of flaws (missing or doubtful data) among the 27 stations in the state of Bahia were used to validate the ERA5-Land reanalysis and CPC/NOAA Precipitation Project. However, the AZCR was performed in Alagoinhas because this municipality is the one with the higher rainfall rates and consequently larger sowing window for maize crop.

The recommended dates for planting in Alagoinhas according to the simulation of the crop water balance are organized in Table 5. The initial date was found to be the eighth ten-day period of the year in all three simulations (weather station data only; reanalysis and CPC/NOAA Precipitation Project only; and hybrid). The deadline for planting was also the same in the three simulations, but varying according to the duration of the cycle. Both the reanalysis data-based and the hybrid simulation described the same period as the weather station data-based simulation, thus confirming that the estimated data can be used for the AZCR of maize in the municipality.

The AZCR published by the Ministry of Agriculture, Livestock and Supply in Ordinance number 170 of May 25, 2022, in the Federal Official Gazette considers the planting of maize at a frequency of 80% for the early cycle with ten-day periods from 10 to 17, 10 to 18, 10 to 19, for the medium cycle with ten-day periods from 10 to 16, 10 to 17, 10 to 17, and for the late cycle with ten-day periods 10 to 14, 10 to 15, 10 to 16 for sandy, medium and clayey soil textures, respectively (MAPA 2022). There is a clear similarity between the recommended dates for planting maize found in the present study and MAPA (2022). Moreover, the initial discrepancy between the ten-day periods 8 and 10 can be explained by the different methodologies applied, such as the use of different water requirement satisfaction indices to start planting.

Table 5

Dates, in ten-day periods of the year, suitable for planting in the municipality of Alagoinhas
Alagoinhas	Cycle (ten-day period)
Alagoinhas	Early	Medium	Late
Station	8 to 20	8 to 18	8 to 16
Reanalysis	8 to 20	8 to 18	8 to 16
Hybrid	8 to 20	8 to 18	8 to 16

Casanovas et al. (2003) found that droughts during the flowering period of maize negatively affected leaf physiology and grain yield and, thus, water deficits during this period are the ones that cause greater productivity losses. In the present study, in the hybrid simulation (Fig. 3), the period with water surplus comprises the entire period in which water requirements are higher for the development of the crop, from the establishment to the fruiting phase. The graphs present the period with water surplus and indicate the phenological phases of the plant during each ten-day period. The same result was observed in the reanalysis data-based and weather station data-based simulations.

Thus, the suitable dates for planting maize and respective phenological phases depend on the water balance of the crop. The initial date indicated was the eighth ten-day period, when the WRSI is favorable (WRSI > 0.55) or intermediate (WRSI between 0.45 and 0.55). Further, as the studied site has a long period with water surplus, it is possible to plant rainfed maize with early (Fig. 3a), medium (Fig. 3b) and late (Fig. 3c) cycle varieties.

3.2 Agricultural climate risk zoning for the state of Bahia

The recommendations for planting rainfed early-cycle maize in the state of Bahia go from regions that do not have any ten-day period recommended for maize cultivation to the coastal strip of the state where planting is recommended all year round (Fig. 4).

The largest region where planting of rainfed early-cycle maize is recommended is the mesoregion of the far north of Bahia (Fig. 4). This is because the state of Bahia has the majority of the municipalities (276) with dry conditions in Northeastern Brazil (Avala et al. 2017). In the semiarid region, the highest rainfall (900 mm) is observed in the eastern and western region and rainfall decreases towards the center, reaching minimum annual values of less than 600 mm in the far north of Bahia (Lopes et al. 2019).

Besides the mesoregion of the far north of Bahia, planting maize is not recommended in the area found in the 12.75º S and 39.75º W grid either (Fig. 4), which comprises part of the municipalities of Iaçu and Milagres. This is a semiarid area located between the Diamantina and Conquista-Jequitinhonha plateaus known as the Southern Sertaneja Depression and presents irregular rainfall volumes according to the historical series, with events of extreme droughts (Jelinek et al. 2014; Santos et al. 2014; Santos Neto et al. 2022). Sousa et al. (2016) analyzed 73 stations in the Paraguaçu river basin, the most important in the state of Bahia, and observed that 5 municipalities presented extreme droughts. The longest droughts were found in Iaçu, lasting 34 months, from 1961 to 1963, as a result of changes in atmosphere dynamics when the displacement of precipitating systems was changed.

As also Bahia state do not have 31 to 35 ten-day periods accumulated for this study, and it has five areas with no recommendation along its coast due to no available data from reanalysis ERA5-Land.

In contrast, planting in the southern coastal strip is recommended during the 36 ten-day periods. A study by Silva et al. (2019) that used the precipitation concentration degree (PCD) for the NEB found values ranging from 0.16 to 0.36 for the southern coast of Bahia, indicating that annual rainfall is uniformly distributed throughout the year, what may be related to the atmospheric systems that operate in this part of of the state in different seasons of the year, namely, the South Atlantic Convergence Zone (Simões et al. 2018), Frontal systems (Kousky 1979), and Easterly Wave systems (Gomes et al. 2019). Bahia has the greatest variability of all components of the water balance, concentrating the highest volumes of precipitation and water surplus in its coastal region, with the highest values observed between May and July. Further, within the southern mesoregion of Bahia, there is a lower amount of rainfall in the northwest and southwest, with minimum values of 878.8 mm year^− 1, and higher in the northeast, with maximum values of 1,619 mm year^− 1 (Mencia et al. 2021; Aparecido et al. 2022). The analyses of seasons suitable for cultivation of maize for the other geographical areas of the state of Bahia are presented in the following subsection.

3.2.1 Recommendations of suitable dates

Figure 5 shows the recommendations for planting rainfed early-cycle maize crops in the state of Bahia, with occurrence of 80%, for the ten-day periods 30 and 31 (Fig. 5a), 32 and 33 (Fig. 5b), 34 and 35 (Fig. 5c), 36 and 1 (Fig. 5d), 2 and 3 (Fig. 5e) which are associated with the main rainy period in most of the state of Bahia, including the semiarid region, which occurs from November to January, with a maximum precipitation rates in December (Kousky and Chu 1978; Rao and Hada 1990; de Souza and Nascimento, 2020). Rainfall in this period is associated with the penetration of Frontal Systems or their vestiges (Kousky 1979), the South Atlantic Convergence Zone (Rosa et al.,2020; Simões et al. 2020; Pezzi et al. 2022), South Atlantic Subtropical High (Chaves and Nobre 2004), and Upper Tropospheric Cyclonic Vortices (Kousky and Gan 1981; Morais et al. 2021; Escobar and Reboita 2022). Reis et al. (2021) studied the spatial distribution of the Upper Tropospheric Cyclonic Vortices for a 37-year period and found that the east of the state of Bahia and almost all of its coastal region are among the Northeastern regions with the highest occurrence of precipitation associated with this system

In the east of Bahia, that is, throughout the coastal strip, the most intense rains occur from April to August, corresponding to the ten-day periods 4 and 5 (Fig. 5f), 6 and 7 (Fig. 5g), 8 and 9 (Fig. 5h), 10 and 11 (Fig. 5i), 12 and 13 (Fig. 5j), 14 and 15 (Fig. 5k), and 16 and 17 (Fig. 5l) which, according to Yamazaki and Rao (1977) and Gomes et al. (2019), are rains resulting from Easterly Wave Disturbances. However, Kousky (1980) found that in the eastern part of the NEB, including practically the whole coast of Bahia, the most intense rains observed in the months from April to June are largely explained by the interaction between circulation of Terrestrial Breeze and predominant east and southeast trade winds. Such interaction imposes a peak of nocturnal precipitation. The rains on the coast are also explained by the modulation of the trade winds and the Subtropical High and by vestiges of Frontal Systems (Kousky 1979; Rao et al. 1993).

Assuming that precipitation is the only way for water to enter the maize water balance system, the 232 areas in which the AZCR was carried out for early-cycle maize in Bahia obtained recommendations for planting that coincide with the rainiest period of each region and their active precipitation systems. Fonseca et al. (2022) explain that this is because sowing dates in rainfed systems are strongly correlated with the start of the rainy season.

The AZCR for the medium and late cycles indicated dates suitable and unsuitable for planting maize in Bahia. Due to their size, they were divided into two parts and comprise 232 areas, starting from the western to the central region of the state (Table 6) and from the central region to the eastern region (Table 6.1).

In relation to the early cycle, dates for planting the medium and late cycles in each study area are more restricted, with the exception of the southern coastal area in which planting is recommended all year round for the early, medium and late cycles. Naturally, areas not recommended for planting of the early cycle were not recommended for the other cycles either. And areas with a short sowing window for planting the early cycle were not recommended for the medium cycle, following this trend as the number of days required to complete the cycle increases.

Table 6

Dates, in ten-day periods, recommended for planting medium and late cycle maize in the state of Bahia, starting from the western to the central region.
Longitude	Latitude	Medium cycle	Late cycle	Longitude	Latitude	Medium cycle	Late cycle	Longitude	Latitude	Medium cycle	Late cycle	Longitude	Latitude	Medium cycle	Late cycle
46.25	13.75	30 to 3	30 to 1	44.75	10.75	30 to 2	30 to 36	43.25	9.75	31 to 2	31 to 36	42.25	12.25	31	-
46.25	13.25	29 to 3	29 to 1	44.75	11.25	29 to 36	29 to 34	43.25	10.25	31 to 36	31 to 34	42.25	12.75	31 to 36	31 to 34
46.25	12.75	29 to 4	29 to 2	44.75	11.75	29 to 2	29 to 36	43.25	10.75	31 to 1	31 to 35	42.25	13.25	31 to 36	31 to 34
46.25	12.25	29 to 4	29 to 2	44.75	12.25	29 to 2	29 to 36	43.25	11.25	31 to 1	31 to 35	42.25	13.75	31 to 35	31 to 33
46.25	11.75	29 to 4	29 to 2	44.75	12.75	30 to 2	30 to 36	43.25	11.75	31 to 36	31 to 34	42.25	14.25	-	-
46.25	11.25	29 to 4	29 to 2	44.75	13.25	30 to 1	30 to 35	43.25	12.25	31 to 36	31 to 34	42.25	14.75	31	-
46.25	10.75	29 to 4	29 to 2	44.75	13.75	30 to 36	30 to 34	43.25	12.75	31 to 1	31 to 35	41.75	8.75	35 to 1	-
45.75	10.25	29 to 2	29 to 36	44.75	14.25	30 to 36	30 to 34	43.25	13.25	31 to 36	31 to 35	41.75	9.25	35 to 36	-
45.75	10.75	29 to 2	29 to 36	44.25	10.25	30 to 2	30 to 36	43.25	13.75	31 to 35	31 to 33	41.75	9.75	35 to 2	-
45.75	11.25	29 to 2	29 to 36	44.25	10.75	30 to 2	30 to 36	43.25	14.25	31 to 35	31 to 33	41.75	10.25	31 to 35	31 to 33
45.75	11.75	29 to 2	29 to 36	44.25	11.25	31 to 2	31 to 36	43.25	14.75	31 to 35	31 to 33	41.75	10.75	31 to 32	-
45.75	12.25	29 to 3	29 to 1	44.25	11.75	29 to 2	29 to 36	42.75	9.25	32 to 36	32 to 34	41.75	11.25	31 to 36	31 to 34
45.75	12.75	29 to 3	29 to 1	44.25	12.25	30 to 2	30 to 36	42.75	9.75	35 to 1	35 to 1	41.75	11.75	31 to 36	31 to 29
45.75	13.25	29 to 3	29 to 1	44.25	12.75	30 to 2	30 to 36	42.75	10.25	31 to 36	31 to 34	41.75	12.25	31 to 3	31 to 29
45.75	13.75	30 to 2	30 to 36	44.25	13.25	30 to 2	30 to 36	42.75	10.75	31 to 36	31 to 34	41.75	12.75	31 to 1	31 to 35
45.75	14.25	29 to 2	29 to 36	44.25	13.75	31 to 36	31 to 34	42.75	11.25	31 to 36	31 to 34	41.75	13.25	31 to 1	31 to 35
45.75	14.75	28 to 3	28 to 1	44.25	14.25	31 to 36	31 to 34	42.75	11.75	31 to 36	31 to 34	41.75	13.75	-	-
45.75	15.25	28 to 2	28 to 36	43.75	9.25	32 to 3	32 to 1	42.75	12.25	31 to 36	31 to 34	41.75	14.25	-	-
45.25	9.75	30 to 3	30 to 1	43.75	9.75	31 to 2	31 to 36	42.75	12.75	31 to 36	31 to 34	41.75	14.75	-	-
45.25	10.25	30 to 2	30 to 36	43.75	10.25	31 to 2	31 to 36	42.75	13.25	31 to 36	31 to 34	41.25	8.25	35 to 1	-
45.25	10.75	29 to 2	29 to 36	43.75	10.75	31 to 2	31 to 36	42.75	13.75	31 to 35	31 to 35	41.25	8.75	-	-
45.25	11.25	29 to 36	29 to 34	43.75	11.25	31 to 1	31 to 35	42.75	14.25	31 to 35	31 to 33	41.25	9.25	-	-
45.25	11.75	29 to 36	29 to 34	43.75	11.75	31 to 2	31 to 36	42.75	14.75	31 to 35	31 to 33	41.25	9.75	-	-
45.25	12.25	29 to 2	29 to 36	43.75	12.25	30 to 2	30 to 36	42.25	9.25	35 to 2	-	41.25	10.25	-	-
45.25	12.75	29 to 2	29 to 36	43.75	12.75	30 to 1	30 to 35	42.25	9.75	35 to 2	-	41.25	10.75	-	-
45.25	13.25	30 to 2	30 to 36	43.75	13.25	31 to 36	31 to 34	42.25	10.25	31 to 36	31 to 34	41.25	11.25	-	-
45.25	13.75	30 to 2	30 to 36	43.75	13.75	31 to 36	31 to 34	42.25	10.75	31 to 35	31 to 33	41.25	11.75	-	-
45.25	14.25	30 to 2	30 to 36	43.75	14.25	31 to 35	31 to 33	42.25	11.25	31 to 36	31 to 34	41.25	12.25	31 to 5	31 to 3
45.25	14.75	29 to 2	29 to 36	43.25	9.25	32 to 3	32 to 1	42.25	11.75	31 to 36	31 to 34	41.25	12.75	31 to 3	31 to 1

Table 6.1

Dates, in ten-day periods, recommended for planting medium and late cycle maize in the state of Bahia, starting from the western to the central region.
Longitude	Latitude	Medium cycle	Late cycle	Longitude	Latitude	Medium cycle	Late cycle	Longitude	Latitude	Medium cycle	Late cycle	Longitude	Latitude	Medium cycle	Late cycle
41.25	13.25	31 to 1	-	40.25	12.75	-	-	39.75	17.75	5 to 30	5 to 28	38.75	11.75	8 to 15	8 to 13
41.25	13.75	-	-	40.25	13.25	-	-	39.75	18.25	7 to 29	7 to 27	38.75	12.25	8 to 18	8 to 16
41.25	14.25	-	-	40.25	13.75	-	-	39.25	8.25	36 to 2	36 to 36	38.75	12.75	4 to 20	4 to 18
41.25	14.75	-	-	40.25	14.25	31 to 1	31 to 29	39.25	8.75	-	-	38.75	13.25	1 to 24	1 to 22
41.25	15.25	-	-	40.25	14.75	31 to 1	31 to 35	39.25	9.25	-	-	38.75	13.75	1 to 36	1 to 36
40.75	8.25	36 to 2	-	40.25	15.25	31 to 3	31 to 3	39.25	9.75	-	-	38.75	14.25	1 to 36	1 to 36
40.75	8.75	36	-	40.25	15.75	29 to 5	29 to 3	39.25	10.25	-	-	38.75	15.25	1 to 36	1 to 36
40.75	9.25	-	-	40.25	16.25	29 to 6	29 to 3	39.25	10.75	-	-	38.75	15.75	1 to 36	1 to 36
40.75	9.75	-	-	40.25	16.75	29 to 7	29 to 5	39.25	11.25	-	-	38.75	16.25	1 to 36	1 to 36
40.75	10.25	-	-	40.25	17.25	29 to 12	29 to 10	39.25	11.75	7 to 11	7 to 9	38.25	8.75	-	-
40.75	10.75	-	-	40.25	17.75	30 to 7	30 to 5	39.25	12.25	7 to 11	7 to 12	38.25	9.25	-	-
40.75	11.25	-	-	39.75	8.75	-	-	39.25	12.75	4 to 18	4 to 16	38.25	9.75	-	-
40.75	11.75	-	-	39.75	9.25	-	-	39.25	13.25	1 to 36	1 to 36	38.25	10.25	15	-
40.75	12.25	-	-	39.75	9.75	-	-	39.25	13.75	1 to 36	1 to 36	38.25	10.75	12 to 14	12 to 13
40.75	12.75	-	-	39.75	10.25	-	-	39.25	14.25	1 to 36	1 to 36	38.25	11.25	10 to 15	10 to 13
40.75	13.25	-	-	39.75	10.75	-	-	39.25	14.75	1 to 36	1 to 36	38.25	11.75	8 to 18	8 to 16
40.75	13.75	-	-	39.75	11.25	-	-	39.25	15.25	1 to 36	1 to 36	38.25	12.25	8 to 18	8 to 16
40.75	14.25	-	-	39.75	11.75	7 to 13	7 to 9	39.25	15.75	1 to 36	1 to 36	38.25	12.75	5 to 18	5 to 16
40.75	14.75	-	-	39.75	12.25	-	-	39.25	16.25	1 to 36	1 to 36	37.75	9.25	-	-
40.75	15.25	31 to 36	31 to 35	39.75	12.75	-	-	39.25	16.75	1 to 36	1 to 36	37.75	9.75	13 to 15	13 to 13
40.75	15.75	29 to 5	29 to 3	39.75	13.25	4 to 15	4 to 13	39.25	17.25	5 to 30	5 to 28	37.75	10.25	11 to 15	11 to 14
40.25	8.75	-	-	39.75	13.75	31 to 16	31 to 14	39.25	17.75	5 to 30	5 to 28	37.75	10.75	10 to 17	10 to 15
40.25	9.25	-	-	39.75	14.25	31 to 18	31 to 16	38.75	8.25	-	-	37.75	11.25	9 to 18	9 to 16
40.25	9.75	-	-	39.75	14.75	29 to 18	29 to 16	38.75	8.75	-	-	37.75	11.75	9 to 18	9 to 16
40.25	10.25	-	-	39.75	15.25	29 to 15	29 to 13	38.75	9.25	-	-	37.75	12.25	8 to 18	8 to 16
40.25	10.75	-	-	39.75	15.75	29 to 13	29 to 11	38.75	9.75	-	-	37.25	9.75	12 to 15	12 to 13
40.25	11.25	-	-	39.75	16.25	29 to 13	29 to 11	38.75	10.25	-	-	37.25	10.25	10 to 15	10 to 13
40.25	11.75	32	-	39.75	16.75	29 to 18	29 to 16	38.75	10.75	-	-	37.25	10.75	9 to 20	9 to 18
40.25	12.25	-	-	39.75	17.25	29 to 14	29 to 12	38.75	11.25	-	-	37.25	11.25	5 to 20	5 to 18

The validation of the ERA5-Land reanalysis and CPC/NOAA Precipitation Project allows us to conclude that these data can be reliably used to estimate reference evapotranspiration and crop water balance values and applied in agricultural zoning of climate risk.

The agricultural zoning of climate risk was improved, increasing the refinement of the spatial network of this very important decision-making tool in the state of Bahia so as to mitigate risks in rainfed maize crops. Weather stations that provide accurate and reliable data are essential for agricultural zoning of climate risk and their maintenance and expansion should be encouraged. The satisfactory results in the validation of reanalysis data showed that these data are efficient and robust for the development of future studies requiring high-resolution meteorological data, providing in-depth knowledge about the climatology of any location.

Funding

This work was supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). The authors declare that only the first author has recieved a grant to conducting her scientific research.

Conflicts of interest

All authors declare that they have no conflicts of interest or competing interests do disclosure.

Ethics approval

I state herewith that the material of this article is not being presently considered for publication in any
other journal, that its publication is approved by all authors and by the responsible authorities where the
work was carried out, and that, if accepted, it will not be published elsewhere, including electronically,
in the same form, in English or any other language without the written consent of the copyright-holder.

Consent to participate
All authors consent to participate in the submission.

Consent for publication

All authors read and approved the final manuscript.

Data availability

The datasets analyzed in the present study are available in the ECWMF repository [https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land],
NASA repository [https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html], and INMET repository
[https://bdmep.inmet.gov.br/].
Authors' contributions

The preparation data was made by and analysis were performed by WKM, GCAJ and JIBB. The first draft of the manuscript was written by WKM and the authors ESGS, FFL, JIBB, CACS and EPL commented, rating and proofreading on previous versions of the manuscript to improve. All authors contributed to this study and approved the final manuscript.

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Application of ERA5-Land reanalysis data in climate risk zoning for maize in the state of Bahia - Brazil

Status:

Journal Publication

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Abstract

Figures

1 Introduction

2 Methodology

2.1 Validation

2.1.1 Weather stations

2.1.2 Parameters analyzed for validation

2.2 Agricultural zoning of climate risk for the state of Bahia

3 Results

3.1 Validation

3.1.1 Statistical validation

3.1.2 Agricultural zoning of climate risk for Alagoinhas

3.2 Agricultural climate risk zoning for the state of Bahia

3.2.1 Recommendations of suitable dates

Conclusions

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1