Spatiotemporal variability and change in rainfall in the Oti River Basin, West Africa

Sustainable management of water resources, agricultural production and all hydro-ecological services at a river basin level is directly linked to empirical knowledge of the distribution of rainfall in space and time over the basin. Understanding climatic conditions, particularly spatiotemporal distribution of rainfall, is therefore vital for natural resources management in developing countries as in Africa with rising population growths and weak responsive capacity to climate change and variability impacts (Ampadu 2021).

With significant rainfall variability in time and space, West Africa is sensitive to extreme droughts and flooding due to climate change (Lebel et al. 2009). This is due to an undeviating relationship between rainfall and global climate (Twisa & Buchroithner 2019; Nhemachena et al. 2020). The variability of rainfall has caused lengthy droughts and floods in most parts of West Africa, posing a serious threat to the region. The savannah and semiarid areas, since the 1960s, have recorded events of famines and floods (Epule et al. 2017). Regrettably, climate variability is likely to intensify (Suleiman & Ifabiyi 2015), thereby making rainfall in the Sahel region increasingly unpredictable (IPCC 2014). When the climate changed in the 1960 and 1970s, rainfall was said to have decreased by 15 and 30%, respectively (Oguntunde et al. 2006). The 1980s have been regarded as West Africa's driest decade of the 20th century (Nicholson & Palao 1993).

Kasei et al. (2010) studied drought recurrence across the Volta Basin and found that the severity was below −2.0, which affected almost 75% of the area. This means that most of the areas in the basin suffered from drought in 1961, 1970, 1983, 1992 and 2001. On the other hand, the Oti River Basin (ORB) has experienced flood situations in 1998, 2007, 2008, 2010 and 2018, resulting in massive human deaths and infrastructural damage (Tschakert et al. 2010; Komi et al. 2016). The recorded damage during flooding episodes shows the extent to which the basin-dependent countries are exposed. Regarded as the worst flood ever faced in the basin was that of 2007, which killed 23 people in Togo, 46 in Burkina Faso and 56 in Ghana (Tschakert et al. 2010; Komi et al. 2016).

Water and agriculture sectors are key to the actualization of sustainability and economic growth within the region; however, their regular susceptibility toward the changing climate makes it difficult to achieve sustainable development (Hope 2009; Chemnitz & Hoeffler 2011; Nhemachena et al. 2020). Alterations in climate have been seen as a major challenge to agriculture and food sufficiency in Africa, especially in rainfed agriculture-dependent regions like West Africa (Wheeler & Von Braun 2013). Agriculture in the West African region represents around 95% of the arable land, engages around 65% of workers and augments the region's economy by 30–70% (Blanc 2012; Akumaga & Tarhule 2018). Kasei et al. (2010) have opined that the high irregularity in rainfall amounts, as well as spatiotemporal patterns in the ORB, are the main reasons for food production changes, particularly in the basin's northern sections.

Considering the critical role that rainfall plays in the economies of the countries in the basin, water resource authorities and the agricultural sector have to deal with issues relating to rainfall fluctuations on a regular basis. Accordingly, adjusting to this climatic fluctuation is a key component of reducing climatic shocks in the basin-dependent countries (Nhemachena et al. 2020). Therefore, a close monitoring of the present and future amount of rainfall and its variations would be useful for managing water and agronomic scheduling over the ORB.

Moreover, different studies have been conducted on the Volta Basin and some parts of the ORB with only scarce station data with no full focus on the main ORB. Kasei et al. (2010), for instance, explored the yearly rain deficiency over the Volta Basin. Komi et al. (2017) modeled the extent of flood hazard in the Togo part of the ORB. Klassou & Komi (2021) also analyzed extreme rainfall over the middle part of the ORB and focused on some selected extreme indices. Badjana et al. (2014) also probed the changes in land cover in the Kara basin, a sub-basin of the ORB. The present study uses both observed data from meteorological stations and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data (Funk et al. 2015) to analyze rainfall changes within the ORB. The objective of this study is therefore to analyze the spatiotemporal variability and change in rainfall over the ORB for an understanding of the distribution of rainfall in space and time as vital empirical evidence for sustainable management of water resources and agricultural production in the basin.

The geographical location of the ORB is found within four West African nations (Ghana, Burkina Faso, Togo and Benin) and lies between longitudes 6°W and 2°E and latitudes 0° and 15°N (Figure 1). It has an estimated surface area coverage of around 72,000 km², and it is a sub-basin of the Volta basin system in West Africa (Barry et al. 2005; Kasei 2009). The movement and interactions of the Inter-Tropical Discontinuity (ITD) and the associated West African Monsoon govern the basin's climate. Annual rainfall ranges from 1,100 mm (north) to 1,400 mm (south), with pan evaporation of about 2,540 mm/year and runoff of approximately 254 mm/year (Kasei 2009). The basin experiences a single rainfall pattern, having its maximum rain in August. The rainy season lasts from April to October, while the dry season lasts from November to March (Klassou & Komi 2021). The average annual temperature ranges from 25.9 to 34 °C. Because the basin's topography is steep and rainfall is quite high, surface runoff is facilitated, resulting in around 25% of the yearly total flow contributions to the Volta Lake (Barry et al. 2005).

Daily rainfall data for eight climate stations within the Oti basin for the period 1981–2010 were acquired from the Ghana Meteorological Agency, the National Meteorological Service of Togo and the Benin Meteorological Department. Additionally, due to the limited spatial allocation of weather stations, gridded daily rainfall data for 22 gridded points were sourced from CHIRPS, as presented in Table 1 and shown in Figure 1. Daily rainfall CHIRPS data were extracted in R software (Packages: ncdf4 and raster) applying respective locations of the 22 gridded points for 1981–2010. Several research studies (e.g., Dembélé et al. 2020; Muthoni 2020; Satgé et al. 2020) have applied CHIRPS in both the Volta basin and the West African region and have proven that it can accurately estimate station data, and therefore the CHIRPS data can be used in place of station data.

The study used rainfall simulations from global circulation models (GCMs) under the Coordinated Regional Climate Downscaling Experiment (CORDEX-Africa) (Samuelsson et al. 2011; Kjellström et al. 2016). The GCMs were downscaled by the Rossby Centre regional atmospheric model (RCA4) at a spatial resolution of 0.44°×0.44° (∼50 km×50 km). Table 2 shows the climate models and the institute that created them. The GCMs-RCMs were selected because they have been widely used across the Volta basin (e.g., Kunstmann & Jung 2005; Annor et al. 2017). In addition, Akinsanola et al. (2017) and Agyekum et al. (2018) used these GCMs to evaluate rainfall simulations over West Africa and have attested to their suitability. The study considered climate change datasets under representative concentration pathways (RCPs) 4.5 and 8.5 for the future period 2021–2050.

Using Microsoft Excel and Rclimdex package, the study checked for data quality for the eight stations (Natitingou, Kete-Krachi, Yendi, Dapaong, Kara, Mango, Niamtougou and Sokode). The CHIRPS rainfall data were used to accurately fill rainfall data gaps detected in the Kete-Krachi (1–30 June 1984) and Niamtougou (2006) (see Aguilar et al. 2009; Larbi et al. 2018).

To evaluate the efficiency of the RCMs, the simulated historical rainfall was compared with the observed rainfall at the mean monthly scale using the Taylor diagram. The performance evaluation was done at a monthly scale because it best shows the characteristics of rainfall change (Gulacha & Mulungu 2016). The commonly used statistical measures like Pearson's correlation coefficient (r), standard deviation and root-mean-square error (RMSE) were employed to evaluate the effectiveness of the RCMs. The position of each model and the ensemble mean in the acceptable range of the statistical measures [(r=−1 to 1) and (RMSE=0–⁠, with 0 being the perfect fit)] showed how well it simulates rainfall in the basin (Moriasi et al. 2007; Dembélé & Zwart 2016; Bessah et al. 2020).

To explore the occurrence of rainfall in the ORB, the monthly rainfall analysis was conducted for both the observed (1981–2010) and future periods (2021–2050) under RCP4.5 and RCP8.5 scenarios. The percentage change in rainfall for the peak month (August) under the future scenarios was computed relative to the observed. Also, the amount and rate change in rainfall for rainy and dry seasons were determined.

Variations and trends in rainfall at an annual scale for the historical period (1981–2010) and the future period (2021–2050) under RCP4.5 and RCP8.5 scenarios were analyzed using the coefficient of variation (CV) and the standardized anomaly index (SAI).

The study employed the Mann–Kendall (MK) trend statistics to assess trends in rainfall, and Sen's slope estimator was also used to determine the magnitude of the trends over the ORB. The MAKESENS Software was used to compute the trends at a 5% significance level. The test is universally acknowledged due to its robustness, least susceptible to outliers and appropriate for discovering tendencies in time series records (Siraj et al. 2013). It is the most commonly recommended test, and it has been used in a number of research to assess trends in rainfall (Yue et al. 2002; Seleshi & Zanke 2004; Kiros et al. 2016). The MK test compares the null hypothesis (H_o) of no trend to the alternative hypothesis (H₁) that there is a trend (Önöz & Bayazit 2003). Positive values of the MK test show rising trends, whereas negative values indicate declining trends. Sen's slope (β) estimation test computes the gradient (i.e., the linear rate of change), and it is a linear regression test. A positive value of β demonstrates an ‘upward trend’ or an increase, while a negative value shows a ‘downward trend’ or a decrease. No change is represented by a Zero β (Ayanlade et al. 2018). The rainfall change is the difference in yearly average rainfall between the observed (1981–2010) and future periods (2021–2050).

The result obtained for comparing the RCMs with the observation is demonstrated on the Taylor diagram in Figure 2. It can be seen that the correlation values obtained from the comparison are above 0.9, with a standard deviation value below 2.0 and an RMSE value below 0.75. The CNRM-CM5 model had the strongest correlation (R=0.99), the least centered RMSE (below 0.25) and a lowest standard deviation (below 1.25). Although the GFDL-ESM2M model was found to be the weakest, it recorded a correlation of 0.93, a centered RMSE below 0.75 and a standard deviation below 1.75. Although the CNRM-CM5 model performed best among the individual models, the models' ensemble also performed better than most single models with a correlation of 0.98, RMSE below 0.25 and standard deviation below 1.25. The findings are similar to those of Akinsanola et al. (2017) and Lin et al. (2020), who also noted that models' ensemble mean makes the representation of rainfall characteristics better than the majority of single models. Generally, the results show a close match between the RCMs and ensemble simulated rainfall pattern and observation.

Figure 3 provides the result from the bias-correction of the eight CORDEX-Africa RCMs. The correlation between the raw models' ensemble and observation were 0.76 and the NSE value of 0.53. The uncorrected models each show clear variances in how it reproduces historical rainfall (Figure 3(a)). However, after bias-correction, the individual models and their ensemble are seen to be enhanced (Figure 3(b)). After bias-correction, a correlation between the ensemble and observed was 0.91 with an improved NSE (0.98). Generally, it is observed that the corrected individual RCMs and their ensemble were improved to reproduce the rainfall pattern demonstrated by the observed records at the monthly scale. Hence, the bias-corrected RCMs can be said to represent the ORB and are considered reliable for the analysis.

Figure 4 illustrates the annual cycle of rainfall per month for both observed (1981–2010) and near-future periods (2021–2050) under RCP4.5 and RCP8.5. It is observed that the monthly rainfall for the future period would have its peak in August just like the observed period. The amount of rainfall for the peak season, August, is revealed to be 246.1 mm for the observed period; 246.8 mm (about 0.26% peak increment) and 269 mm (about 9.3% increment) of rainfall in the near-future period for RCP4.5 and RCP8.5, respectively. The expected rise in rainfall during the peak month could cause the basin to experience some flooding episodes, and great disturbance to agricultural production (crops and livestock). It can also lead to the outbreak of rain-related diseases like malaria. It is worthy to note that the basin's adaptation and mitigation efforts be strengthened during this period so as to help curb the occurrence of any devastating situations.

The amount of rainfall for the rainy season (April–October) (Klassou & Komi 2021) is expected to be around 947.7 mm (a decrease of about 8.8%) under the RCP4.5 scenario and around 1,004 mm (3.4% decrease in rainfall amount) for the RCP8.5 scenario comparing with the observed, which recorded 1,038.8 mm of rainfall. For the dry season (November–March), the amount of rainfall is expected to be around 22.5 mm (a decrease of about 36.4%) under RCP4.5 and about 24 mm (a decrease of about 32.3%) for the RCP8.5 scenario in relation to the observed rainfall of 35.4 mm. As water is crucial to crop production in the basin and agriculture in the region being highly rainfall-dependent, the projections of future rainfall change could greatly influence crop production (Reilly et al. 2003; Olesen et al. 2007; Gornall et al. 2010).

Mean, standard deviation (SD) and CV were computed for 1981–2010 and 2021–2050 (Table 4). During the observed period, mean annual rainfall was 1,073.9 mm which ranged between 783.4 mm at GRID3 and 1,464.8 mm at GRID20. The mean annual rainfall during the 2021–2050 period recorded 970.2 mm under RCP4.5, which ranged between 640.2 mm at GRID3 and 1,408.3 mm at Sokode; and 1,027.9 mm under the RCP8.5 scenario, which also ranged from 758.4 mm at GRID3 to 1,287.1 mm at Sokode. It is observed that the lower basin receives a high amount of rainfall as compared to the middle and upper basins during the observed and future periods. Although rainfall in the lower basin would rise in the future period, the amount would be reduced. The study discovered a reduction in the mean annual rainfall for the future period under both emission scenarios. This could reflect the predictions of the IPCC (2007) that subtropical areas would experience a decreased rainfall due to the changing climate.

Figure 5 illustrates the allocation of rainfall spatially at the basin for observed and future periods. It is generally seen that rainfall in the basin is more pronounced in the lower basin as compared to the upper basin. Rainfall would generally decrease entirely across the basin under the RCP4.5 scenario. However, under RCP8.5, rainfall in the lower basin would further decrease, while the upper basin would experience a slight recovery of rainfall. Similarly, rainfall is expected to decline in future under both climate scenarios at a temporal scale (Figure 6). From the time series, it is detected that annual rainfall in the basin during the historical period was between 835.5 mm (1983) and 1,239.1 mm (2009). In the future period and under RCP4.5, annual rainfall is expected to range between 842.4 mm (2027) and 1,082.3 mm (2037); and between 900.6 mm (2041) and 1,126.1 mm (2028) of rainfall for RCP8.5. Declined rainfall anticipated at both spatial and temporal scales could severely impact agricultural planning and crop production in the basin. In addition, it could greatly impact streamflow and water levels in the basin. Subsequently, the existing adaptation strategies in the basin must be evaluated in order to ensure that the livelihoods of peripheral communities are not hindered by expected changes.

Figure 7 and Table 5 show the projected changes in rainfall at the ORB. For RCP4.5, almost all stations would experience a reduced amount of rainfall except for the Sokode station which recorded an increment. Similarly, under RCP8.5 in the future, the majority of the stations in the basin would experience a reduced amount of rainfall (particularly stations in the lower basin), except for a few stations which would record an increment in rainfall. Rainfed agriculture in the West African sub-region could significantly be at risk (Owusu & Waylen 2009). In addition, Lare & Nicholson (1994) have pointed out that inadequate large-scale rainfall, as shown at the stations, can affect ecosystems. The MK test for annual rainfall in the basin revealed a significant increasing trend as well as stations such as GRID1-GRID12, GRID 15, Natitingou and Dapaong for the observed period (1981–2010). The MK test for the basin during the future period (2021–2050) under RCP4.5 exposed an insignificant increasing trend at a 5% significant level. However, the Niamtougou station revealed a significant increasing trend. Rainfall across the basin revealed an insignificant decreasing trend in the future (2021–2050) for the RCP8.5 scenario (Table 6).

The standard deviation ranged between 94.8 mm at GRID1 and 320.9 mm of rainfall at Kete-Krachi with a mean value of 104.1 mm. CV varied from 10.2% at GRID16 to 23.6% at Kete-Krachi with a mean CV of 9.7%, indicating less rainfall variability (<20%) according to Asfaw et al. (2018) and Alemu & Bawoke (2019). The moderate variability in rainfall at Kete-Krachi (23.6%) and Dapaong (20.3%) suggests that the amount of water available in these areas was somewhat more erratic compared to the areas with low CV (Alemu & Bawoke 2019).

The mean standard deviation recorded under RCP4.5 was 52.8 mm for the basin, which varied from 56.4 mm at Fada to 100.9 mm at Niamtougou. Under the RCP8.5, the mean standard deviation is 51.4 mm for the basin, ranging between 54.5 mm at GRID3 and 91.2 mm at Sokode. The mean CV in the basin recorded for RCP4.5 was 5.4% ranging from 5.4% at GRID19 to 9.7% at GRID3. The mean CV in the basin under the RCP8.5 scenario recorded 5% which ranged between 5% at GRID16 and 9% at Natitingou. This shows a low variation of rainfall for RCP4.5 and RCP8.5 scenarios. Although almost all stations revealed a low to moderate variation in rainfall amount for the understudied periods, studies such as Nyatuame & Agodzo (2017), Ayanlade et al. (2018) and Alemu & Bawoke (2019) have stated that such variations can adversely impact agriculture and water management. The spatial distribution of CV for annual rainfall is given in Figure 8. The CV for the entire basin was found to be high during the historical period but shows a general increase from the lower basin to the upper basin.

The annual rainfall anomalies in the ORB for the observed (1981–2010) and future periods (2021–2050) under RCPs 4.5 and 8.5 scenarios are shown in Figures 9 and 10, respectively. The results exposed the year-to-year variations in rainfall across the basin. The year 2009 is seen to have had the highest positive anomaly (+1.58), while 1983 had the highest negative anomaly (−2.29). This conforms with the study by Kasei et al. (2010), which found out that 1983 was the driest year in the Volta Basin. More than 90% of the basin was in a severe state of drought and a moderate drought occurred prior to 1982. From the results, the early 1980s experienced pronounced negative anomalies. This shows a relationship with the historic drought in the basin and West Africa between 1961 and 2005 (Kasei et al. 2010). This resulted in the loss of lives and farm animals in desertlike areas of Burkina Faso (Dembélé & Zwart 2016). Generally, the historical period had 4 very wet years (1991, 1994, 2003 and 2009), 1 moderately wet year (1999), 5 moderately dry years (1982, 1984, 1990, 2001 and 2006), 1 extremely dry year (1983) and the remaining 19 years being near normal years. The SAI for the future period is displayed in Figure 10. In the future period, the negative anomaly would be more pronounced in 2027 (−2.42) under RCP4.5 and in 2041 (−2.48) under the RCP8.5 scenario, indicating a possibility of extremely dry conditions. The positive anomaly is expected to be more pronounced in 2037 (2.12) under RCP4.5 and 2028 (1.91) under RCP8.5, indicating extremely wet and very wet periods, respectively (McKee et al. 1993). Thus, the RCP4.5 scenario would experience 1 extremely wet year (2037), 2 very wet years (2024 and 2044), 2 moderately wet years (2035 and 2036), 3 moderately dry years (2023, 2026 and 2047), 1 extremely dry year (2027) and the rest of the 21 years experiencing near normal years. The RCP8.5 scenario would experience 2 very wet years (2028 and 2045), 3 moderately wet years (2033, 2035 and 2046), 4 moderately dry years (2025, 2039, 2043 and 2049), 1 severely dry year (2022), 1 extremely dry year (2041) and the remaining 19 years experiencing near normal years. The predicted very wet and extremely wet years could lead to flood incidences, which could have impacts on human lives and property (Klassou & Komi 2021). Water scarcity in the basin could occur from the basin's moderately dry, severely dry and extremely dry years. Long dry seasons caused by climate change result in water scarcity, harming people's health and crop productivity (Zhang et al. 2015).

Cropland is seen to be the dominant land use in the ORB from the 2016 land-use land-cover map (Figure 11). Farming covers areas from latitude 9°N to the northern part of the basin, which was observed to have an annual rainfall between 800 and 1,300 mm (Figure 5). The projected changes under RCP4.5 (decrease between 20 and 180 mm) could make the forest areas below latitude 9°N also suitable climatologically for farming if soil and other factors are conducive (Travis 2016). Moreover, the projected decrease in the upper basin may influence a shift or gradual migration of cropping areas toward the lower basin. On the other hand, the projected increase in annual rainfall under RCP8.5 from the middle basin toward the upper basin could result in changes of cropping systems that make use of the additional rainfall amount (Chemura et al. 2020). Also, it might imply that more water-resilient varieties would be needed. Grassland and cropland may be expanded to meet up with demands of food and livestock feed as climate change adaptation or coping strategies in the ORB. This means that as an exchange for higher agricultural productivity in the basin, the conversions to cropland can affect the ecosystem services predominantly, especially water yield due to increased runoff (Brink & Eva 2009; Li et al. 2018). Changes in land use, particularly the continuous expansion of cropland, have contributed to the degradation of vegetation and water scarcity in recent years, as reported elsewhere (Sajikumar & Remya 2015; Woldesenbet et al. 2017; Balist et al. 2022). Land-use/land-cover changes alter the rainfall path into runoff by affecting important hydrological elements like surface runoff, groundwater recharge, infiltration, interception and evaporation (Song & Deng 2017; Woldesenbet et al. 2017; Balist et al. 2022). Water yield is one of the most important ecosystem services, and it is critical to the regional economy and ecosystem's long-term sustainability (Yang et al. 2021). The spatial distribution of CV of rainfall in the basin (Figure 8) shows that extreme changes may not occur and, therefore, could prevent sudden migration of farm activities from one location to the other. As the ORB is also crucial to the West African sub-region's economy, it is obvious that the expansion of cropland and decline in tree cover may become vital environmental stressors and subsequently impact surface runoff and water yield in the basin. In addition, the anticipated decline in rainfall in the basin during the near-future period (2021–2050) under both RCP4.5 and RCP8.5 scenarios calls for an evaluation of land use and climate impacts on water yield in the basin in order to know the future water availability.

This work has investigated rainfall variations and anticipated change over the ORB for observed (1981–2010) and future periods (2021–2050) under RCP4.5 and RCP8.5, using data from meteorological stations, CHIRPS gridded-observed data as well as CORDEX-Africa. Percentage change in rainfall for the peak month as well as for rainy and dry seasons under the two climate scenarios was determined at a monthly scale. The mean annual rainfall in the ORB was 1,073.9 mm, SD of 104.1 mm and CV of 9.7% for the observed period. The annual average rain in the future period under RCP4.5 recorded 970.2 mm, SD of 52.8 mm and CV of 5.4%. Under RCP8.5, the basin recorded 1,027.9 mm average rainfall which had SD and CV of 51.4 mm and 5%, respectively. Generally, rainfall amount would decline under both emission scenarios. At a monthly scale, percentage rainfall increment for the peak month (August) is expected to be around 0.26% for RCP4.5 and 9.3% under RCP8.5. The amount of rainfall for the rainy (April–October) and dry seasons (November–March) would experience a decrease under both climate scenarios. Also, during the observed period, the highest positive anomaly of 1.58 was seen in 2009, with the highest negative anomaly (−2.29) recorded in 1983 revealing a connection with historic drought conditions. The year 2037 would experience a more positive anomaly (2.12), while a high negative anomaly (−2.42) is expected to be recorded in 2027 under RCP4.5 disclosing a possible very wet and extremely dry situation. Also, a high positive anomaly in the year 2028 (1.91) and a high negative anomaly (−2.48) in the year 2041 under RCP8.5 are observed, unveiling a very wet and extremely dry condition to be expected, respectively. The trend analysis presented both statistically nonsignificant growing and declining movement for RCP4.5 and RCP8.5, respectively, with the historical period revealing a significant increasing trend. The expected decrease in rainfall is cause for concern because it signifies that the dependent countries' source of livelihood and economic boost could be endangered. In addition, the expected moderately dry, severely dry and extremely dry years in the future could throw off the balance between water demand and availability, putting basin-dependent countries in a water stress situation. Although the study relied on gridded datasets to complement missing data, its finding offers crucial information for a better understanding of the spatiotemporal distribution and variations necessary for the sustenance of economic development and livelihoods through such sectors as agriculture, water resources, ecosystem and biodiversity. However, the impact of projections on land use and water yield could only be inferred. Therefore, modeling the basin hydrology for the accessibility of water in the future is advised. Research would be necessary to assess the impacts of land-use and land-cover changes on rainfall variability and change over the basin. Additionally, the future study should concentrate on climate change resilient management strategies in the ORB.

The German Federal Ministry of Education and Research (BMBF) via the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) supported this work, which is a section of the first authors' Doctoral study at Université d'Abomey-Calavi, Benin. The authors acknowledge the national meteorological agencies of Ghana, Togo and Benin for the rainfall data, and also CHIRPS and the CORDEX-Africa project for making their data accessible.

The authors declare no conflict of interest.

Data cannot be made publicly available; readers should contact the corresponding author for details.