Spatial and temporal variation in the isotopic composition of Ethiopian precipitation

Journal Pre-proofs Research papers Spatial and temporal variation in the isotopic composition of Ethiopian precipitation Zelalem K. Bedaso, Nicole M. DeLuca, Naomi E. Levin, Benjamin F. Zaitchik, Darryn W. Waugh, Shuang-Ye Wu, Ciaran J. Harman, Dula Shanko PII: DOI: Reference:

S0022-1694(19)31099-6 https://doi.org/10.1016/j.jhydrol.2019.124364 HYDROL 124364

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Journal of Hydrology

Received Date: Revised Date: Accepted Date:

5 April 2019 25 September 2019 14 November 2019

Please cite this article as: Bedaso, Z.K., DeLuca, N.M., Levin, N.E., Zaitchik, B.F., Waugh, D.W., Wu, S-Y., Harman, C.J., Shanko, D., Spatial and temporal variation in the isotopic composition of Ethiopian precipitation, Journal of Hydrology (2019), doi: https://doi.org/10.1016/j.jhydrol.2019.124364

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Spatial and temporal variation in the isotopic composition of Ethiopian precipitation

Zelalem K. Bedasoa*, Nicole M. DeLucab, Naomi E. Levinc, Benjamin F. Zaitchikb, Darryn W. Waughb, Shuang-Ye Wua, Ciaran J. Harmand, Dula Shankoe

a

Department of Geology, University of Dayton, 300 College Park, Dayton, OH 45469-

2364, USA

b

Department of Earth and Planetary Sciences, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA

c

Department of Earth and Environmental Sciences, University of Michigan, 1100 North

University Avenue, Ann Arbor, MI 48109-1005, USA

d Department

of Geography and Environmental Engineering, Johns Hopkins University, 3400 N.

Charles Street, 313 Ames Hall, Baltimore, MD 21218, USA

e National

Meteorological Agency of Ethiopia, PO Box 1090, Addis Ababa, Ethiopia

*corresponding author: [email protected]

6126 words (main text including abstract), 5 figures, 5 tables

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Supplemental information: Table S1, Table S2, Table S3, Table S4 and Figure S1-S4

Highlights:     

Our precipitation isotope data shows the need for high-frequency spatial sampling. Daily 18Oprecip values decrease by >8‰ synchronously at 3 stations in August 2013. The northwestern highland stations show similar seasonal trends in 18Oprecip. Seasonality of 18Oprecip at Jijiga is less distinct than in northwest highlands. Storm intensity and convection likely explain seasonal isotope variations.

Abstract

Stable isotopes of oxygen (18O) and deuterium (2H) in precipitation are used as tracers of the hydrologic cycle in studies of both past and present climate. In Ethiopia, a drought sensitive region where direct observations of climate are limited, understanding the stable isotopic composition of precipitation can provide an integrated view of the hydroclimate today and help interpret records of climate variability in the past. To date, the isotopic composition of precipitation in Ethiopia is known primarily from monthly precipitation collections from one long-term monitoring station in Addis Ababa. Here we report 18O and 2H values of precipitation from a 16-month long sampling campaign at four stations in Ethiopia that represent different climate regimes. Precipitation samples collected at daily, weekly and monthly intervals

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between August 2012 and November 2013 at these stations exhibit a large range of 18O (-12.6 to +10.3 ‰), 2H (-91.8‰ to +80.8 ‰), and d-excess values (-9.9‰ to +29.9‰). The four stations have similar amount-weighted mean 18O values, but exhibit different seasonal pattern in 18O values as clearly shown in daily and weekly data. The most striking feature of this dataset is the synchronous decrease in 18O values in daily and weekly precipitation samples from the three stations in the northwestern highlands (Gondar, Debre Markos, Jimma) during the first three weeks of August 2013. This decrease in 18O values does not correlate strongly to precipitation amount but it is likely due to a combination of increased storm intensity and deep convection at the time of lower 18O values of precipitation. Our high-resolution dataset provides new insights into the controls on the isotopic composition of rainfall in Ethiopia and how we might use it to understand hydroclimate variability today and in the past.

Key words: precipitation, stable isotopes, d-excess, convective storm, HYSPLIT, Ethiopia

1. Introduction

The stable isotopes of oxygen (δ18O) and hydrogen (δ2H) in precipitation are used as conservative tracers in the hydrologic cycle because they track both local and large-scale atmospheric processes (Dansgaard, 1964; Gat, 1996; Darling et al., 2005). In order to use this tool to its full potential, it is critical to establish a detailed understanding of how the isotopic composition of precipitation varies spatially and temporally. Isotopes in precipitation vary due to the fractionation of water isotopologues (e.g. 1H216O, 1H2H16O and 1H218O) during evaporation, condensation and other processes within the hydrologic cycle. In the tropics, the isotopic

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composition of precipitation is controlled by the amount of precipitation, distance from the moisture source, altitude, continental recycling, and convective activity (Dansgaard, 1964; Salati et al., 1979; Rozanski et al., 1993; Worden et al., 2007; Risi et al., 2008). The isotopic composition of precipitation is monitored by the Global Network for Isotopes in Precipitation (GNIP) operated by the International Atomic Energy Agency and the World Meteorological Organization (IAEA/WMO) since the 1960s. GNIP includes over a 1,000 monthly precipitation collection stations in more than 125 countries (IAEA, 2013) with uneven spatial distribution. In Ethiopia, there is only one long-term GNIP station located in Addis Ababa (Figure 1) collecting monthly data only. Because of these limited data, our understanding of the spatial variation in the isotopic composition of meteoric water in Ethiopia comes mostly from groundwater, rivers, lakes and spotty rainwater collections (e.g. McKenzie et al., 2001; Darling and Giza, 2002; Ayenew et al., 2008; Kebede et al., 2008; Levin et al., 2009; Wynn and Bedaso, 2010).

The existing isotopic data indicate that the 18O values of Ethiopian precipitation are higher than expected from models given its inland location (563 km from the nearest coastline) and high elevation (2386 m above mean sea level) (Rozanski et al., 1996; Bowen and Wilkinson, 2002; Levin et al., 2009; Kebede and Travi, 2012). The causes for such anomalously high values, and isotopic variations in general, are poorly understood largely because of such limitations in the available data: 1) the monthly data only reflect the mean isotopic composition of water vapor in the atmosphere during these time intervals (Darling et al., 2005) and 2) a single location (Addis Ababa) cannot capture the complicated topography and diverse hydroclimates in the region. In order to characterize and understand the driving forces for the isotopic variability of meteoric water in the region, we need to generate isotopic data of higher temporal resolution from the

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country’s different climatic regimes. Such datasets have the potential to expand the utility of oxygen and hydrogen isotopes in studies of hydrology, water resources, climate sensitivity and change in the region, as well as providing a baseline for interpreting stable isotopic records of climate and environmental change in the past (e.g. Sultan et al., 1997; Aronson et al., 2008; Bedaso et al., 2010, 2013; Levin et al., 2011; Tierney et al., 2011; Abouelmagd et al., 2012; Feakins, 2013; Tierney et al., 2017; Bedaso et al., 2019)

In this study, we report the 18O and 2H values of precipitation collected at daily, weekly, and monthly intervals between August 2012 and November 2013 at four locations in Ethiopia: Gondar, Debre Markos, Jimma and Jijiga (Figure 1). Using these data, this study aims to 1) evaluate the importance of high-frequency precipitation sampling for isotope-based investigations of climatic processes, 2) characterize the temporal and spatial distributions of 18O and 2H values in precipitation within Ethiopia, 3) investigate how variation in the isotopic composition of precipitation relates to climatic and meteorological parameters and how in turn we can use isotopic data as indicators of climatic processes in the present and past, and 4) evaluate whether the isotopic composition of precipitation from the long-term GNIP station in Addis Ababa is representative of precipitation from elsewhere in Ethiopia.

2. Methods

2. 1. Precipitation collections

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In total, 894 precipitation samples were collected at four meteorological stations owned and operated by the Ethiopian National Meteorological Agency at daily, weekly and monthly intervals between August 2012 and November 2013. These four precipitation sampling stations (Gondar, Debre Markos, Jimma and Jijiga) were selected to represent different physiographic and climatic regions of Ethiopia (Figure 1). The Gondar (elev. 2133 m) and Debre Markos (elev. 2446 m) stations are located in the western highlands that experience one rainy season, the “Kiremt”, per year. As the northernmost station, Gondar has a shorter rainy season than Debre Markos, and its proximity to Lake Tana could influence the precipitation dynamics. The Jimma station (elev. 1780 m), located in the southwestern corner of the Ethiopian highlands, experiences rainfall year-round due to the passage of the Inter Tropical Convergence Zone (ITCZ) (Diro et al., 2008). The Jijiga station (elev. 1609 m) in the southeastern highlands experiences two distinct rainy seasons but receives considerably less precipitation each year than other three stations (Figure 1). Custom-made devices, comprised of a funnel affixed to a plastic jug, were used to collect precipitation. Mineral oil (density~0.8 g/cc) was placed in the jugs of the weekly and monthly collectors to prevent evaporation of water. Precipitation samples were collected in the morning between 9:00-11:00 AM at each station. The volumetric amount of precipitation and the prevailing weather conditions were documented with each collection. The daily water samples were stored in 3.8 ml (1 dram) glass vials with polycone seal caps. The weekly and monthly samples were stored in 15 ml glass vials with polycone seal caps. The seals of all vials were wrapped with Parafilm to prevent evaporation and the vials were stored in a covered box. Daily precipitations samples were passed through 0.45-μm filter to remove particulates. Following procedures described by West et al. (2010), the weekly and monthly water samples were separated from the mineral oil using a separatory funnel, treated with

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activated charcoal for 24 hours to remove organics, and then passed through a 0.2-μm filter to remove particulates.

2.2 Laboratory analyses

Precipitation samples were analyzed for 18O/16O and 2H/1H ratios using a Los Gatos Research (LGR) liquid water isotope analyzer, model LWIA-24EP and LGR triple water isotope analyzer, model IWA-45EP, via off-axis Integrated Cavity Output spectroscopy at Johns Hopkins University (JHU) following the IAEA standard procedure (IAEA, 2009). A subset of the samples from the daily collections were analyzed at the University of Utah’s SIRFER laboratory using a Picarro L2130-I analyzer via cavity ring-down spectroscopy. To confirm the mineral oil used in the weekly and monthly samples has no effect on the isotopic composition, we analyzed randomly selected samples on isotope ratio mass spectrometer and compared with result of the LGR analysis. We found no significant difference between the two analyses of the two samples.

Results are reported as δ18O and δ2H values, which are defined as

 Rsample  1 *1000 , R   Standard

 2 H or  18O  

where Rsample and Rstandard are the 2H/1H and 18O/16O ratios in the sample and standard, the Vienna Standard Mean Ocean Water (VSMOW). Based on Craig (1961), d-excess is calculated as d-excess = δ2H – 8×δ18O

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In reporting 18O, 2H and d-excess values in the text below we use the subscript "precip“ to indicate that these values are associated with precipitation.

Repeated measurements of the commercially available United States Geological Survey (USGS) isotopic reference (USGS 45, 46, 47 and 48) were used as internal and external references calibrated to the VSMOW-SLAP scale. An analytical run, five samples bracketed by standards, typically yielded standard deviations of 0.1‰ for δ18O and 0.3‰ for δ2H at JHU and 0.04‰ for δ18O and 0.3‰ for δ2H at SIRFER. The external precision of d-excessprecip is calculated as 0.8‰ and 0.4‰ for the analyses at JHU and SIRFER for respectivly. Outliers in this dataset were identified using d-excess values. Two weekly data points (JIJ-W-120917 and JIM-W-130520) have very low d-excess values (-58.1‰ and -26.7‰) whereas d-excess values of all other samples were > -10‰. The combination of very low d-excessprecip and high δ 18Oprecip values from these samples suggests post-collection evaporation might have modified their isotopic composition. For this reason, they were excluded from plots and statistical analyses, but still included in Table S1 for completeness.

2.3 Climate data and HYSPLIT trajectories

In order to explore how climate conditions affect the isotopic composition of precipitation, we used the nearest grid point ERA-Interim daily 24-hours average climate reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF), including such variables as 2-meter air temperature and relative humidity. Intercomparisons of reanalysis products performed for Ethiopia have shown that ERA-Interim compares favorably with other available

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reanalysis data (Tesfaye et al., 2017). We measured the amount of water associated with each collection interval to determine the association between precipitation amount and the isotopic composition of precipitation (Table S1). In addition, as a proxy for convective intensity, the daily Outgoing Longwave Radiation (OLR) data from the nearest grid point were obtained from the National Oceanic and Atmospheric Administration (NOAA) (https://www.esrl.noaa.gov/psd/data/gridded/data.interp_OLR.html). We used the NOAA Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler, 2011) to generate backward trajectories of air in order to evaluate potential changes in moisture source at the different collection stations during the study interval. The Global Data Assimilation System (GDAS) 0.5 data were used for HYSPLIT model runs, and a starting height of 1000 m above ground level (AGL) was chosen to target vapor-rich parcels as most of the moisture is found within the lower 2000 m of the atmosphere (Wallace and Hobbs, 2006). For sensitivity tests, different starting heights, 500 m and 2000 m, were also used for the trajectory calculations with similar results. The daily vapor trajectories back to 240 hours were calculated for six time intervals (Aug.-Sep. 2012, Nov. 2012-Jan. 2013, Feb.-Apr 2013, May-Jun. 2013, Aug. 2013 and Sep. 2013), selected based on δ18Oprecip value variations (e.g. low δ18Oprecip values in August 2013, high δ18Oprecip values in March 2013, see gray boxes on Figures 2). For the trajectory calculation 240 hours (10 days) are chosen because it is an average residence time of moisture in the atmosphere (Gat, 2000) and the precipitation collection stations in Ethiopia are located well in-land. All back-trajectories were started at noon (12:00 local time) because our precipitation samples for isotopic analysis were typically collected in the morning hours.

3. Results 9

3.1 Isotopic variation of precipitation with different sampling intervals

The stable isotopic composition of all samples collected from the four stations between August 2012 and November 2013 are summarized in Table 1 and Figure 2. All original data are provided in Table S1. Across the four stations, the daily data exhibit a larger range in δ18Oprecip and 2Hprecip values (-12.6 to +10.3 ‰ for 18O; -91.8‰ to +80.8 ‰ for 2H) than the weekly and monthly data (Figure 2, Table 1). The daily d-excessprecip values range from -9.9‰ and +29.9‰. The limited number of data points for some time intervals (e.g. January – March at Gondar, December – February at Jijiga) reflects the absence of rainfall (i.e. the dry season) not missing data (Figure 2). Considering that 2Hprecip values generally vary linearly with 18Oprecip values (Craig, 1961), for the remainder of this paper we mainly discuss variation in the observed 18Oprecip values and use d-excessprecip values to track deviations from the average 18Oprecip2Hprecip relationship (Figure 3). Amount-weighted means of 18Oprecip values are used for comparisons of 18Oprecip values from different collection intervals (i.e. daily, weekly, monthly).

Comparisons of the amount of precipitation recovered from the daily, weekly, and monthly collectors indicate equivalent recovery and retention of precipitation among the different collectors (Table S1 and S2). The measurements of the amount of monthly rainfall data are within 2σ of the long-term monthly precipitation means for each meteorological station (Figure 1B-F). The amount-weighted mean 18Oprecip and 2Hprecip values are highly consistent among daily, weekly and monthly data (Table S2). The amount-weighted daily data over the same collection periods are highly correlated with the weekly and monthly data (Table S3). The robust

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nature of the comparison suggests high level of data integrity, and that the samples did not experience significant evaporation or isotopic fractionation during and after sample collection.

3.2 Isotopic distinctions among stations and seasons

There is no significant difference in the amount weighted mean 18Oprecip values of daily samples among the four sampling stations over the 16-month sampling period (ANOVA, p-value > 0.05). These values are similar to the amount-weighted mean δ18Oprecip values from monthly data in Addis Ababa sampled between 1961 and 2009 (-1.3 ± 2.3‰) (Table 1). In contrast, the mean dexcessprecip values are significantly different among the four stations (p-value < 0.05). Debre Markos has the highest weighted mean d-excessprecip values (18.2 ± 4.6‰) and Jijiga yields the lowest mean d-excess (11.1 ± 4.2‰) (Table 1). These values bracket the amount-weighted annual d-excessprecip value for the GNIP station at Addis Ababa, 13.8 ± 4.2 ‰ (Table 1).

Despite similarities in the weighted-mean 18Oprecip values from the different stations, there are seasonal trends in the daily and weekly 18Oprecip values that distinguish the northwestern highland stations (i.e. Debre Markos, Gondar, Jimma) from the southeastern station of Jijiga (Figure 2). The temporal variations of δ18Oprecip values are similar for the three northwestern highland stations. These stations are characterized by relatively low 18Oprecip values during the main rainy season (June – September) and relatively high 18Oprecip values for the rest of the year (Figure 2 A, B, D). They also exhibit seasonal trends in d-excessprecip values, with the lowest values between November and May (Figure 2 A, B, D). The seasonal patterns in d-excessprecip values are not as distinct as those observed in 18Oprecip values. There are some seasonal

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variations in the 18Oprecip and d-excessprecip values from Jijiga, although they are not as strong as those observed in the northwest highland stations (Figures 2C).

We examined the seasonal pattern (Figure 2) and the correlations (Figure 4, Table 2) between 18Oprecip values and local meteorological parameters (precipitation amount, temperature, relative humidity and OLR). There is a weak negative correlation between 18Oprecip values and precipitation amount at all stations. 18Oprecip values decrease with increasing relative humidity and decreasing outgoing long wavelength radiation (OLR) at all stations. There is no significant correlation observed between 18Oprecip values and temperature at the stations except for Debre Markos. More detailed discussion on this is presented later in section 4.3.1.

Comparison of Local Meteoric Water Lines (LMWLs), which are linear regressions of the δ18Oprecip and δ2Hprecip values, provides a way to summarize the distribution of isotopic data from precipitation in one place. The slopes of the amount-weighted LMWLs of the daily, weekly and monthly collections from the four stations in this study cluster around 7.6 ± 0.4 ‰ (Figure 3). The amount-weighted LMWLs slopes from the daily, weekly and monthly data at all stations are similar and do not show statistically significant differences except for the weekly collection at Jijiga slope, which could be caused by sub-cloud evaporation. The slopes from all stations are close to the slope determined from the long-term monthly collections at Addis Ababa (7.0 ± 0.2) and elsewhere in eastern Africa (7.0 ± 0.2 to 8.1 ± 0.2) (Table 3; Rozanski et al., 1996). However, the intercepts of the MWLs at our four stations vary from 10.5 ± 0.3 ‰ at Jijiga to 18.3 ± 0.4 ‰ at Debre Markos. The range in the observed intercepts is similar to the range observed at eastern and central Africa from 6.8 ± 0.7 ‰ at Kericho, Kenya to 16.2 ± 0.3 ‰ at

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Virunga, D. R. Congo (Table 3; Rozanski et al., 1996; Balagizi et al., 2018). The intercept for the MWL at the Addis Ababa GNIP station (11.5 ± 0.6 ‰) plots within the range of intercepts observed from the stations monitored for this study.

4. Discussion

4.1 Temporal trends

Precipitation samples collected at different frequencies enable us to evaluate whether daily and weekly isotopic data add value to monthly data, which is the standard collection interval for GNIP datasets (IAEA, 2013). Although daily, weekly and monthly data show similar temporal trends in 18Oprecip and d-excessprecip values (Figure 2), the daily data show the highest amplitude in the seasonal variations of 18Oprecip and d-excessprecip values (Figures 2). The availability of daily data makes it possible to examine the influence of a single storm system on the isotopic composition of precipitation. For example, the low 18Oprecip values of the weekly and monthly data at Jijiga in October 2012 are strongly influenced by a single storm event on October 27, 2012 (Figure 2). The most prominent feature of the precipitation isotope record is the decrease in 18Oprecip values observed in the daily and weekly samples from Gondar, Debre Markos and Jimma during the first three weeks of August 2013 (Figure 2 A, B, D). This trend in 18Oprecip values, which is nearly synchronous at Gondar, Debre Markos and Jimma, cannot be clearly identified with the monthly samples alone. The GNIP monthly rainfall isotope data in Addis Ababa also misses the sub-monthly trend in 18Oprecip values that is evident from both the daily and weekly collections at the three northwestern highlands stations. The regional coherency and

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amplitude of this short decrease in 18Oprecip values (with an amplitude of >4‰ in the weekly collections, > 8‰ in the daily collections) is not evident in the monthly collections because it occurs over a 3-week time interval that spans two of the monthly collection intervals.

To evaluate the climatic controls on the isotopic composition of precipitation, we performed correlation analysis between daily 18Oprecip values and variables representing daily climate conditions (e.g. precipitation amount, OLR, temperature, RH) (Table 2, Figure 4b, f, j, n). Daily 18Oprecip values show significant correlations with precipitation amount, relative humidity and OLR at all stations and with temperature at Debre Markos and Gondar (p<0.05). On the contrary, no significant correlations are observed between monthly 18Oprecip values and these climate factors on monthly time scales except for with relative humidity and OLR at Debre Markos and OLR at Gondar (Table 2). Weekly 18Oprecip values show significant correlation with relative humidity and OLR at the northwestern highland stations (p<0.05), but no correlation with temperature at all four stations, and largely insignificant correlation with precipitation amount (Table 2). The lack of statistical significance between our monthly 18Oprecip values and the climate factors could also be due to small sample size. Nevertheless, our daily and weekly data signifies the need for high frequency sampling of precipitation to provide isotopic variations and examine the climatic controls of isotopes in precipitation that are not evident in our monthly samples collection and the GNIP monthly interval data collection in Addis Ababa.

4.2 Spatial and seasonal variation

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Comparisons of the isotopic composition of precipitation at the four stations sampled for this study reflect climatological differences within Ethiopia. Stations in the northwestern highlands of Ethiopia (Debre Markos, Gondar, and Jimma), follow similar annual trends in daily, weekly and monthly 18Oprecip values with low 18Oprecip values during the main rainy season. This phenomenon is clearly exhibited in the latter half of our sampling campaign when we captured the full rainy season in 2013. Specifically, daily and weekly 18Oprecip values from Gondar, Debre Markos and Jimma were consistently < -2‰ between mid-July and early September in 2013, with minima in daily 18Oprecip values occurring on the same day (August 3, 2013) at Debre Markos (-10.9 ‰) and Gondar (-10.4 ‰), and then 5 days later at Jimma (-10.0‰, August 8, 2013) (Figures 2). Relatively low δ18Oprecip values are also observed at these stations in the beginning of the study period in mid-August 2012, although our sampling did not start early enough to capture the full rainy season in 2012. These trends are consistent with the monthly collections made at Gondar, Debre Markos and the long-term GNIP station at Addis Ababa, which yield lower 18Oprecip values during the June – September rainy season (Figure 2). However, the amplitude of the trends in monthly data is considerably muted relative to the seasonal trends observed in the daily and weekly 18Oprecip values (Figures 2). The seasonal trend in 18Oprecip values from Jijiga in the southeastern Ethiopia highlands is less distinct than the seasonal trends in the northwestern highlands. The variation in d-excessprecip values from the four collection stations is not as coherent as the records of 18Oprecip values, but there is a clear difference in d-excessprecip values among the stations (Table 1).

The spatial and seasonal variation in 18Oprecip and d-excessprecip values among stations we monitored provide avenues to understand the controls on the isotopic composition of

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precipitation in Ethiopia. The clearest separation among the isotopic data from precipitation collected at the four stations is between Jijiga and the northwest highland stations. Jijiga does not exhibit the clear seasonal pattern in 18Oprecip values as the other stations and the d-excessprecip values are lower than the other stations (Table 1). Jijiga’s position in the southeastern plateau, where it receives substantially less annual precipitation, likely explains these distinctions; the storms that result in rainfall in the northwest highlands do not reach Jijiga. The precipitation that does fall in Jijiga is also likely susceptible to evaporation of raindrops which would result in lower d-excessprecip values, as a result of re-evaporation of falling raindrops, which is typical in arid climates (Dansgaard, 1964; Stewart, 1975; Gat, 1996; Guan et al., 2013).

Among the three northwestern highland stations (Gondar, Debre Markos and Jimma), the most striking observations are as follows: 1) there are distinct decreases in 18Oprecip values in the June-September rainy season at all three stations, 2) the seasonal trend in 18Oprecip values is synchronous among the stations, and 3) d-excessprecip values from these three stations do not exhibit seasonal trends that are as distinct as 18Oprecip values but yield the lowest values during the drier part of the year (January – April), when 18Oprecip values are highest (Figures 2). We explore explanations for these observations in the following section (section 4.3) and summarize these explanations in Table 4.

4.3 Explanations for the observed spatial and seasonal isotope variations

4.3.1 Local climate controls on 18Oprecip.

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Precipitation amount: The isotopic composition of precipitation is influenced by the amount of precipitation (amount effect), temperature, distance from the source (continentality), altitude, continental recycling, and convective activity (Dansgaard, 1964; Salati et al., 1979; Rozanski et al., 1993; Worden et al., 2007; Risi et al., 2008). In the tropics, the most obvious explanation between the association of lower 18Oprecip values during the peak of the rainy season would be the “amount effect”, which is a phenomenon observed throughout the tropics where there is a strong negative correlation between the amount of precipitation and monthly 18Oprecip values (Dansgaard, 1964; Rozanski et al., 1993). High rainfall amounts have been traditionally associated with lower 18Oprecip values. This is due to the progressive distillation of the heavier water isotopologues from an air parcel and because raindrops associated with bigger rain events experience less evaporation and less exchange with vapor below the cloud base (Dansgaard, 1964; Gat, 1996; Vuille et al., 2003; Lee and Fung, 2008). However, in this study, we show no significant correlation between monthly 18Oprecip values and the amount of rainfall (p>0.05), and only a weak correlation with daily 18Oprecip values (Figure 4, Table 2). Even in the case of the daily 18Oprecip values that show weak correlation (p<0.05), the correlation coefficient (r) is low (Figure 4, Table 2). The apparent difference in the relationships between precipitation amount and 18Oprecip values for the daily, weekly, and monthly collections is likely due to the strong influence of small storms on the amount-18Oprecip relationship for the daily collections; the correlations weaken for the daily collections if small precipitation amounts (≤ 5 mm) are excluded (r > -0.3 for all sites, p > 0.05 for all sites but Gondar). The strong influence of small precipitation amount samples in the regressions of the daily data also explains why the 18Oprecip amount trends are muted or absent in the weekly and monthly collections, where smaller precipitation amounts that may comprise a daily sample contribute to a smaller proportion of a

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total weekly or monthly sample. Likewise, we also observe that the relationship between 18Oprecip values and precipitation amount is particularly strong for daily collections of rainfall during the dry season (r < -0.4 and p < 0.05 for all sites but Jijiga), where 18Oprecip values of low intensity rains are more affected by re-evaporation of falling raindrops than larger events (Kebede and Travi, 2012). As a result, the very low daily 18Oprecip values that we observe in August 2013 at Gondar, Debre Markos and Jimma cannot exclusively be explained by the increase in the amount of precipitation. This observation agrees with results from other studies that show that the high rainfall amounts and local rainout mechanisms do not fully explain low 18Oprecip values associated with rainy season precipitation in eastern Africa and elsewhere in the tropics (Rozanski et al., 1996; Kebede and Travi, 2012; Otte et al., 2017; Balagizi et al., 2018; Martin et al., 2018).

Evaporation at source and during precipitation: The d-excessprecip values are informative as they can provide insight into synoptic weather systems and moisture source (Guan et al., 2013). The global average d-excess in precipitation is 10‰ (Craig, 1961) and it is attributed to direct moisture from ocean evaporation, but high (> 10‰) d-excessprecip values can be indicative of moisture sources with a continental recycled component (Dansgaard, 1964). The high (> 10‰) d-excessprecip values that we observe in the northwestern highland stations (Figure 3, Table 1) are consistent with evaporative moisture sources from Central Africa, the Red Sea and/or the Mediterranean that have been identified by Viste and Sorteberg (2013a) (Table 4). High dexcessprecip values are also observed elsewhere in Africa, such as in Tanzania, where they have been attributed to the influence of recycled moisture on precipitation (e.g. Otte et al., 2017). The lowest d-excess and highest 18Oprecip values among the northwestern highland stations occur 18

during January-May, when there is the least amount of rain. This might be attributable to subcloud evaporation (i.e. re-evaporation of the falling raindrops) due to low relative humidity compared to the rainy season (18Oprecip inversely related to relative humidity, Figure 4d, h, i, p). Lower d-excessprecip values during short rains is also observed elsewhere in eastern Africa and in the tropics, which corresponds with low intensity rains and lower relative humidity and can result from sub-cloud evaporation of rain droplets (e.g. Otte et al., 2017; Table 4). Role of convection: Convective rainfall makes a significant contribution to rainfall in the tropics and in Ethiopia (Mutai and Ward, 2000; Segele and Lamb, 2005). In the tropics, convective activity is also associated with lower 18Oprecip values and considered to be a strong determinant of low 18Oprecip values with rainy season precipitation (e.g. Tharammal et al., 2017; Martin et al., 2018). Higher rain rates and droplet formation at higher elevations and lower temperatures are associated with lower 18Oprecip values of convective rainfall in the tropics (e.g. Risi et al., 2008; Kurita et al., 2009; Martin et al., 2018). For this reason, we used OLR as a proxy for convective intensity, as it measures top-of-cloud temperature, which is inversely related with the depth of convection (Arkin and Ardanuy, 1989). The northwestern highland stations show clear seasonality in OLR values. OLR is lowest in the rainy season (June -September), corresponding to the lowest 18Oprecip values, suggesting that strong/deep convective activities during the rainy season (Figure 2). 18Oprecip is significantly correlated to OLR for all stations (Figure 4), with a correlation coefficient ranging from 0.31 (Jimma) to 0.5 (Debre Markos). The correlation is even stronger between the 18Oprecip and the OLR values of the previous day (0.36 < r < 0.58), because the precipitation samples are usually collected in the morning, hence represent the rainfall of the previous 24 hours. The similar OLR seasonal variations among the three northwestern highland

19

stations (as with 18Oprecip) also suggest that regional scale convective systems affect the entire northwestern part of Ethiopia during the rainy season. In contrast, OLR in Jijiga does not exhibit a similar seasonality as the rest of the northwestern highland stations, which is consistent with distinct weather conditions in the southeastern highlands.

Using a multiple regression model, we examined how much variability in 18Oprecip could be explained by the combination of all four climate factors (precipitation amount, temperature, OLR, relative humidity, see Table 5). The models based on weekly data results in the highest R2, ranging from 0.37 (Jimma) up to 0.61 (Debre Markos). The model based on daily data are also highly significant with R2 ranging from 0.25 (Jimma) to 0.42 (Debre Markos). However, none of the models based on monthly data are significant and this could also be partly due to small monthly sample size. Given the complexity in the spatial and seasonal variations of precipitation in Ethiopia, in addition to the potential effects of climatic conditions during precipitation events discussed above, sources of moisture and moisture transport path could also affect the isotopic composition of precipitation.

4.3.2 Effects of moisture source and transport path on 18Oprecip and d-excessprecip values Moisture from multiple sources, including the Indian Ocean, the Mediterranean and Red Seas, Central Africa, and the Gulf of Guinea reaches the western Ethiopian highlands during the rainy season (Seleshi and Demaree, 1995; Segele and Lamb, 2005; Viste and Sorteberg, 2013a, b). Many studies have tried to invoke the relative contribution of these different sources to explain seasonal trends in 18Oprecip and d-excessprecip values in Ethiopia and the anomalously enriched 18Oprecip values at Addis Ababa (Rozanski et al., 1996; Levin et al., 2009; Kebede and Travi,

20

2012; Costa et al., 2014). However, the conclusions from these studies are limited because they rely on isotopic data from monthly rainfall collections in Addis Ababa that do not have the resolution required to attribute isotopic variation in precipitation to specific climatological phenomena.

Table 4 presents possible ways that moisture source and transport path might affect the isotopic composition of precipitation in Ethiopia. To explore some of these processes, we calculated air mass back-trajectories using the HYSPLIT model (see section 2.3) at our four sampling stations for 6 time periods that are representative of the isotopic variation we observed over the 16-month precipitation collection interval (gray boxes, Figure 2; Figures S1 – S4, Table S4).

The HYSPLIT trajectory analyses indicate that in August 2013, when 18Oprecip values are lowest at Gondar, Debre Markos and Jimma, back-trajectories indicate moisture sources with a southerly component (Figure 5). This southerly component can also be seen in the 2012 rainy season (August–September 2012) at Jimma and to some degree at Debre Markos, although northerly and westerly trajectories dominate at Gondar at this time (Figures S1, S2, S4). Outside of the June–September rainy season, trajectories reaching Debre Markos and Gondar predominantly come from the North and East. At Jimma, the trajectories are primarily from the South, except between November 2012 and January 2013, when the back-trajectories indicate easterly transport. At all times of year, trajectories reaching Jijiga are southerly or southeasterly, with a small northerly or northeasterly component except during the March–May rains when the southeasterly component is most clear (Figures S3).

21

Although there is some coherency to these results, the spatial and seasonal patterns of the HYSPLIT trajectory results do not match the patterns that we observe in the isotopic data. As such we cannot make clear links between moisture paths and the variation in 18Oprecip and d-excessprecip values. While moisture source and transport path likely place important controls on the isotopic composition of precipitation in Ethiopia, this level of analysis cannot resolve them. Additional and more comprehensive analyses of moisture sources and fluxes along the transport path during the rainy season would help to resolve the role of moisture source on the 18Oprecip values in Ethiopia.

In summary, our analysis shows that the main controls on the isotopic composition of precipitation in Ethiopia are likely conditions during precipitation events and particularly the presence of regional-scale convective systems that cover the northwestern highlands of Ethiopia. It appears that convective activity might be more important than rainfall amount in controlling the isotopic composition of precipitation during the rainy season. While moisture source and path may also be important, we do not yet have enough information to fully probe these effects.

5. Conclusions

Ethiopian precipitation isotope data collected on daily, weekly and monthly intervals at four stations (Debremarkos, Gondar, Jijiga and Jimma) shows seasonal and spatial variability. The most prominent observations include 1) the three northwest highland stations exhibit similar seasonal variations in 18Oprecip values that is not apparent in Jijiga in the southeastern highland, and 2) there is a nearly synchronous pronounced decrease (~-8‰) in 18Oprecip values in the three

22

northwest highland stations in August 2013 that cannot be explained by the “amount effect”. The daily 18Oprecip data provide the opportunity to investigate local controlling factors (precipitation amount, relative humidity and OLR), which explain up to 50% of variation in 18Oprecip values in Ethiopian precipitation. Using daily OLR data we also demonstrate that convective activity explains seasonal 18Oprecip variations in Ethiopia precipitation. As with the trends in 18Oprecip values, the OLR results from the three northwestern highland stations are in synchronous with each other and suggest regional scale convective systems affect the entire northwestern part of Ethiopia during the rainy season. The moisture trajectory analysis indicates that this strong convective activity and low 18Oprecip values in August 2013 coincide with southerly trajectories at all three northwestern highland stations. This coincidence suggests that moisture source or transport path may also play a role in the regional decrease in 18Oprecip values, but we are reluctant to take these explanations further as a more detailed moisture source and trajectory analysis would be required to fully understand these links fully. Moreover, with 16-month isotope data collection alone, we do not claim to have an all-inclusive explanation of the variability of the isotopic composition of Ethiopian precipitation. If isotopic variation in precipitation is to be used as an effective tool for documenting and monitoring hydroclimate variability in Ethiopia, then we need to generate additional records of this variability and probe the mechanisms that drive it. Comprehensive moisture source analysis will be particularly useful to investigate the impact of moisture source and transport path on variations in 18Oprecip values.

In addition to providing integrated views of modern processes, the data from our study provide useful perspective for paleoclimate studies in the region, as sedimentary archives of 18O and 2H values are commonly used to infer past variations in rainfall amount and changes in

23

moisture dynamics (e.g. Bedaso et al., 2010; Levin et al., 2011; Feakins, 2013; Costa et al., 2014; Tierney et al., 2017). The weak correlation between rainfall amount and 18Oprecip or 2Hprecip values in Ethiopian rainfall today should temper interpretations that equate lower 18O or 2H values from sedimentary archives with wetter intervals. Likewise, we do not observe a strong association between the isotopic composition of precipitation and moisture source. Instead, lower 18O or 2H values from sedimentary archives may indicate times of greater storm intensity or more convection, but not necessarily a change in the amount of moisture delivered to a region.

Author contribution

The study was designed by Z. Bedaso, N. Levin, B. Zaitchik, and D. Shanko and fieldwork was carried out by Z. Bedaso. N. DeLuca, Z. Bedaso and N. Levin drafted the initial version of the manuscript which was later commented, updated and approved by all co-authors.

Acknowledgments

We thank the National Meteorological Agency (NMA) of Ethiopia for facilitating this collection of precipitation for this study. We are particularly grateful to Melesse Lemma and the NMA technicians who conducted the precipitation collections. We thank Enquye Negash for continuous help through the research, and Aynalem Zenebe and Tamiru Alemayehu for their assistance during station set up and logistics of maintaining the precipitation collections. We thank Stephanie Spetka, Edward Kardish, Benjamin Kahn and Gabrielle Stephens for preparation of samples for analysis at Johns Hopkins University. We also would like to thank Dr. Daniel

24

Goldman, University of Dayton for his support. Funding for this research was provided through a Johns Hopkins Morton K. Blaustein Postdoctoral fellowship to ZKB, the Department of Earth and Planetary Sciences at JHU, and the Department of Geography and Environmental Engineering at JHU.

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IAEA. 2009. Laser Spectroscopic Analysis of Liquid Water Samples for Stable Hydrogen and Oxygen Isotopes. Vienna, 1, 1-35. Kebede, S. and Travi, Y., 2012. Origin of the δ18O and δ2H composition of meteoric waters in Ethiopia. Quat. Int. 257, pp.4-12. Kebede, S., Travi, Y., Asrat, A., Alemayehu, T., Ayenew, T. and Tessema, Z., 2008. Groundwater origin and flow along selected transects in Ethiopian rift volcanic aquifers. Hydrogeol. J. 16(1), p.55. Kurita, N., Ichiyanagi, K., Matsumoto, J., Yamanaka, M.D. and Ohata, T., 2009. The relationship between the isotopic content of precipitation and the precipitation amount in tropical regions. J. Geochemical Explor. 102(3), pp.113-122. Lawrimore, J.H., Menne, M.J., Gleason, B.E., Williams, C.N., Wuertz, D.B., Vose, R.S. and Rennie, J., 2011. An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. J. Geophys. Res. Atmos. 116(D19). Lee, J.E. and Fung, I., 2008. “Amount effect” of water isotopes and quantitative analysis of post‐condensation processes. Hydrol. Process. 22(1), pp.1-8. Levin, N.E., Brown, F.H., Behrensmeyer, A.K., Bobe, R. and Cerling, T.E., 2011. Paleosol carbonates from the Omo Group: Isotopic records of local and regional environmental change in East Africa. Palaeogeogr. Palaeoclimatol. Palaeoecol. 307(1-4), pp.75-89.

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Rozanski, K., Araguás‐Araguás, L. and Gonfiantini, R., 1993. Isotopic patterns in modern global precipitation. Climate change in continental isotopic records, pp.1-36. Salati, E., Dall'Olio, A., Matsui, E. and Gat, J.R., 1979. Recycling of water in the Amazon basin: an isotopic study. Water Resour. Res. 15(5), pp.1250-1258. Segele, Z.T. and Lamb, P.J., 2005. Characterization and variability of Kiremt rainy season over Ethiopia. Meteorol. Atmos. Phys. 89(1-4), pp.153-180. Seleshi, Y. and Demaree, G.R., 1995. Rainfall variability in the Ethiopian and Eritrean highlands and its links with the Southern Oscillation Index. J. Biogeogr. pp.945-952. Stewart, M.K., 1975. Stable isotope fractionation due to evaporation and isotopic exchange of falling waterdrops: Applications to atmospheric processes and evaporation of lakes J. Geophys. Res. 80(9), pp.1133-1146. Sultan, M., Sturchio, N., Hassan, F.A., Hamdan, M.A.R., Mahmood, A.M., El Alfy, Z. and Stein, T., 1997. Precipitation source inferred from stable isotopic composition of Pleistocene groundwater and carbonate deposits in the Western desert of Egypt. Quat. Res. 48(1), pp.29-37. Tesfaye, T.W., Dhanya, C.T. and Gosain, A.K., 2017. Evaluation of ERA-Interim, MERRA, NCEP-DOE R2 and CFSR Reanalysis Precipitation Data using Gauge Observation over Ethiopia for a period of 33 years. AIMS Environ. Sci, 4(4), pp.596-620. Tharammal, T., Bala, G. and Noone, D., 2017. Impact of deep convection on the isotopic amount effect in tropical precipitation. J. Geophys. Res. Atmos. 122(3), pp.1505-1523.

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Tierney, J.E. and Pausata, F.S., and deMenocal PB., 2017. Rainfall regimes of the Green Sahara. Sci. Adv. 3(1), p.e1601503. Tierney, J.E., Russell, J.M., Damsté, J.S.S., Huang, Y. and Verschuren, D., 2011. Late Quaternary behavior of the East African monsoon and the importance of the Congo Air Boundary. Quat. Sci. Rev. 30(7-8), pp.798-807. Viste, E. and Sorteberg, A., 2013a. Moisture transport into the Ethiopian highlands. Int. J. Climatol. 33(1), pp.249-263. Viste, E. and Sorteberg, A., 2013b. The effect of moisture transport variability on Ethiopian summer precipitation. Int. J. Climatol. 33(15), pp.3106-3123. Vuille, M., Bradley, R.S., Werner, M., Healy, R. and Keimig, F., 2003. Modeling δ18O in precipitation over the tropical Americas: 1. Interannual variability and climatic controls. J. Geophys. Res. Atmos. 108(D6). Wallace, J.M. and Hobbs, P.V., 2006. Atmospheric science: an introductory survey (Vol. 92). Elsevier. West, A.G., Goldsmith, G.R., Brooks, P.D. and Dawson, T.E., 2010. Discrepancies between isotope ratio infrared spectroscopy and isotope ratio mass spectrometry for the stable isotope analysis of plant and soil waters. Rapid Commun. Mass Spectrom. 24(14), pp.19481954.

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Worden, J., Noone, D., Bowman, K., Beer, R., Eldering, A., Fisher, B., Gunson, M., Goldman, A., Herman, R., Kulawik, S.S. and Lampel, M., 2007. Importance of rain evaporation and continental convection in the tropical water cycle. Nature. 445(7127), pp.528. Wynn, J.G. and Bedaso, Z.K., 2010. Is the Pliocene Ethiopian monsoon extinct? A comment on Aronson et al. (2008). J. Hum. Evol. 59(1), pp.133-138. Figures Figure 1. (A) Location map of precipitation collection stations in Ethiopia used in this study. The location of the long-term GNIP isotope monitoring station at Addis Ababa is also noted. (B-F) Long-term mean monthly temperature (dotted line) and mean monthly precipitation amounts (histogram) with error bars represent the 1σ on the mean for each station in this study and Addis Ababa. Source for precipitation data: NOAA Climate Data Center (CDC) Global Historical Climatology Network-Monthly (GHCN-M) version 3 dataset (Lawrimore et al., 2011), retrieved from the KNMI Climate Explorer (http://climexp.knmi.nl). Diamonds represent the average monthly precipitation amounts recorded at each of the collection stations during the study interval. Figure 2. δ18Oprecip, δ2Hprecip, d-excessprecip data collected at a daily (crosses), weekly (circles) and monthly (squares) interval at each station (A-D) are shown in the top three subplots for samples collected between August 2012 and November 2013. Daily temperature, daily OLR and daily precipitation amount data are shown in the lower three subplots. Time intervals used to evaluate moisture source Aug-Sep 2012, Nov. 2012-Jan 2013, Feb.-Apr 2013, May-Jun. 2013, Aug. 2013 and Sep. 2013 are shown as gray boxes.

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Figure 3. Local meteoric water lines (LMWLs) from the four sample collection stations (A-D) for the three sampling intervals (daily, weekly and monthly) are shown (a-l). The red dotted line represents the GMWL (δ2H = 8δ18O+10; Craig, 1961). The amount weighted LMWLs are shown in blue and equations, and number of samples are shown on the top left corner of each plot. Figure 4. Correlations between daily δ18Oprecip values and precipitation amount, OLR, temperature and relative humidity for each station (a-p). Linear regression lines, correlation coefficients (r2) and p values are also shown on top of each plot. Figure 5. Mean moisture back-trajectory paths from six intervals Aug-Sep 2012, Nov. 2012-Jan 2013, Feb.-Apr 2013, May-Jun. 2013, Aug. 2013 and Sep. 2013. The back-trajectories were calculated for 240 hours starting at noon from all of sampling stations (A-D). Supplementary Information Table S1. Isotopic data collected at four stations at daily, weekly and monthly intervals between August 2012 and November 2013. Table S2. Summary of regressions comparing data collected at different frequencies (daily, weekly and monthly). Table S3. Summary weighted means from data collected over the entire period (August 2012November 2013) at different frequencies (daily, weekly and monthly). Table S4. Subset of dates within six time intervals used for HYSPLIT model runs, including respective isotopic data.

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Figure S1-S4 Air mass trajectories from the four sampling stations for 240 hours from six time intervals Aug-Sep 2012, Nov. 2012-Jan 2013, Feb.-Apr 2013, May-Jun. 2013, Aug. 2013 and Sep. 2013. Dates used for these trajectories and daily 18Oprecip and d-excessprecip values are listed in Table S4.

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Table 1. Summary of 18Oprecip and 2Hprecip and d-excesprecip values from daily precipitation collections from the four sampling stations for this study and the monthly isotope data from Addis Ababa. The Addis Ababa data are from the long-term monthly precipitations collected by Global Networks of Isotopes in precipitation (GNIP) and reported by Rozanski et al. (1996). Station

Date range

n mean

Debre Markos. 10.33˚N, 37.74˚E 2446 m Gondar 12.52˚N, 37.43˚E 2133 m Jijiga 9.35˚N, 42.79˚E 1609 m Jimma 7.84˚N, 36.17˚E 1780 m Addis Ababa† 9.00˚N, 38.73˚E. 2360 m

δ18OVSMOW (‰) w. mean max

min

mean

δ2HVSMOW (‰) w. mean max

min

mean

d-excessVSMOW (‰) w. mean max

min

Aug 11, 2012 – Sept 26, 2013

197

-0.8 ± 3.8

-2.1 ± 3.3

10.3

-10.9

9.3 ± 29.0

1.3 ± 26.6

80.8

-79.8

16.0 ± 6.4

18.2 ± 4.6

29.9

-9.9

Aug 7, 2012 Oct 8, 2013

164

-0.8 ± 3.1

-1.9 ± 2.8

8.4

-10.4

6.9 ± 23.6

-0.0 ± 22.3

64.8

-69.8

13.6 ± 5.5

14.9 ± 4.5

27.6

-9.6

Aug 20, 2012 Nov 15, 2013

103

-0.1 ± 3.2

-1.5 ± 2.9

6.4

-12.6

7.6 ± 23.3

-0.8 ± 22.5

47.1

-91.8

8.5 ± 5.2

11.1 ± 4.2

21.5

-7.8

Aug 4, 2012 Sept 28, 2013

219

-0.4 ± 2.6

-1.2 ± 2.6

7.5

-10.0

11.2 ±19.9

6.4 ± 20.4

66.2

-72.0

14.1 ± 4.4

15.9 ±3.3

22.1

-6.7

Jan 01, 1961Dec 31, 2009

284

-0.3 ± 2.6

-1.3 ± 2.3

8.4

-8.0

9.8 ± 19.1

3.6 ± 17.7

49.6

-55.1

12.2 ±5.8

13.8 ±4.2

22.7

-17.5

n is number of samples collected and analyses at the respective stations δ18O, δ2H and d-excess values are reported in ‰ units, relative to VSMOW. †The

Addis Ababa data are from the-long term monthly precipitations (1961-2009) collection by the IAEA (IAEA, 2013). Years which have data for all δ18O, δ2H, and amount are included in the calculations. Summary statistics for data reported here are calculated from data tabulated in Table S1. Weighted means are weighted by precipitation amount listed in Table S1. Errors reported in this table are standard deviation (1σ).

35

Table 2. Correlation analysis of daily, weekly and monthly 18Oprecip values and climate factors at the four sampling stations. Location

Climate factors

Daily δ18OVSMOW (‰) n

Debre Markos

r

p

Weekly δ18OVSMOW (‰) n

r

p

Precipitation amount 197 -0.33 <0.05 40 -0.38 0.04 Temperature 197 0.35 <0.05 40 0.26 .10 Relative Humidity 197 -0.55 <0.05 40 -0.69 <0.05 OLR 197 0.49 <0.05 40 0.62 <0.05 Gondar Precipitation amount 164 -0.38 <0.05 38 -0.53 0.26 Temperature 164 0.16 <0.05 38 0.18 0.28 Relative Humidity 164 -0.42 <0.05 38 -0.67 <0.05 OLR 164 0.42 <0.05 38 0.57 <0.05 Jijiga Precipitation amount 102 -0.40 <0.05 32 -0.25 <0.05 Temperature 102 0.10 0.29 32 0.19 0.30 Relative Humidity 102 -0.40 <0.05 32 -0.06 0.76 OLR 102 0.41 <0.05 32 -0.05 0.77 Jimma Precipitation amount 212 -0.32 <0.05 47 -0.45 0.94 Temperature 212 0.09 0.23 47 -0.06 0.67 Relative Humidity 212 -0.37 <0.05 47 -0.49 <0.05 OLR 212 0.31 <0.05 47 0.40 <0.05 n is number of samples from respective sampling intervals, r is correlation coefficient and p is significance level of 0.05. OLR is Outgoing Longwave Radiation

Monthly δ18OVSMOW (‰) n 13 13 13 13 11 11 11 11 13 13 13 13 15 15 15 15

r -0.56 0.42 -0.78 0.63 -0.75 0.36 -0.79 0.40 -0.01 0.01 -0.06 -0.05 -0.28 0.33 -0.33 0.03

p 0.42 0.15 <0.05 <0.05 0.42 0.28 <0.05 0.22 0.49 0.75 0.84 0.87 0.15 0.22 0.23 0.91

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Table 3. Local Meteoric Water Lines (LMWL) for sites in Africa: slopes and intercepts for the linear regression lines are generated from daily 18Oprecip and 2Hprecip values. Station Debre Markos Gondar Jijiga Jimma All stations

n 197 164 103 219 683

R2 0.96 0.95 0.96 0.96 0.95

slope 8.0 ± 0.1 7.8 ± 0.1 7.6 ± 0.1 7.9 ± 0.1 7.8 ± 0.1

intercept 18.3 ± 0.4 14.5 ± 0.4 10.5 ± 0.4 15.7 ± 0.3 15.5 ± 0.2

Stations in the IAEA/WMO network in eastern Africa and Indian Ocean sitesa Addis Ababa -0.92 7.0 ± 0.2 11.5 ± 0.6 Entebbe -0.90 7.4 ± 0.2 10.8 ± 0.8 Kericho -0.91 8.0 ± 0.4 11.4 ± 1.0 Dar es Salaam -0.85 7.0 ± 0.3 6.8 ± 0.7 Ndola -0.97 7.7 ± 0.2 9.3 ± 1.1 Harare -0.93 7.0 ± 0.8 9.2 ± 0.9 Antananarivo -0.97 8.1 ± 0.2 14.0 ± 1.2 Diego Garcia -0.88 7.0 ± 0.2 4.7 ± 0.8

collection frequency daily daily daily daily daily

monthly monthly monthly monthly monthly monthly monthly monthly

Other stations in eastern and central Africa Mt. Killimanjarob

748

0.92

7.4± 0.1

13.1± 0.3

weekly

Virunga DRCC 223 0.97 7.6 ± 0.1 16.2 ± 0.3 n is number of samples Errors reported in this table for the new data are standard errors.

monthly

aRegressions

for stations in eastern Africa are from Rozanski et al. (1996)

bRegressions

for other stations in eastern Africa are from Otte et al. (2017)

CRegression

for other stations in central Africa are from Balagizi et al. (2018)

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Table 4. Links between precipitation origin, transport or mode and 18Oprecip and d-excessprecip values Process, Implications for 18Oprecip and d- Support for explaining observed precipitation origin or 18Oprecip and d-excessprecip values in excessprecip values mode Ethiopia 18 (1) Distillation of air parcel Lower  Oprecip values result when Could explain low 18Oprecip values in with moisture loss as it moisture has traveled longer August 2013 in northwestern highlands travels to Ethiopia distances, known as the “continent if moisture loss along transport path. effect”; d-excessprecip values are not affected. (2) Contribution of Results in moisture similar to Process may be happening but cannot transpired moisture from average 18Oprecip values from fully explain the low (< -8‰) 18Oprecip Central Africa to air parcels Central Africa, ca. -2.3‰ (see values in northwest highlands August Balagizi et al., 2018); d-excessprecip 2013. values are unchanged and should be equivalent to d-excessprecip values in place where transpiration occurs (ca +13.7‰, see Balagizi et al., 2018). (3) Contribution of Results in 18O values of moisture Could explain low 18Oprecip values in evaporated, terrestrial (vapor) lower than the initial water northwest highlands in August 2013 but moisture from Central that is evaporated; d-excess values temporal patterns in d-excessprecip values Africa to air parcels of moisture will be higher than do not strongly support this. initial waters. (4) Distillation of moisture Results in incrementally lower Could explain low 18Oprecip values in 18 once it is released as rainfall  Oprecip values as a parcel August 2013 in northwestern highlands, in Ethiopian highlands progressively releases moisture; if the potential for distillation were should not result in significant greater at this time. changes in d-excessprecip values. (5) Convective storm Convective activity has been OLR data suggest greater convective activity activity peaked in August 2013 and associated with lower 18Oprecip likely explain lower 18Oprecip values values (e.g. Risi et al., 2008; Kurita et al., 2009; Martin et al., during this time. 2018) (6) Sub-cloud evaporation of Dry and warm environment Likely explanation for higher 18Oprecip 18 rain droplets increase  Oprecip values, and lower d-excessprecip values from decreased d-excessprecip values of Jijiga compared to the northwest falling raindrops. highland stations and also during low relative humidity in February-April at Gondar and Debre Markos and in January-March at Jimma.

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Table 5. Multiple linear regression model result for the daily, weekly and monthly 18Oprecip at each of the four sampling stations, using multiple climate factors (temperature, precipitation amount, relative humidity, OLR). n Stations R2-daily p-daily R2-weekly p-weekly R2-monthly p-monthly Debra 197 0.42 0.00 0.61 0.00 0.60 0.02 Markos 164 0.33 0.00 0.44 0.00 0.72 0.02 Gondar 103 0.40 0.00 -0.01 0.46 -0.42 0.97 Jijiga 219 0.25 0.00 0.37 0.00 0.28 0.12 Jimma 2 2 R indicates adjusted R of a multiple linear regression

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