Next Article in Journal
Combining Hydrologic Analysis and Life Cycle Assessment Approaches to Evaluate Sustainability of Water Infrastructure: Uncertainty Analysis
Previous Article in Journal
Comparative Study on Violent Sloshing with Water Jet Flows by Using the ISPH Method
Previous Article in Special Issue
Validation of Cryogenic Vacuum Extraction of Pore Water from Volcanic Soils for Isotopic Analysis
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Use of 222Rn and δ18O-δ2H Isotopes in Detecting the Origin of Water and in Quantifying Groundwater Inflow Rates in an Alarmingly Growing Lake, Ethiopia

1
Center for Water Resources Research, School of Agricultural Earth and Environmental Sciences, University of KwaZulu Natal, Pietermaritzburg 3201, South Africa
2
School of Earth Sciences, Addis Ababa University, POBOX 1176 Addis Ababa, Ethiopia
*
Author to whom correspondence should be addressed.
Water 2019, 11(12), 2591; https://doi.org/10.3390/w11122591
Submission received: 16 September 2019 / Revised: 25 November 2019 / Accepted: 26 November 2019 / Published: 9 December 2019

Abstract

:
Dual Radon (222Rn) and δ18O-δ2H isotopes were utilized to (a) detect the origin of water, (b) pinpoint groundwater inflow zones and (c) determine rates of groundwater inflows in an expanding lake in central Ethiopia. The lake area expanded from 2 km2 to 50 km2 over the last 60 years, causing serious engineering and socio-economic challenge (inundation of urban utilities, irrigation farms, railways and roads; ecological changes in the lake; and threatening water salinization for water users downstream). Commensurate with the changes in volume, there was a change in salinity of the lake from a hypersaline (TDS 50 g/L) to a near freshwater (3 g/L) condition. 222Rn is powerful in pinpointing sites of groundwater inflows and determining groundwater inflow rates in lake systems with non-hydrologic steady-state conditions. The 222Rn method is complemented by the use of the stable isotopes of water (δ18O-δ2H pair). The δ18O-δ2H isotopes were used to discriminate the source of the water responsible for the expansion of the lake. The results show that the main source of water responsible for the expansion of the lake is the irrigation of excess water joining the lake through subsurface flow paths. The fast and voluminous flow is aided by a dense network of faults and by seismically induced modern ground-cracks that enhance the transmissivity of the aquifers to as high as 15,000 m2/day. The 222Rn mass balance shows the groundwater inflow rate is estimated at 4.6 m3/s. This is comparable with the 4.9 m3/s annual seepage loss from three large farms in the area. This work adds to the meager literature in the use of 222Rn in lake-groundwater interaction studies by demonstrating the capability of the method in addressing a practical engineering and socio-economic challenges.

1. Introduction

1.1. Background

Lake Beseka, a tectonic lake, located in central Ethiopia, is an exceptional lake where substantial increase in volume, area and depth, and decrease in salinity has been observed over the last 60 years [1,2]. The region is characterized by active tectonics related deformation [3,4], convergence of regional groundwater flow [5,6] and presence of intensive mechanized irrigation. Several hypotheses have been forwarded to explain the expansion of the lake including (a) seismically induced deformation of the basin which causes increased discharge of thermal springs feeding the lake [7], (b) change in regional water balance leading to increased activity of groundwater discharge [6,8,9], (c) Irrigation return water from the adjacent farms joining the lake via sub surface paths [2,10] and (d) occasional accidental and unreported direct spill of excess irrigation water from farms through surface drains as a result of faulty drains arising from breakage and siltation related clogging [10].
Most of these hypotheses do not clearly identify the source of the water versus the mechanism that has led to increase in discharge of the water. For example, [7], using remote sensing method-based mapping of temperature of the lake, concluded the increase in discharge of thermal springs feeding the lake to be the principal cause of the swelling of the lake. However, the same authors indicated the source of the water that has led to the increment in thermal water discharge is unknown and is not related to the irrigation water.
The presence of competing possibilities makes it challenging to identify the direct and indirect causes of the lake level changes and to mitigate the impact of the growing lake [10]. The aims of this work are to investigate the source of water responsible for the lake level rise using δ18O-δ2H and 222Rn tracers and to estimate groundwater inflow to the lake.
The δ18O and δ2H are proven tools in tracing movement of water around lakes [11,12,13,14,15]. 222Rn isotope on the other hand is a proven tool in detecting specific site of groundwater discharge to open water bodies (lakes and rivers) and in quantifying the groundwater inflow to lakes without the need to reverting to detailed hydrological measurements [16,17] of groundwater flow into lakes. 222Rn is a radioactive daughter of radium–226, which in turn is a radionuclide in the U-Pb decay series.

1.2. Damages Caused by Lake Beseka Water Level Rise and Chemistry Changes

The economic damage caused by the expansion of Lake Beseka is enormous. The damage caused prior to 2008 is estimated in monetary value at 2.5 million USD [7]. Additional damage has been caused by the lake, which continued to expand since 2008 until it spilled into the nearby Awash River in 2010. The recent damages include (a) submergence under lake water of 8 km long drinking water distribution pipes and 5 km service pipes of the Metahara town water supply (20,000 people); (b) submergence underwater of urban utilities (clinics, schools and offices), residential areas and recreational facility (c) submergence of 900 ha of sugarcane farm at Metahara plantation under saline water (Figure 1); (d) damage and submergence underwater of the Ethiopia-Djibouti railway and main express road connecting Addis Ababa to Djibouti port; (e) spillage of brackish water from the lake and in to an adjacent Awash River (Figure 2 and Figure 3), degrading the quality of the river water for irrigation as well as drinking water use for millions of inhabitants downstream; (f) alteration/changes in ecology of the lake [1,18] and subsequent change in the faunal and floral composition of the lake; and (g) disruption of the movement path of wildlife in an adjacent national park.

1.3. Site Characterization

1.3.1. Geology and Hydrogeology

The diversity of hypotheses made about the growth of Lake Beseka stems from the complexity of the geophysical and ‘man-made’ features of the region. Interplay of processes that can lead to change in hydrology and geometry of the lake coexists in the region. Firstly, the lake fills a tectonic depression in a seismically active region in East Africa. At around 1 mm/yr. extension, the fastest rift opening rate in the Ethiopian rift valley is centered on Lake Beseka area [4] leading to long-term and abrupt changes in lake geometry and geothermal activities. Reports show swarms of ground fissures (swarm of faults) have formed at different times over the last 100 years [3] following earthquake events. The recent swarms of fractures, six in number, are localized to the region north of the Lake over a 4 by 7 km belt [4]. The fractures generally appear as narrow cracks which may be several hundred meters long (Figure 1). Beyond this belt, they either disappear or grade into normal faults. Such cracks are believed to increase permeability structure of the rocks and thereby lead to increased discharge of springs feeding the lake [9].
Secondly, the area is underlain by volcanic rocks such as ignimbrites, volcanic ash, basalts and alluvio-lacustrine sediments. The basalts cover most of the western sector of the area. The basalts are highly vesicular with dense network of rift related fractures and joints leading to high transmissivity in the range of 7000 to 15,000 m2/day and hydraulic conductivity of 10 to 800 m/day [10]. The ignimbrites are densely dotted by blister caves, unique landforms that form when ashes fall on wet ground and subsequent vaporization of the moisture by heat inflates the overlying ash layer. This unique landform increases the permeability of the ignimbrites and ashes, which would otherwise have low hydraulic conductivity among the volcanic rocks in the region.

1.3.2. Hydrology, Chemistry and Irrigation Development

Lake Beseka was a small closed lake of 2.6 km2 in the early 1960s before it expanded to its present size of 54 km2 in area and (in 2014) 243,600,000 m3 in volume. In the early 1960s, the lake was fed by saline springs discharging at the western lake shore [18,19]. These springs are now submerged underwater and discharge at more than 500 l/s. Commensurate with growth in size, electrical conductivity decreased tenfold from nearly 75,000 μScm−1 in 1960s [19,20] to 3600 μScm−1 in 2014. However, this decrease in EC was not regular. It markedly decreased in 1970s to 10,500 μScm−1 over a period of 10 years and then decreased to around 6000 μScm−1 over the next 40 years to 2009 (Belay, 2010). According to [8], the lake has an EC of 7100 μScm−1 in 1998. Current EC values of the lake vary spatially—at 2600 μScm−1, it is the lowest in the South West bay area and at the 6400 μScm−1 it is the highest in North West area. Detailed measurement by [7] shows that the temperature of the lake varies spatially. Maximum depth of the lake is recorded at 12 m while mean depth and mean volumes are 5 m and 243,600,000 m3, respectively.
Four large scale irrigation farms run since 1965, with a total irrigation area exceeding 20,000 ha. Net irrigation efficiency from the farms has been estimated at 40% [21] and return flow reaches 20% of the total intake. The detailed characteristics of the irrigation schemes are given in Table 1. Figure 1 shows the location of the farms. The Fentale irrigation scheme became operational in 2008 and the other three started in 1960s. The Fentale irrigation area is located in several small patches East of Fentale canal. Studies [10] MWIE (2014) shows that the main irrigation water loss takes place from the conveyance (primary) and distribution (secondary) canals and field level loss account for only 10% of total loss. Except the main Metahara farm, which is located at relatively lower ground (south of Awash River) and in a different catchment, three of the farms are located partially or completely within the surface and groundwater catchment of the lake (Figure 1). Compared to all the four irrigation sites, the lake is located at lowest elevation, forming a closed depression. A net total loss via seepage from the three schemes upstream of the lake is estimated at 4.89 m3/s [10]. In the year 2007, the conveyance loss from the Fentle main canal alone was estimated at 2.81 m3/s.
The lake water balance and volume changes are accounted for by (a) a quasi-stable direct rainfall on the lake (0.6 m/yr.), (b) direct overland flow from wadi beds amounting to another 0.6 m/yr. [6], (c) an evaporation depth of 2 m/yr., (d) unknown amount of surface water outflow and (e) variable groundwater inflow and unknown groundwater seepage out of the lake.

2. Methodology: Theoretical Description

2.1. 222Rn in Lake-Groundwater Interaction Studies

222Rn is a proven isotope in detecting surface water groundwater interaction and in quantifying groundwater flows to lakes and seas [16,17,22,23,24]. Qualitatively, 222Rn can be used to pinpoint specific sites of groundwater discharge to lakes [16,22]. It is a good tracer because of its substantial quantity in groundwaters. Surface waters are devoid of 222Rn. In surface water bodies, high 222Rn concentrations are usually detected around the site of groundwater discharge allowing pinpointing groundwater advective inflow and seepage zones.
Quantitative estimation of groundwater inflow to lakes is based the assumption that 222Rn sources balances for 222Rn sinks under steady state conditions [16,22]. Under steady state conditions, water and 222Rn balance equations for a well-mixed lake can, respectively, be written as:
G + S + PA L =   Q + EA L
C G G + C S S + FA b =   QC L + kAC L + λ VC L
Table 2 gives a detail description of what the individual symbols means in the equations. The first equation means that, under a hydrologic steady state, the sum of all inflowing waters (groundwater, surface water and precipitation on the lake) equals the outflowing components (aggregate surface and groundwater outflow and evaporation from the lake). Similarly, the 222Rn balance equation (Equation (2)) is obtained by balancing the total 222Rn entering and total 222Rn leaving the lake. The 222Rn balance in Equation (2) is a little bit complex. This is because there is additional source and sink of 222Rn other than the advective flux coming and leaving the lake along the water flow components in Equation (1). That is (a) additional 222Rn flux (F) joins the source terms from diffusion from lake bottom sediments covering an area (Ab), (b) additional loss of 222Rn takes place from the lake, CL, through radioactivity decay at rate of λ = 0.18 day−1, over the lake volume (V) and (c) additional sink of 222Rn takes place through exchange with overlaying atmosphere at velocity k = 0.16 m/day from the surface area of the lake. Thus, Equation 2 balances all the diffusive and advective sources with combined advective, radioactive decay and gas exchange sinks of 222Rn. 222Rn coming from production within lake by decay of dissolved or suspended parent isotope (226Ra) is assumed to be negligible as measured and found so in many previous studies [17] because 226Ra bounds to sediments than to water. 222Rn concentration in atmosphere is negligible; thus, the 222Rn flux to the lake along with rainwater is not included in Equation (2).
Combining the two equations (Equations (1) and (2)), an equation for computing the groundwater inflow rate (G) without reverting to the need of data on aggregated ground and surface water outflow can be derived (Equation (3)). Details of the derivation of the equation and the explanations can be found in [16,21] and [22].
G =   ( S + PA L + kA L + λ V ) C L   FA b SC S C G C L
The advantage of radon is that it reaches steady state condition over the matters of few days [16] because of its apparent short half-life (3.82 days) and low residence time. This justifies the assumption of radon steady-state condition [16,22,23] in Equation (2). Since nutrient loading and chemistry of groundwater depends on the depth of water circulation, identifying the source of the groundwater inflow to lakes may be essential. In this particular study separating the shallow groundwater from the deep groundwater inflows to the lake is essential in order to understand the source of the groundwater (deep circulating geothermally heated groundwaters versus shallow irrigation return water) responsible for swelling of the lake. To help separate the groundwater source to the lake, independent δ18O-δ2H isotope tracers were used.

2.2. δ18O-δ2H in Lake-Groundwater Interaction Studies

The isotopes of water molecule (δ18O, δ2H) are proven tools in lake–groundwater interaction studies. These isotopes have been used in detecting direction of groundwater flow around lakes [13]. This is because pristine groundwaters unaffected by lake water mixing shows the isotope signal of local or regional groundwaters while when δ18O-δ2H enriched lake water mixes with it, the groundwater isotopic signal changes towards positive value proportional to mixing ratio. This helps to identify groundwaters affected by lake water inflow. The δ18O-δ2H isotopes have also been widely used in quantitative determination of evaporation rate and groundwater flow around lakes. Extensive literature exists on the use of δ18O and δ2H in quantitative determination of lake water balance. Because of the likely absence of isotopic steady state condition of Lake Beseka and lack of time series of δ18O and δ2H of the lake, the method has not been used in quantitative determination of the water balance of the lake. These isotopes are also used in determining the composition of the initial water that feeds the lakes. This indirectly helps in identifying the type of water that feeds the lakes. It has been proven in many studies that the δ18O, δ2H composition corresponding to intersection point of the local evaporation line (LEL) and the local meteoric water line (LMWL) in a δ18O-δ2H plot represents the average composition of the waters feeding lakes under consideration [11,14]. This allows the tracing back of the source of groundwater as shallow or deep, regional or local and geothermal or cold water if these sources are marked by different compositions.

3. Data requirements, Sampling and Analysis

3.1. 222Rn and δ18O-δ2H in Waters

222Rn survey has been conducted using portable DURRIGE-RAD7 electronic 222Rn counter fitted with RAD aqua module. A total of 16 222Rn inventory points were taken. The measurement sites within the lake were chosen, taking key questions into consideration, including: (a) what is the role of the seismically induced ground fissures and blister caves in the northern sector of the lake in channeling groundwater to the lake, (b) what is the role of the irrigation return water from irrigation farms located in southern, western and south western sector of the lake and (c) is there upwelling of groundwater inflow throughout the bottom of the lake. Considering these, most of the measurements were conducted in all areas except in the south eastern and south-central part of the lake. Groundwater 222Rn content was measured in one borehole and one spring. The measurements were conducted from 21 March, 2014 to 30 March, 2014. The counting is based on the principle of liquid–gas-membrane extraction [25] extraction module, which consists of hollow vinyl fibers, allows 222Rn stripping from the water of interest into a connected closed air-loop. 222Rn counting was conducted over a period of 4 to 5 h depending on the stabilization of the reading. Subsequent to the process water temperature was measured using HoBo temperature sensor. The temperature record along with the 222Rn record is entered into CAPTURE software provided by the supplier of the DURRIGE equipment to obtain the final 222Rn result accounted for water temperature variation. The overall standard deviation varies between 15 and 25%, higher for low 222Rn contents. The first read is discarded and the average of the last four reading was taken as the mean 222Rn composition of the specific water point. Accompanying 222Rn measurement survey of electrical conductivity was also conducted. The measured data and the measurement sites are shown in Table 3 and Figure 2. The assumption is that the measured 222Rn content in the well represents the mean composition of 222Rn in inflowing groundwater. This assumption is not strictly valid in cases where diffusive flux dominates the groundwater inflow to lakes [22]. In the study, because of the fact that discreet fracture flow is the main mode of groundwater inflow to the lake, the assumption that 222Rn in groundwater well equals the 222Rn concentration in the inflowing water gives sense. In order to show the uncertainties associated with this, a sensitivity run was conducted. The mean of the measured lake 222Rn content was used as input into the Equation (3). Since all surface water inflows are seasonal and occur from dry wadi beds with no contact with groundwater the 222Rn concentration in surface runoff to the lake is considered negligible. Table 4 enumerates the list of variables, parameters and associated values utilized in this work and in 222Rn mass balance calculations.
Samples for δ18O and δ2H were taken intermittently between 1997 and 2014 from the lake, the Awash River and from adjacent groundwaters. The samples were acquired under the IAEA-TC projects entitled ETH8003, ETH8005, ETH8007 (www.iaea.org). During this time, a total of 60 samples were collected and measured for δ18O-δ2H content in the laboratory of Krakow and the IAEA Isotope Hydrology Laboratory Vienna Austria (ETH8/003, ETH8005/ ETH80060 and in the Laboratory of Nuclear physics University of Krakow, Poland (ETH8/007). All the δ18O-δ2H results are reported in per-mil (‰) notation calibrated against V-SMOW. The groundwater samples were collected from two distinct zones. The first zone represents groundwater in the catchment of the Lake Beseka area and particularly from piezometers and water supply wells located inside the irrigation schemes as wells as in upstream and downstream zone of the anticipated groundwater flow direction to and from the lake. The second group of groundwater samples represent deep groundwaters (>100 meters) collected from drilled deep wells along an east-west transect stretching from lake Koka to Lake Beseka (Figure 2).

3.2. 222Rn in Diffusive Flux

Diffusive 222Rn flux (F) in Equation (3) is 222Rn joining the lake from the lake bed sediments. This is the function of the bulk density of the sediments, 222Rn diffusion coefficient in sediments and 222Rn production rates from 226Ra in the sediments. Ideally the diffusive 222Rn flux is determined from consideration of bulk density of sediments, bulk density of the grains, the 222Rn diffusion coefficient and the 222Rn production rate from 226Ra in water using diffusion equation [16] or using empirical relations [26] (Burnett et al., 2003). In cases where groundwater advection is significant, which is true for the lake under investigation, inputs via diffusion can usually be ignored [17,23]. The near absence of lake bed sediments [7] underneath the lake and the rocky bottom of Lake Beseka could justify this. An alternative approach is to solve for F in Equation (2) by assuming the lowest 222Rn content in the lake corresponds to region of only diffusive 222Rn flux [16,22]. This means Equation (2) reduces to F A b = k A C L + λ V C L for that part of the lake. Plugging the lowest lake 222Rn concentration in the equation, the diffusive flux (F), can be solved for. In this specific work we followed this approach to compute F and to later show the sensitivity of the estimated groundwater inflow to the variation in the F.

3.3. Lake Geometry (Bathymetry, Mean Depth, Area)

Bathymetric survey of the lake has been conducted in a number of occasions [7,10]. The geometric data (mean depth, lake volume, lake bottom area and lake surface area) required in the 222Rn balance equation is retrieved from [10]. It is assumed that the surface of the lake bottom sediments over which diffusive flux of 222Rn is taking place is equal to the lake surface area. The size of the island in the middle of the lake is accounted for in the lake area computation. Because the lake is spilling water since 2010 through an artificial canal in the eastern sector of the lake, the area of the lake is pretty much stable since that time.

3.4. Timing of 222Rn and δ18O-δ2H Data Collection

The primary approach of this work is complementing results obtained from 222Rn with the results obtained from δ18O-δ2H regarding the magnitude and the sources of water entering the lake. However, it should be noted that neither the location nor the timing of the samples for each measurement are the same. 222Rn data has been collected over a period of 9 days in March 2014, while the stable isotope data has been collected over a period of 17 years (1997–2014). Since the two isotope sets were used for different but complementary purposes, the difference in timing of the data collection should not be a problem. The δ18O-δ2H dataset were used to detect the composition of the initial water that feeds the lake. This is based on the theory that the intersection between the Local Evaporation Line (LEL) and the local meteoric water line (LMWL) represents the composition of the initial water that feeds open water bodies. The slope of the local evaporation line depends on the humidity and the isotopic composition of the ambient vapor. These two parameters, humidity and the δ18O-δ2H composition of the ambient air, can reasonably be assumed have remained unchanged since 1997. This means the difference in time of the data collection will not erase the complementarity of the two tracers.

4. Results and Discussion

4.1. Causes of Lake Level Rise: 222Rn Evidence

The first management question about the lake is the reason behind the alarming expansion of the lake. Since there is no change in the trends in rainfall and therefore runoff in the catchment, the suspect is thus an increase in groundwater discharge. We followed the approaches proposed elsewhere in relating the 222Rn pattern with pinpointing groundwater discharge zones. Pattern in the 222Rn has been used for example to interpret the role of the lake bed geology in governing 222Rn pattern [22] or in localizing sites of groundwater discharge [16,24]. A closer inspection of the 222Rn pattern presented in Figure 2 shows anomalous 222Rn content along the shoreline in the South (S), South West (SW) and Western (W) part of the lake. The shoreline in the north and the east as well as the central part of the lake shows no such anomaly. The 222Rn pattern indicates groundwater inflow is restricted to the SW, S and W part of the lake. The low 222Rn content in the northern portion of the lake precludes presence of substantial groundwater discharge emerging from the volcano in the North. An increase in discharge of groundwater coming from the northern sector of the lake catchment was one of the primary suspects [10]. This is because, over the last six decades, a number of new seismically induced ground-fissure swarms have occurred. This process was suspected to enhanced the hydraulic conductivity of the aquifers (Figure 1 and Figure 3) and channeling the groundwater stored in the abundant buried and exposed blister caves in that region. The very low 222Rn concentration in the northern part of the lake means the ground fissures and the caves are not contributing significant volume of groundwater to the lake in that sector. The lack of high 222Rn content in the center of the lake also indicates the absence of substantial advective groundwater inflow from the lake bed precluding the fact that the entire groundwater regime in surrounding the lake has been affected. The pattern in 222Rn variation mirrors the pattern observed in surface temperature variation as reported by [7]. High lake surface temperature recorded by the authors corresponds to the sites where we recorded high 222Rn content in the lake water. The authors used the surface temperature anomaly of the lake water to pinpoint sites of groundwater inflow, by assuming that the high temperature areas are where anomalous groundwater inflow is happening. There is a very good match between temperature anomaly data and the 222Rn values at the three sites with the highest 222Rn content (2340 Bq/m3, 2060 Bq/m3, 798 Bq/m3). Typical values of surface water affected by groundwater would range in 222Rn content in the order of tens to few hundreds of Bq/m3. As can be observed, the 222Rn content of the lake near the island a km from the shore shows a value of 53 Bq/m3 confirming the rapid loss of the 222Rn in the lake water over short distance from the shore. The anomalously high 222Rn and temperature values at the three sites correspond to sites of the submerged springs. Both 222Rn and temperature methods thus confirm increase in discharge of these springs are the primary cause of the lake volume changes. A visual observation of the groundwater inflow sites in the SW shore of the during the sampling shows spring waters forcefully pushing the overlying lake water column over an area of nearly 500 m2 [7] proposed the increase in discharge of these springs is caused by increase in geothermal activity triggered by the recent tectonic activities. [7] precludes the role of the irrigation return water from the main possible causes on the assumption that irrigation return water should have lowered the lake temperate in that zone. This is because irrigation uses cold river water from nearby Awash River. The temperature-based evidence did not confirm the actual source of the water itself. The 222Rn method is insufficient to confirm sources of groundwater. [16] for example proposed use of other tracers if such information is sought in 222Rn based lake groundwater interaction studies.

4.2. Cause of Lake Level Rise: δ18O-δ2H Evidence

The stable isotope of water (δ18O-δ2H) can provide complementary evidence to the 222Rn and provide evidence on the actual sources of groundwater that is feeding the lake. It is long established that isotopes of water are good tracers of origin of groundwater in lakes. Early on, [11] suggested that the δ18O and δ2H composition corresponding to the intersection point between the Local Evaporation Line (line that best fits the δ18O versus δ2H plot of evaporated water bodies) and the Local Meteoric Water Line represents the net composition of initial water that feeds the evaporating water body. The same approach has been used to trace the δ18O-δ2H composition of the groundwaters feeding the various Boreal lakes in Canada [14], lakes in Ethiopia [15] and lakes elsewhere [12]. In most studies, the net δ18O-δ2H composition of the waters feeding the lakes is sought for water balance study purposes. This is because the δ18O-δ2H composition of inflowing water is an input parameter in the lake isotope balance models [11,12,27,28]. However, in the current study, the information is sought to first identify the δ18O-δ2H composition of the inflowing water and then seek further what kind of groundwater this water corresponds to—such as shallow or deep, geothermal or cold, local or regional. This requires the condition that the different water compartments are imprinted by distinctive δ18O-δ2H compositions.
The δ18O-δ2H pattern of groundwaters from the region shows there are two types of groundwaters (Figure 3 and Figure 4). These are (a) δ18O-δ2H depleted, regional deep and sometimes thermal groundwaters that plot slightly below the LMWL and (b) δ18O-2H enriched groundwater which plot in the enriched end of the δ18O-δ2H space following the local evaporation sign. The latter group belongs to waters from shallow wells within the irrigation area and within the surface water catchment of the lake. [5,29,30,31] and attribute the δ18O-2H depleted waters representing old waters circulating in deeper groundwater zones in the rift valley aquifers. These waters return uniform 14C age values of around 2000-3000 yrs [5]. Comparison between the modern day δ18O-δ2H in rainfalls at Addis Ababa IAEA/WMO station with the δ18O-δ2H depleted waters show the groundwaters are too depleted to have been recharged from modern rains, and thus, are probably recharged under different climate condition in the past. [6] and [15] postulated from isotopic and groundwater modeling evidence that the old δ18O-δ2H depleted water discharge in the Lake Beseka area. The isotopic evidence was based on the presence of the δ18O-δ2H depleted (δ18O = −4.48‰ and δ18O = −3.15 ‰) groundwaters (one borehole and one spring) within the upstream catchment of the Lake Beseka discharging as spring at the lake and within a few hundred meters from the lake (Figure 3). However, we will later show that the deep δ18O-δ2H depleted water is not the main cause of lake level changes.
Figure 4 shows four water types. Lake Beseka plots below the LMWL indicating evaporative fractionation the water. The same evaporation signal is observed in the Awash River waters collected from a transect extending from the source region in central plateau to lower Awash basin. The regional deep groundwaters show the most depleted signal showing δ18O less than −4‰. Groundwaters from water wells in the irrigation areas surrounding of Lake Beseka show a distinct signal from that of the regional deep groundwaters originating from the plateau and they plot along the Local Evaporation Line and on enriched end of the LMWL.
The intersection point between the LEL and the LMWL shows an enriched δ18O-δ2H value compared to the deep regional groundwaters, testifying the deep regional groundwater is not contributing significant water to the lake. The LEL points to or originates from water with δ18O and δ2H composition similar to local groundwaters with in the Lake Beseka surroundings (including groundwaters from Metahara state farm) testifying the origin of the Lake Beseka is from shallow groundwaters linked to the irrigation farms. A closer look at the comparison between the composition of shallow groundwaters around Lake Beseka area and the Awash River waters show slight compositional difference. The shallow groundwater has slightly depleted δ18O-δ2H composition compared to the Awash River water testifying the shallow water is derived from some slight admixture (around 10%) of regional groundwaters and more than 90% irrigation water drawn from the Awash River. This means the major source of the water that is responsible for Lake Beseka expansion has its composition similar to the shallow groundwaters in the irrigation farms. Deep geothermal and deep groundwaters hypothetically cannot produce the composition of the Lake Beseka water. This precludes the fact that the spring discharge increase hypothesized by previous authors [2,7] has been as the result of the upwelling of the deep geothermal groundwater. We hypothesize the increase in the spring discharge is caused by irrigation excess water that enters the groundwater and returns to surface after heated by the local thermal activity.
One of the other hypotheses about the cause of the lake level rise was the disturbance in the regional groundwater regime (water level) following the construction of the Koka dam (Figure 3) and thereby increase in groundwater discharge to Lake Beseka [8]. In a farm further upstream (the Wonji farm) (Figure 4) groundwaters shows enriched δ18O-δ2H content similar to the Awash River. At around +4 ‰ in δ18O, lake Koka is enriched too [32]. Inspection of Figure 3 shows almost all deep well waters between Lake Koka and Lake Beseka show little isotopic enrichment with little sign of admixture from the Koka reservoir. The effect of Lake Koka on adjacent groundwaters is limited to few hundred meters within the outflow zone [32]. Thus, these observations preclude the role of the Koka dam construction in affecting the water volume in Lake Beseka.
Groundwater outflow is already detected downstream of Lake Beseka from the spatial plot of δ18O in groundwaters. The waters downstream (Figure 4) show enrichment in δ18O as a result of mixing of the regional groundwater with groundwater leakage from the lake.

4.3. Quantifying Rate of Groundwater Inflow-222Rn Balance Method

Using the steady state solution for the 222Rn flux (Equation (3)) and considering the input parameters in Table 2, the groundwater inflow to the lake has been estimated. The value is presented in Table 5.
Regardless of the inherent uncertainties in some of the input parameters and the various assumptions made, the estimated groundwater inflow rate (4.6 m3/s) is comparable with the net irrigation water loss to seepage 4.89 m3/s from the three irrigation schemes upstream of the lake (Table 1). Compared with previous estimates [6,7], the 222Rn based method gives higher groundwater inflow rate. This probably relate to the fact that the year 2014 represent a different hydrologic regime with the introduction of the Fentale canal and the associated irrigation schemes and the resulting substantial leakage loss from the Fentale canal. In the year 2010 for instance water loss to subsurface leakage has been estimated at 2.85 m3/s from the Fentale irrigation scheme [10].
Another independent check on the validity of the result is the observation made on the outflow from the lake. The outflow from the lake during the week of sampling was estimated at around 2.5 m3/s and it was comparable to the difference between the estimated groundwater inflow rate and the evaporation rate from the lake. A more complete independent check of the inflow rate however can be obtained from a simple water balance computation. If one assumes the water balance of the lake during the hydrologic condition of March 2014 represent the steady state hydrologic condition; Equation (1) can be solved for total surface and groundwater outflow rate (Q) plugging in the groundwater inflow rate (G) obtained from the 222Rn balance method. This resulted in a total outflow rate of 2.2 m3/s. This is comparable with the surface water outflow via the artificial canal connected to the lake. The challenge with this assertion is the fact that some of the outflow could go through subsurface path as also observed from δ18O plot (Figure 5). The presence of groundwater outflow from the lake can be confirmed from a closer look of the δ18O pattern of groundwater around lake Beskea (Figure 5). The enrichment in the groundwaters east of the lake (some of the wells are now buried) is indicative of groundwater outflow in eastern direction of the lake. The isotope pattern mirrors the general west to east groundwater flow pattern described in [10] from piezometric evidence. Given the low permeability of the ignimbrite and ash beds under the very thin lacustrine sediments east of Lake Beseka zone, the groundwater outflow rate can be considered small.

4.4. Sensitivity Analysis

The inherent uncertainty in the model input parameters necessitates sensitivity analysis run so as to evaluate the validity of the results. Figure 6a–f shows sensitivity of the model output (groundwater inflow rate) to the various input parameters. The sensitivity was run on selected input parameters. The selection is based on the level of confidence on the value of the input parameters, i.e., parameters on which we made assumptions were chosen for sensitivity run and parameters for which exact measurement exist were not chosen (e.g., lake geometry parameter, area and volume were not chosen and the measured evaporation rate, precipitation rate). In the sensitivity test a parameter value was forced to vary by certain amount keeping all other values as in the model input value except for the one under consideration. The chosen variables were (a) the 222Rn concentration of the lake ( C L ), (b) the diffusive 222Rn flux (F), (c) the gas exchange velocity ( k ), (d) the concentration of the groundwater end member ( C G ) and (e) the surface water inflow rate which was estimated from runoff coefficient consideration (S) and its 222Rn concentration (CS)
The sensitivity run shows the model output is less sensitive to the diffusive 222Rn flux (F), the gas exchange velocity (k) and the runoff from the catchment (S) and its 222Rn composition. The model is highly sensitive to the 222Rn concentration of the lake water ( C L ) and that of the groundwater inflow ( C G ). The fact that groundwater inflow to the lake comes through discrete fractures increase our confidence in the assumption that concentration of the inflowing water is similar to the concentration of the wells water, some better constraint on the composition of the lake water particularly (a) depth profiling of the 222Rn content and (b) area and or volume integrated averaging of the 222Rn content of the lake as well as detailed and dense surveying of the 222Rn content would improve the confidence on the model output.

4.5. Mitigation and Water Resources Implication

Socio-economic and engineering problem caused by a simple hydrologic change creates a condition that is extremely complex to mitigate. This is because the mitigation measures that can be taken will have socio economic and environmental tradeoffs by themselves. Quantifying and modeling the unintended consequences of mitigation measures is not the objective of the current work. However, there are three choices to be taken to mitigate the problem associated with the lake level rise. The first is to improve irrigation efficiency to minimize the groundwater inflow through seepage loss from the farms, the second is to regulate lake outflow and the third is to intentionally divert more river water into the lake to dilute it and use it as a fresh water reservoir. Each of these measures has its own advantages and disadvantages as discussed below.
Improving irrigation efficiency: Improving irrigation efficiency in the farms would reduce the seepage loss from the farms to the groundwater. This will ultimately reduce the groundwater inflow to the lake. However, any reduction of groundwater inflow to the lake would result in a decrease in lake outflow and thereby an increase in salinity of the lake. Thus, the saline lake will remain a permanent threat. Given that it starts to flow out again because of other hydrological reasons, such as exceptional heavy rains or other geological reasons, the lake can cause salinization of the Awash River system downstream of the lake. Improving irrigation efficiency will have also its cost implication to the state farms.
Regulating release of lake outflow: As of the year 2010, the lake has been spilling to the Awash River. Since the Awash River has sufficient discharge and dilute chemistry during wet season, it can buffer the effect of the saline water outflow from the lake to the level that can be used for drinking and irrigation water use. However, during the dry season when the discharge of the river is low, the buffering capacity of the Awash River is low. As already observed, unregulated spilling of the lake into the River during the dry season causes high level of salinity and high level of other undesirable elements (such as fluoride) in the river. A regulated release of the lake water to Awash River, taking into consideration the discharge and the chemistry of the Awash River, can be used as an immediate measure to mitigate the impact of the Lake on dry season river salinity.
Intentionally diverting the Awash River into the Lake: Disregarding the ecological changes that would come along with additional dilution of the lake water, diverting the dilute Awash River into the lake can cause additional reduction of salinity of the lake to the level it can be used as a fresh water reservoir. This requires detailed accounting of chemical and hydrological mass balance and engineering considerations. Given that the dilution of the lake Water is successfully conducted, the lake water can be used as a fresh water reservoir of substantial volume.

5. Conclusions

The few cases of 222Rn use in lake studies focus on methodological development as well as on quantifying groundwater inflow to lakes. This study shows a case were the combined 222Rn and δ18O-δ2H study could yield an important information in a practical water management question. The current work shows how the approach could be successfully used in addressing practical field problems regardless of the uncertainties associated with estimating 222Rn balance model input parameters. The deficiencies encountered in using 222Rn in quantifying groundwater inflow to the lake have been filled by using the combined δ18O-δ2H tracers. The capability of 222Rn in (a) detecting groundwater inflow zones and (b) quantifying groundwater inflow means that, if utilized along the δ18O-δ2H lake balance studies, the uncertainties associated with individual approaches could be substantially reduced. In this particular work, we demonstrated the combined use of 222Rn and δ18O-δ2H tracer for detecting source of groundwater to the lake as well as quantifying its flux. The deficiency in δ18O-δ2H data and the non-steady state hydrology of the lake did not allow quantitative estimation of the groundwater inflow from δ18O-δ2H application. Application of δ18O-δ2H for estimating water flux around the lake could be subject for future research.
The dual 222Rn and δ18O-δ2H isotope systematic effectively demonstrate the origin of water and causes of growth of Lake Beseka. It is concluded that the cause of the lake swelling is related to irrigation return water joining the lake via subsurface flow paths. The regular horizontal 222Rn pattern (decreasing from west to east and SW to NE) is an indication of a simple geologic setting where by discharge of groundwater to lake is taking place in restricted spots (in SW, S and W) ruling out the importance of lake deformation and the role of the ground fissures located in the northern sector of the lake. The ground fissures in the S, SW and W plays a role in enhancing the permeability of the rocks.
Although 222Rn is a proven tool in detecting sites of groundwater discharge into surface waters and lakes the type of that groundwater (shallow or deep) could not be identified from use of 222Rn alone [16]. The insufficiency of the 222Rn method in deciphering the source of the groundwater (deep or shallow) has been overcome in this study by using the δ18O-δ2H tracer in pinpointing the type of groundwater (shallow or deep) feeding the lake. The two types of evidence show that the water that led to the changes in hydrology of the lake is the increase in discharge of shallow groundwater (though springs), which in turn is related to irrigation return water from the expanding irrigation fields. The dense fractures leading to high permeability and porosity of the basaltic aquifers underlying the western sector of the catchment has aided rapid transfer of the infiltrating water to reach the lake. The other option of running unsteady state δ18O-δ2H balance model was not achieved because of paucity time series data over the lake’s historical change in volume and isotopic composition.
The conclusions made about the origin of water do not contradict the conclusion made by [7] in that the increase in discharge of springs in the western and south western sector of the lake is responsible to increase the lake water gain. However, the current work pinpoints the origin of the water to be from irrigation return water entering the geothermal reservoir and returning to the lake as the main source of water. While the mechanism was clearly supported with evidence, [7] left an open question about where the actual water that leads to the increase in the discharge of the spring comes from. The isotopic evidence in this work reveals the source of the actual water is irrigation return water following the subsurface flow path. Because of the shallow geothermal gradient related to the Fentale volcano [4], the shallow groundwaters can easily be heated, thereby imparting high temperature to the seepage waters leading to the temperature profile observed by [7].
The water balance of the lake computed using 222Rn can represent the annual average values. This is the water balance of the lake that is dominated by groundwater flow, and the seasonality in rainfall and surface flows is small. However, improvement in the computed groundwater flux could be made if monthly time series of 222Rn has been obtained from the lake and the groundwaters.
A more recent view of irrigation efficiency suggests the irrigation loss to seepage should not be counted as a loss as far as that water returns to the river system and service downstream users [34]. In the case of Lake Beseka, the irrigation loss has led to environmental, engineering and socio-economic challenges, necessitating a more unified definition of irrigation efficiency accounting in not only water quantity but also quality.

Author Contributions

Conceptualization and writing, S.K.; field data collection and data compilation, S.Z.

Funding

See acknowledgment section below.

Acknowledgments

This document is an output supported by the REACH program funded by UK Aid from the UK Department for International Development (DFID) for the benefit of developing countries (Aries Code 201880). The views expressed and information contained in it are not necessarily those of or endorsed by DFID, which can accept no responsibility for such views or information or for any reliance placed on them. The authors acknowledge the logistics support by Addis Ababa University, School of Earth Sciences and the Ministry of Water Irrigation and Electricity-Ethiopia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wood, R.B.; Tailing, J.F. Chemical and algal relationships in a salinity series of Ethiopian inland waters. Hydrobiologia 1988, 158, 29–67. [Google Scholar] [CrossRef]
  2. Alemayehu, T.; Ayenew, T.; Kebede, S. Hydrogeochemical and lake level changes in the Ethiopian Rift. J. Hydrol. 2006, 11, 290–300. [Google Scholar] [CrossRef]
  3. Asfaw, L. Environmental hazard from fissures in the Main Ethiopian Rift. J. Afr. Earth Sci. 1998, 27, 481–490. [Google Scholar] [CrossRef]
  4. Williams, F.M.; Williams, M.A.J.; Aumento, F. Tensional fissures and crustal extension rates in the northern part of the Main Ethiopian Rift. J. Afr. Earth Sci. 2003, 38, 183–197. [Google Scholar] [CrossRef]
  5. Kebede, S.; Travi, Y.; Asrat, A.; Alemayehu, T.; Ayenew, T.; Tessema, Z. Groundwater origin and flow along selected mountain-valley transects in Ethiopian rift volcanic aquifers. Hydrogeol. J. 2009. [Google Scholar] [CrossRef]
  6. Belay, E.A. Growing Lake with Growing Problems: Integrated Hydrogeological Investigation on Lake Beseka, Ethiopia. Ph.D. Thesis, University of Bonn, Bonn, Germany, 2010. [Google Scholar]
  7. Goerner, A.; Jolie, E.; Gloaguen, R. Non-climatic growth of the saline Lake Beseka, Main Ethiopian Rift. J. Arid Environ. 2009, 73, 287–295. [Google Scholar] [CrossRef]
  8. Tessema, Z. Hydrochemical and Water Balance Approach in the Study of High Water Level Rise of Lake Beseka. Master’s Thesis, University of Birmingham, Edgbaston, UK, 1998. [Google Scholar]
  9. Ayenew, T. Environmental implications of changes in the levels of lakes in the Ethiopian Rift since 1970. Reg. Environ. Chang. 2004, 4, 192–204. [Google Scholar] [CrossRef]
  10. MoWIE. Assessment and Evaluation of the Causes for Beseka Lake Level Rise and Designing Mitigation Measures Part II: Study for Medium-and Long-Term Solutions, Main Report; Ministry of Water Irrigation and Energy: Addis Ababa, Ethiopia, 2014. [Google Scholar]
  11. Dincer, T. The use of oxygen-18 and deuterium concentrations in the water balance of lakes. Water Resour. Res. 1968, 4, 1289–1305. [Google Scholar] [CrossRef]
  12. Zuber, A. On the environmental isotope method for determining the water balance components of some lakes. J. Hydrol. 1983, 61, 409–427. [Google Scholar] [CrossRef]
  13. Krabbenhoft, D.P.; Bowser, C.J.; Anderson, M.P.; Valley, J.W. Estimating groundwater exchange with lakes. 1. The stable isotope mass balance method. Water Resour. Res. 1990, 26, 2445–2453. [Google Scholar] [CrossRef]
  14. Yi, Y.; Brock, B.E.; Falcone, M.D.; Wolfe, B.B.; Edwards, T.W.D. A coupled isotope tracer method to characterize input water to lakes. J. Hydrol. 2008, 350, 1–13. [Google Scholar] [CrossRef]
  15. Kebede, S.; Travi, Y.; Rozanski, K. The δ18O and δ2H enrichment of Ethiopian lakes. J. Hydrol. 2010, 365, 173–182. [Google Scholar] [CrossRef]
  16. Cook, P.G.; Wood, C.; White, T.; Simmons, C.T.; Fass, T.; Brunner, P. Groundwater inflow to a shallow, poorly-mixed wetland estimated from a mass balance of radon. J. Hydrol. 2008, 354, 213–226. [Google Scholar] [CrossRef]
  17. Dimova, N.T.; Burnett, W.C.; Chanton, J.P.; Corbett, J.E. Application of radon-222 to investigate groundwater discharge into small shallow lakes. J. Hydrol. 2013, 486, 112–122. [Google Scholar] [CrossRef]
  18. Kebede, E.; Gebremariam, Z.; AhlgrenI, I. The Ethiopian Rift Valley lakes: chemical characteristics of a salinity-alkalinity series. Hydrobiologia 1994, 288, 1–12. [Google Scholar] [CrossRef]
  19. Pitwell, L.R. Analysis of Ethiopian and other natural waters. UNWMO 1971, 301, 2–31. [Google Scholar]
  20. Talling, J.F.; Talling, I.B. The chemical composition of African lake waters. Int. Rev. Gesamten Hydrobiol. 1965, 50, 421–463. [Google Scholar] [CrossRef]
  21. FAO. Coping with Water Scarcity, the Role of Agriculture. Developing a Water Audit for Awash River Basin; SYNTHESIS REPORT GCP/INT/072/ITA; FAO: Addis Ababa, Ethiopia, 2013; p. 46. [Google Scholar]
  22. Schmidt, A.; Gibson, J.; Santose, I.; Schubert, M. The contribution of groundwater discharge to the overall water budget of two typical Boreal lakes in Alberta/Canada estimated from a radon mass balance. Hydrol. Earth Syst. Sci. 2010, 11, 79–89. [Google Scholar] [CrossRef] [Green Version]
  23. Peterson, R.N.; Burnett, W.C.; Taniguchi, M.; Chen, J.; Santos, I.R.; Ishitobi, T. Radon and radium isotope assessment of submarine groundwater discharge in the Yellow River delta, China. J. Geophys. Res. 2008, 113, C09021. [Google Scholar] [CrossRef]
  24. Kluge, T.; von Rohden, C.; Sonntag, P.; Lorenz, S.; Wieser, M.; Aeschbach-Hertig, W.; Ilmbe, J. Localizing and quantifying groundwater inflow into lakes using high-precision 222Rn profile. J. Hydrol. 2012, 450, 70–81. [Google Scholar] [CrossRef]
  25. Schmidt, A.; Schlueter, M.; Melles, M.; Schubert, M. Continuous and discrete on-site detection of radon-222 in ground-and surface waters by means of an extraction module. Appl. Radiat. Isot. 2008, 6, 1939–1944. [Google Scholar] [CrossRef] [PubMed]
  26. Burnett, W.C.; Bokuniewicz, H.; Huettel, M.; Moore, W.S.; Taniguchi, M. Groundwater and porewater inputs to the coastal zone. Biogeochemistry 2003, 66, 3–33. [Google Scholar] [CrossRef]
  27. Gat, J.R.; Levy, Y. Isotope hydrology of island sabkhas in the Bardawil area, Sinai. Limnol. Oceanogr. 1978, 23, 841–850. [Google Scholar] [CrossRef]
  28. Gonfiantini, R. Environmental isotopes in lake studies. In Handbook of Environmental Isotope Geochemistry; Fritz, P., Fontes, J.C., Eds.; Elsevier: New York, NY, USA, 1986; Volume 13, pp. 113–168. [Google Scholar]
  29. Craig, H.; Lupton, J.E.; Horowiff, R.M. Isotope Geochemistry and Hydrology of Geothermal Waters in the Ethiopian Rift Valley; Scripps Institute of Oceanography University of California: San Diego, CA, USA, 1977. [Google Scholar]
  30. Bretzler, A.; Osenbrück, K.; Gloaguen, R.; Ruprecht, J.S.; Kebede, S.; Stadler, S. Groundwater origin and flow dynamics in active rift systems—A multi-isotope approach in the Main Ethiopian Rift. J. Hydrol. 2011, 402, 274–289. [Google Scholar] [CrossRef]
  31. Kebede, S.; Travi, Y.; Stadler, S. Groundwaters of the Central Ethiopian Rift: diagnostic trends in trace elements, δ18O, and major elements. Environ. Earth Sci. 2010, 61, 1641–1655. [Google Scholar] [CrossRef]
  32. Mamo, S.; Yokota, S. Estimation of Koka reservoir leakage by hydrogeological and isotope techniques. In Proceedings of the Eighth International Congress International Association for Engineering Geology and the Environment, Vancouver, BC, Canada, 21–25 September 1998. [Google Scholar]
  33. Halcrow, S.W.; Partners Ltd. Masterplan for the Development of Surface Water Resources in the Awash Basin; Ministry of Water Resources: Addis Ababa, Ethiopia, 1989.
  34. Perry, C. Accounting for water use: Terminology and implications for saving water and increasing production. Agric. Water Manag. 2011, 98, 1840–1846. [Google Scholar] [CrossRef]
Figure 1. Characteristics of the study area: geology, inundated areas, extent of farms, irrigation canal location and infrastructure damaged by the lake expansion.
Figure 1. Characteristics of the study area: geology, inundated areas, extent of farms, irrigation canal location and infrastructure damaged by the lake expansion.
Water 11 02591 g001
Figure 2. 222Rn composition map of the lake and groundwater, the size of the circles are proportional to the 222Rn content, the numbers marked near the circles indicate the actual 222Rn content of the site.
Figure 2. 222Rn composition map of the lake and groundwater, the size of the circles are proportional to the 222Rn content, the numbers marked near the circles indicate the actual 222Rn content of the site.
Water 11 02591 g002
Figure 3. Spatial pattern in δ18O in regional groundwaters and groundwaters from the Lake Beseka catchment. The δ2H pattern is not presented because it similar pattern and the δ2H information could be drawn from Figure 5.
Figure 3. Spatial pattern in δ18O in regional groundwaters and groundwaters from the Lake Beseka catchment. The δ2H pattern is not presented because it similar pattern and the δ2H information could be drawn from Figure 5.
Water 11 02591 g003
Figure 4. δ18O-δ2H plot of waters from Lake Beseka Surrounding and regional deep groundwaters.
Figure 4. δ18O-δ2H plot of waters from Lake Beseka Surrounding and regional deep groundwaters.
Water 11 02591 g004
Figure 5. Spatial variation in δ18O of groundwaters around Lake Beseka.
Figure 5. Spatial variation in δ18O of groundwaters around Lake Beseka.
Water 11 02591 g005
Figure 6. Sensitivity run, ran individually by fixing other parameters as in Table 2. (a) sensitivity to diffusive radon flux, (b) sensitivity to radon concentration in Lake, (c) sensitivity to radon concentration in groundwater, (d) sensitivity to runoff coefficient, (e) sensitivity to gas exchange velocity, (f) sensitivity to radon concentration of surface waters.
Figure 6. Sensitivity run, ran individually by fixing other parameters as in Table 2. (a) sensitivity to diffusive radon flux, (b) sensitivity to radon concentration in Lake, (c) sensitivity to radon concentration in groundwater, (d) sensitivity to runoff coefficient, (e) sensitivity to gas exchange velocity, (f) sensitivity to radon concentration of surface waters.
Water 11 02591 g006aWater 11 02591 g006b
Table 1. Net loss m3/s of irrigation water to groundwater percolation over the period application [10].
Table 1. Net loss m3/s of irrigation water to groundwater percolation over the period application [10].
FarmStart DateFarm Size (ha)Uptake (m3/s)Conveyance Loss (m3/s)Distribution Loss (m3/s)Field Level Water Application Loss (m3/s)Net Seepage Loss (m3/s)
Main-Metahara-196580006.02--0.601.41
Abadir-Metahara196835003.51--0.351.06
Nura Hira197037402.55---1.02
Fentale20074520 *4.611.20.31.32.81
Table 2. Description of terms used in Equations (1) and (2).
Table 2. Description of terms used in Equations (1) and (2).
VariableUnitExplanation
Gm3/dayGroundwater inflow rate
Sm3/daySurface water inflow from channelized and overland flows
Pm/dayDepth of precipitation on the lake
Qm/dayAggregate water loss to surface and groundwater outflows
ALm2Lake Area, m2
Em/dayDepth of evaporation from lake surface
Abm2Area of Lake bottom surface contributing 222Rn flux via diffusive pathways from sediments
Vm3Lake volume
λday−1222Rn decay constant
kmday−1Gas exchange velocity (wind speed and temperature turbulent degassing rate)
FBq/m2/dayDiffusive 222Rn flux from lake bottom sediments, F is computed as in [16,22,23] taking into consideration the lowest 222Rn content in the lake corresponds to the sources from diffusive flux only FA b =   kAC L + λ VC L where all the parameters except the diffusive flux F are known
CLBq/LMean 222Rn activity in the lake
CGBq/LMean 222Rn activity in inflowing groundwater obtained from measurement in a borehole
CSBq/LMean 222Rn activity in inflowing runoff
Table 3. 222Rn data from Lake Beseka and groundwater around the lake (standard deviation of individual measurement varies between 15 and 25%). UTM coordinates are for 37N region.
Table 3. 222Rn data from Lake Beseka and groundwater around the lake (standard deviation of individual measurement varies between 15 and 25%). UTM coordinates are for 37N region.
CodeUTM, EastUTM, NorthMean Bq/m3Highest Bq/m3Lowest Bq/m3
LBRn-12593,967982,94524133697.7
BARn-1596,605973,681391062901600
LBRn-19594,903981,6205486.318.9
LBRn-OL600,000982,4263144.712.6
LBRn-CO594,663975,450294549124
LBRn-11592,610981,5057981200138
LBRn-14′596,650983,5093538.431.5
LBRn-13594,748984,042453641104
LBRn-21599,118983,7371829.69.44
LBRn-3594,651975,441191231158
LBRn-9591,564979,25120602670864
LBRn-20597,419982,370414810
LBRn-5591,653976,33223403620684
LBRn-17598,483980,5463451.19.44
LBRn-14596,809983,1132738.918.9
LBRn-15599,385982,689223412
Table 4. Description of terms used in 222Rn mass balance equation.
Table 4. Description of terms used in 222Rn mass balance equation.
VariableUnitValue and Source
Sm3/day81,500
Pm/day0.00151, meteorology station
Em/day0.00548, meteorology station
Qm/dayAggregate water loss to surface and groundwater outflows (not required in Equation (3))
ALm249,000,000 (excluding the area of the island)
Abm249,000,000
Vm2243,000,000 (estimated from bathymetric survey [10])
λday−1222Rn decay constant
kmday−10.16 [16]
FBq/m2/day5.78
CLBq/L0.4 (mean of 222Rn in central and northern part of the lake)
CGBq/L3.9 (measured in a borehole in Abadir farm)
CSBq/L0
Table 5. Annual groundwater inflow rate in 106 m3 to the lake, estimates from previous studies are included.
Table 5. Annual groundwater inflow rate in 106 m3 to the lake, estimates from previous studies are included.
Current StudyBelay, 2010 [6]Goerner et al., 2009 [7]MWIE, 2014 [10]HALCROW, 1989 [33]Tesemma, 1998 [8]
1703317103 *1.537
* The value is for base year 2010.

Share and Cite

MDPI and ACS Style

Kebede, S.; Zewdu, S. Use of 222Rn and δ18O-δ2H Isotopes in Detecting the Origin of Water and in Quantifying Groundwater Inflow Rates in an Alarmingly Growing Lake, Ethiopia. Water 2019, 11, 2591. https://doi.org/10.3390/w11122591

AMA Style

Kebede S, Zewdu S. Use of 222Rn and δ18O-δ2H Isotopes in Detecting the Origin of Water and in Quantifying Groundwater Inflow Rates in an Alarmingly Growing Lake, Ethiopia. Water. 2019; 11(12):2591. https://doi.org/10.3390/w11122591

Chicago/Turabian Style

Kebede, Seifu, and Samson Zewdu. 2019. "Use of 222Rn and δ18O-δ2H Isotopes in Detecting the Origin of Water and in Quantifying Groundwater Inflow Rates in an Alarmingly Growing Lake, Ethiopia" Water 11, no. 12: 2591. https://doi.org/10.3390/w11122591

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop