# The Impact of a Check Dam on Groundwater Recharge and Sedimentation in an Ephemeral Stream

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{3}) recharged the aquifer with an average of 3.1 million m

^{3}of the 10.4 million m

^{3}year

^{−1}of streamflow (30%). The lower and upper uncertainty limits of the check dam recharge were 0.1 and 9.6 million m

^{3}year

^{−1}, respectively. Recharge from the upstream stretch was 1.5 million m

^{3}year

^{−1}. These results indicate that check dams are valuable structures for increasing groundwater resources in semi-arid regions.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area

^{−1}in the Peristerona watershed.

#### 2.1.1. Transient Zone

^{2}, up to AS 98 km

^{2}, and up to OB 105 km

^{2}.

#### 2.1.2. Check Dam Zone

#### 2.2. Field Monitoring

^{3}s

^{−1}) using the mid-section method [27]. The distances between the stations (vertical sections) in each profile were such that no individual station contains more than 10% of the total discharge. The maximum observed streamwater profile was 0.7 m deep and 11 m wide. Measurements were made during the 2014 to 2015 streamflow season (December 2014 to June 2015) at two locations (AS and OB). Between these points, two irrigation channels, one on the east bank and one on the west bank, divert some of the stream water to the downstream agricultural fields. Twice weekly measurements of flow in these channels were made to estimate the amount of water diverted. Diversions to the irrigation canals were irregular. Also, sometimes water was released back to the streambed downstream from the canal inlets and before OB.

^{−3}) of the sediment profile were collected and averaged to compute the sediment mass from 24 locations.

#### 2.3. Water Balance Computations

_{r}is the recharge from the reservoir area, Q

_{in}is the water inflow, Q

_{out}is the water outflow, Q

_{e}is the evaporation, and V

_{d}and V

_{d−}

_{1}are the volumes of water stored in the check dam reservoir area on the current and previous day, respectively. All units are in m

^{3}.

_{in}) was estimated with a linear regression model. Evaporation was calculated using an equation that is a combination of the mass-transfer equation introduced by Harbeck [28] (suitable for lakes with a surface area (A) in the range of 50 m < A

^{0.5}< 100 km located in arid environments) and the equation of Penman and Priestley-Taylor modified by de Bruin [29]:

^{2}), γ is the psychometric coefficient (-), e

_{s}is the mean saturation vapour pressure of the air (kPa), e is the actual vapour pressure of the air (kPa), Δ is the slope of the vapour pressure curve (kPa C

^{0−1}), and α is the Priestley-Taylor coefficient defined as:

^{−1}). Daily meteorological data is obtained from nearby stations.

_{d}(h), Q

_{out}(h), and Q

_{r}(h). To derive Q

_{r}(h), water balance calculations were performed only with actual measurements. The derived functions were used to calculate the daily potential Q

_{out}and Q

_{r}, which set the daily upper limits. However, the actual Q

_{out}and Q

_{r}values can be lower than the estimated Q

_{out}(h) and Q

_{r}(h) because the rate of the change in h throughout a day is ignored. In order to average this change and reduce the error in the estimates, the h value in the middle of a day (h

_{mid}) is taken for the calculations; h

_{mid}is the h of the total water at the end of the previous day in the reservoir plus half of the Q

_{in}of the current day. Summarizing, daily Q

_{r}and Q

_{out}were calculated as:

_{e}(h

_{mid}) is the volume of water evaporated from an open water reservoir area given a water height h

_{mid}, and V

_{p}

_{1}is the volume of the water (m

^{3}) up to the bottom of the pipes.

_{d}, Q

_{e}, and Q

_{r}calculations, h is limited up to the spillway bottom elevation. The calculations were performed with Microsoft Excel

^{®}(Redmond, WA, USA).

#### 2.4. Uncertainty Analyses

^{3}s

^{−1}. In the literature, comparable error values have been reported for similar measurement ranges [33]. For the weir measurements, errors are also assumed to be ±6% [34]. The uncertainty intervals of the evaporation estimates were considered ±5%, based on literature [35].

_{r_max}) and minimum recharge (Q

_{r_min}) estimates as follows:

_{r_min}, Q

_{in}

_{_min}, Q

_{out_min}, and Q

_{e_min}are the lower bounds and Q

_{r_max}, Q

_{in}

_{_max}, Q

_{out_max}, and Q

_{e_max}are the upper bounds of the uncertainty intervals. Negative lower bounds were set to zero.

## 3. Results and Discussion

#### 3.1. Transient Zone

^{3}s

^{−1}to be hydraulically connected to AS and 0.23 m

^{3}s

^{−1}to be connected to OB. This was expected since the distance between PB and OB is longer than that between PB and AS. In addition, when the discharge at PB is less than 2.5 m

^{3}s

^{−1}, the discharge at AS is lower than PB, indicating transmission losses from the stream (groundwater recharge) along the PB-AS river stretch. When the discharge at PB exceeds 2.5 m

^{3}s

^{−1}, the discharge at AS is greater than that at PB, indicating river discharge gains from groundwater or small side streams. Similarly, for the AS-OB river stretch, the threshold between transmission losses and gains can be set for a discharge at AS of 1.2 m

^{3}s

^{−1}. The Cyprus Geological Survey Department measures groundwater levels approximately once per month in wells in the proximity of the river bed. The groundwater surface elevation in these wells from 2011 to 2015 was never higher than the streambed elevation during the streamflow season, suggesting that the increase in streamflow downstream of AS is most likely caused by side streams. Similar streamflow behavior has been reported in the literature for rivers in arid and semi-arid climates, e.g., [36,37,38]. Barthel and Banzhaf [38] noted the difficulties of modelling such surface water and groundwater interactions because the parameters tend to exhibit large spatial and temporal variabilities.

#### 3.2. Check Dam Zone

^{3}, and up to the spillway bottom (262 m a.s.l.) as 24,923 m

^{3}.

_{r}values obtained by water balance calculations with observed data and their exponential relation to h and the observed values of Q

_{out}and h and a third order polynomial trendline, which was used for modelling the potential daily Q

_{out}(h up to the spillway bottom. The upper and lower limit lines of the 90% prediction interval are also presented.

_{r}-h relation is supported by the exponential increase of the water volume in the check dam reservoir with increasing h (Figure 5a). This equation was used to model the potential Q

_{r}(h). It can be seen from the Q

_{r}-h observations that Q

_{r}increases slowly with increasing h and then (around h equal to 3.8 m) increases abruptly. This can be caused by the fact that the bottom of the check dam reservoir is below the ground level of the surroundings and the lower part of the reservoir walls are thicker than the upper parts and partly covered with sediment. The upper parts of the reservoir walls are above ground level, built with boulders and without sediment cover. Therefore, Q

_{r}can occur slowly to a certain level of h and then increase rapidly. However, instead of having two Q

_{r}-h relations (one for smaller and one for higher values than the threshold h), a single exponential relation was assumed. The single exponential relation has the advantage of reflecting, better than two distinct linear relations, the expected uncertainties associated with the Q

_{r}estimates, as the upper and lower limits of the prediction interval show a relatively wide possible range of Q

_{r}values given a certain h. High uncertainties are expected because of three main reasons: (i) the hourly change in Q

_{r}is expected to play a role and daily measurements alone are not able to reflect this; (ii) Q

_{r}is calculated from measured water balance components, which means that the measurement errors of each component contribute to the error in Q

_{r}estimates; and (iii) sediment build-up in the reservoir can cause changes in Q

_{r}over time. Frequent measurements of the flows and sediment-build up could improve the Q

_{r}estimates.

_{out}) is affected by differences among the bottom heights and slopes of the pipes and by disturbances at the inlet of the pipes, the Q

_{out}-h polynomial relation is similar to the typical water depth to discharge relation for circular pipes.

#### 3.3. Water Balance Components

^{3}stream water is lost as transmission losses or recharged to the groundwater over four years. Evaporation is calculated to be 8% of the total loss (not presented). For this zone, the total losses are 4.6 times higher for the segment between PB and AS (7.8 km long on fractured bedrock) than for the segment between AS and OB (3.2 km long on alluvial deposits). This could be due to three main reasons: (i) the majority of the flow is generated upstream of PB, and, because PB-AS and AS-OB are both segments with transmission losses, the downstream stretch (AS-OB) receives less flow for recharge (over the four year period, there were 1029 days of flow at PB, 605 days at AS, and 401 days at OB); (ii) the PB-AS segment crosses four different geological units (Figure 1), two of which are classified as high-groundwater-recharge units due to their fractured nature [21], so preferential flow paths potentially exist and transmission losses and groundwater recharge could be higher than in the alluvial deposits [38,39]; and (iii) a faster increase of the streamflow at AS-OB compared with PB-AS. Several field observations support the third reason as the most important. The PB-AS segment is mostly covered with natural vegetation (e.g., coniferous forest), which creates surface runoff with relatively higher rainfall events in comparison with the bare land for the segment AS-OB [18]. In fact, opposite to AS-OB, the streams at PB-AS were observed to start flowing and discharging only after high rainfall events. In addition, irrigation diversions along AS-OB can also be reasons for a rapid increase of the streamflow. The capacity of the irrigation canals is limited and, after large flows, their inlet can be blocked by the debris. The effect of the third reason can be observed when we compare the model estimates of the streamflow of a wet and a dry year. During the wet 2011 to 2012 season, the model estimated a total 0.7 million m

^{3}increase in streamflow between AS and OB, while, during the dry 2012 to 2013 season, along the same stretch, the model estimated a total of 1 million m

^{3}in transmission losses.

^{3}for their study; 25,000 m

^{3}in our research. They concluded that the percentage of recharge over the total inflow ranged between 6 and 53%, with higher values for higher check-dam capacities.

^{3}, which is 38% of the observed PB discharge. Based on groundwater model calibrations for the Western Mesaoria, Udluft et al. [41] found that groundwater recharge from rivers totalled 33.8 million m

^{3}year

^{−1}. Assuming that approximately one third of this amount is discharged by the Peristerona River, based on its watershed size relative to the other rivers, this number seems to overestimate recharge, considering that the mean annual discharge (1980 to 2010) of the Peristerona River at the Panagia Bridge weir station (PB, Figure 1) equals 9.75 million m

^{3}year

^{−1}[40].

_{r}(0.37 million m

^{3}to 36.61 million m

^{3}) seems quite large (Table 1). However, it should be noted that running the model with upper and lower limits (Equations (7) and (8)) is a maximalist interpretation of the errors. This approach assumes that all the water balance components are either at their upper or lower limits, which means that all errors in outflow, inflow, and evaporation affect simultaneously, at their maximum, the change in storage and are therefore reflected in the computed recharge. Nevertheless, it is remarkable to see such a great effect of the accumulated errors on the recharge for a water balance calculation.

#### 3.4. Sediment Build-up in the Check Dam Reservoir

^{−3}(minimum: 0.9 g cm

^{−3}, maximum: 1.2 g cm

^{−3}, standard deviation: 0.1 g cm

^{−3}) and, for the samples collected in 2015, was 1.1 g cm

^{−3}(minimum: 0.8 g cm

^{−3}, maximum: 1.4 g cm

^{−3}, standard deviation: 0.1 g cm

^{−3}). There was no significant variation in the bulk density values between the samples collected from different depths and in diferent years. However, the surface samples from the two locations closest to the check dam inlet had slightly higher bulk density values (1.2–1.4 g cm

^{−3}) than the average for both years. This is because these samples were sandier than the others. This can be due to the fact that coarser sediments are deposited earlier than finer sediments when the water flow velocity is reduced by the check dam [42]. The average depth of the sediment at the sample locations in 2013 was 21.6 cm (maximum: 35 cm, minimum: 5 cm and standard deviation: 8 cm) and, in 2015, was 22 cm (maximum: 48 cm, minimum: 5 cm, standard deviation: 10.8 cm).

^{−3}) and the elevation differences (average 21.6 cm between 2011 and 2013 and 22 cm between 2013 and 2015), the total sediment build-up in the check dam reservoir was estimated to be 2640 tons for the years 2011 to 2012 and 2012 to 2013 and 2770 tons for the years 2013 to 2014 and 2014 to 2015, showing no significant difference. However, the total modelled streamflow into the reservoir was three times more for the former (Table 1). The season from 2011 to 2012 had above-average inflow to the reservoir, and the 2013 to 2014 season had almost no inflow. Figure 8 shows the discharge into the reservoir per day and cumulative frequency of each day over the two-year periods of 2011 to 2013 and 2013 to 2015. It can be seen from Figure 8 that the years 2011 to 2013 had more days with higher discharge compared with the years 2013 to 2015. The years 2011 to 2013 had ten days with high flow (occurring less than 4% of the time) and none above 1.5 million m

^{3}day

^{−1}, and the years 2013 to 2015 had only five days with high flow, one of which was an extreme event (2.4 million m

^{3}day

^{−1}on 6 January 2015). These results indicate that the high flow events, occurring less than 4% of the time in our case, can carry and build up most, if not all, of the sediment. Visual assessment of the high-flow events also supports this deduction as the turbidity of the stream water for these events was significantly higher. Moreover, events occurring less than 4% of the time constituted 30% of the total inflow in 2011 to 2013 and 40% in 2013 to 2015. The importance of high-flow events for sediment transport, along Mediterranean rivers comparable to Peristerona, has been reported by many authors [43,44,45,46]. Rovira and Batalla [44] studied the temporal distribution of suspended sediment transport in a Mediterranean basin in north-eastern Spain and concluded that 90% of the total sediment flow was carried by flood events, which occur 30% of the time. Vericat and Batalla [45] reported, for a watershed in southern Pyrenees, that 74% of the total sediment load was carried by flood events that occur 4% of the time. Achite and Ouillon [46] investigated sediment transport in a mountainous watershed in Algeria for 40 years and concluded that most of the sediment was carried by the 10 to 15 highest daily discharges over a year.

^{−1}year

^{−1}for the Peristerona watershed, assuming a 15% sediment trapping efficiency of the check dam. This efficiency was based on the ratio of the watershed area (km

^{2}) and the capacity of the check dam (m

^{3}) [47] and ignores the amount of inflowing water. Assuming that the suspended sediment concentration in the water outflow is the same as that of the inflow, the trapping efficiency can be estimated to be around 30%, as the total water outflow from the check dam was 70% of the total water inflow (Table 1). Conversely, assuming that all the sediment is carried by extreme events (upper 4%) then the trapping efficiency can be estimated to be around 6%, as the total outflow from the check dam was 94% of the total water inflow for these events.

## 4. Conclusions

^{3}on an ephemeral river of a 105-km

^{2}watershed in Cyprus was able to recharge 2.1 times more groundwater than an 11-km-long river transect upstream of the check dam. This conclusion is an indication of the usefulness of such structures for the replenishment of groundwater resources in arid and semi-arid regions. We estimated the recharge through water balance calculations, and we stressed the high uncertainties of the results. This is mainly due to uncertainties in the inflow, which affect the change in storage and are reflected directly in the computed recharge. Continuous flow measurements can help to reduce the inflow uncertainties and better evaluate the performance of the check dam.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**Figure 1.**Location of the Peristerona Watershed and the Central and Western Mesaoria Aquifers [26] in Cyprus and its geology with the transient and check dam zones magnified and the streamflow measurement locations marked (modified from the Digital Elevation Model and Geological Map of Cyprus, Cyprus Geological Survey Department).

**Figure 4.**Linear regression of the observed flow data at Panagia Bridge weir (PB) with (

**a**) Alluvial Start (AS) and (

**b**) Orounda Bridge (OB), with 90% prediction intervals.

**Figure 5.**(

**a**) Check dam storage volume (primary y-axis) and open water surface area (secondary y-axis) changes with water height; (

**b**) TIN of the check dam created with topographic data collected in Summer 2013.

**Figure 6.**(

**a**) Groundwater recharge (Q

_{r}) values (derived from water balance calculations with observed data) and (

**b**) observed check dam outflow (Q

_{out}) with reservoir water depth (h) and best-fit trend lines with upper and lower limit of 90% prediction interval.

**Figure 7.**Water level in the check dam reservoir, water levels in the groundwater Wells 1 and 2, check dam inflow (stream discharge), and precipitation during the 2014 to 2015 season.

**Figure 8.**Discharge into the reservoir per day and cumulative frequency of each day over a two-year period for 2011 to 2013 and 2013 to 2015.

**Table 1.**Observed flows at Panagia Bridge (PB) and modelled flows at Alluvial Start (AS) and Orounda Bridge (OB), constituting the water balance components of the transient and check-dam zone, where Q

_{r}is the recharge from the reservoir area, Q

_{in}is the water inflow, Q

_{out}is the water outflow, and Q

_{e}is the evaporation with the best estimate. The uncertainty intervals of the modelled results are presented.

Year | Precip. ^{a} (10^{6} m^{3}) | Transient Zone | Check Dam Zone | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

Flow at PB (Observed) | Flow at AS (Modelled) | Flow at OB = Qin (Modelled) | Q_{r} | Q_{e} | Q_{out} | |||||||

(10^{6} m^{3}) | (10^{6} m^{3}) | (10^{6} m^{3}) | (10^{6} m^{3}) | (10^{6} m^{3}) | (10^{6} m^{3}) | |||||||

B ^{b} | L-U ^{b} | B | L-U | B | L-U | B | L-U | B | L-U | |||

2011–2012 | 75.56 | 23.99 | 23.13 | 19.56–28.74 | 23.84 | 20.24–30.43 | 5.86 | 0.26–13.04 | 0.007 | 0.014–0.014 | 17.98 | 17.38–19.96 |

2012–2013 | 52.38 | 8.61 | 7.22 | 4.46–12.65 | 6.25 | 3.97–12.62 | 2.93 | 0.04–8.56 | 0.004 | 0.014–0.013 | 3.31 | 4.05–3.92 |

2013–2014 | 37.95 | 1.63 | 0.41 | 0.01–6.26 | 0.04 | 0.00–6.90 | 0.04 | 0.00–6.87 | 0.001 | 0.004–0.017 | 0.00 | 0.01–0.00 |

2014–2015 | 65.54 | 13.39 | 11.95 | 8.99–18.09 | 11.54 | 8.91–18.74 | 3.62 | 0.07–10.14 | 0.004 | 0.008–0.013 | 7.91 | 8.57–8.83 |

Total | 231.43 | 47.62 | 42.72 | 33.03–65.73 | 41.66 | 33.13–68.69 | 12.44 | 0.37–38.61 | 0.02 | 0.004–0.06 | 29.21 | 30.00–32.71 |

^{a}Precipitation is calculated over the watershed area up to the check dam with area-weighted average, based on Thiessen polygons created with eight rain gauges.

^{b}B: Best estimate, L-U: Lower and Upper limit of the uncertainty interval.

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Djuma, H.; Bruggeman, A.; Camera, C.; Eliades, M.; Kostarelos, K.
The Impact of a Check Dam on Groundwater Recharge and Sedimentation in an Ephemeral Stream. *Water* **2017**, *9*, 813.
https://doi.org/10.3390/w9100813

**AMA Style**

Djuma H, Bruggeman A, Camera C, Eliades M, Kostarelos K.
The Impact of a Check Dam on Groundwater Recharge and Sedimentation in an Ephemeral Stream. *Water*. 2017; 9(10):813.
https://doi.org/10.3390/w9100813

**Chicago/Turabian Style**

Djuma, Hakan, Adriana Bruggeman, Corrado Camera, Marinos Eliades, and Konstantinos Kostarelos.
2017. "The Impact of a Check Dam on Groundwater Recharge and Sedimentation in an Ephemeral Stream" *Water* 9, no. 10: 813.
https://doi.org/10.3390/w9100813