Catena 110 (2013) 146–154

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Evaluating the performance of reservoirs in semi-arid catchments ofTigray: Tradeoff between water harvesting and soil andwater conservation

D. Teka a,b,⁎, B. van Wesemael b, V. Vanacker b, J. Poesen c, V. Hallet d, G. Taye a,c, J. Deckers c, N. Haregeweyn e

a Department of Land Resource Management and Environmental Protection, Mekelle University, Ethiopiab Georges Lemaître Center for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, Belgiumc Department Earth and Environmental Sciences, Katholieke Universiteit Leuven, Belgiumd Department of Geology, Faculté Universitaire Notre Dame de la Paix, Namur, Belgiume Toronto University, Japan

⁎ Corresponding author at: Department of Land Resronmental Protection, Mekelle University, Ethiopia. Te

E-mail address: dannytk2@yahoo.com (D. Teka).

0341-8162/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.catena.2013.06.001

a b s t r a c t

a r t i c l e i n f o

Article history:Received 17 November 2011Received in revised form 13 May 2013Accepted 2 June 2013

Keywords:Micro damsIrrigation capacityRainfall-runoff responseHydrographNorthern Ethiopia

Micro dams play a vital role towards boosting crop production in Northern Ethiopia as they can be built bypooling local resources, are simple to design and can be constructed quite quickly. However, the reservoirs donot always fulfill their intended purpose, and hence less land is irrigated than initially foreseen. Here, we evalu-ated the performance of twomicro dams in Tigray by analyzing rainfall characteristics, surfacewater inflows andcombined evaporation and seepage losses. Given the scarcity of reliablemeteorological stations, transfer of rain-fall data from nearby stations to the dam sites was necessary. The rainfall magnitude and its annual distributionwere considered for the transfer of the rainfall data. The latter was estimated based on a precipitation concentra-tion index (PCI). Simple rainfall-runoff models, such as the curve number method proposed by the US Depart-ment of Agriculture Soil Conservation Service, or the rational method were used to predict the inflow to thereservoirs. Both methods slightly overestimated the monthly inflow for the catchment without soil and waterconservation measures (rational method: bias of 24% and curve number method: bias of 9%). In the catchmentwhere soil and water conservation measures were implemented, the inflow was overestimated by a factorthree. The high losses of water through seepage and evaporation substantially decreased the irrigation capacityby up to 33%. This case study illustrates that an integrated assessment of the hydrological response in the catch-ment is necessary for an adequate design of water harvesting systems.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

The development strategies of developing countries in dry areasoften depend on the possibility of maintaining, improving andexpanding irrigated agriculture (Siebert et al., 2006). World Bank(2006) noted that the dominant agricultural system in Ethiopia issmall-holder production of cereals under rainfed conditions. Duringthe 1984–85 drought, for example, GDP declined by 9.7% and agricul-ture output declined by 21% (World Bank, 2006). As a result, agricul-tural development through irrigation has been a priority for theEthiopian government, as irrigation during the dry season contributesto increasing food production and food security (Haregeweyn et al.,2006). The development of water storage facilities that could beused, among other things, to develop irrigation schemes are seen asa viable way of reducing Ethiopia's dependence on the inter-annualvariability of rainfall (World Bank, 2006).

ource Management and Envi-l.: +251 914707395.

rights reserved.

Soil moisture stress is the major constraint towards agriculturalproductivity in the dry areas, such as the region of Tigray in NorthernEthiopia. Significant achievements were made in the development ofagriculture through irrigation in Tigray. Runoff is being harvestedduring the rainy season and temporally stored behind earthen dams(Vanmaercke et al., 2010). In 1995, the regional government of Tigrayinitiated the ambitious plan to construct 500 micro dams within tenyearswith a sufficient capacity for irrigating50,000 ha inmoisture stressand drought prone areas. The majority of the micro dams wereconstructed in densely populated areas where agricultural activitiesare concentrated (Constable, 1985). By the end of 1997, 25 micro damswith a total irrigation capacity of 2500 ha were completed (Hagoset al., 1999) and 54 micro dams were completed in 2003 (Haregeweynet al., 2006; Fig. 1). Based on a survey of 54 micro dams, Haregeweynet al. (2006) indicated that themicro dams have a drainage area rangingfrom 0.36 to 51.91 km2, a dam height from 9 to 25 m, a storage capacityof 0.1 to 3.1 × 106 m3, a dead storage capacity ranging from 0.009 to0.64 × 106 m3 and an area to be irrigated from 8 to 250 ha.

Although the benefits of micro dam projects could be quite substan-tial in this drought-prone region, only 300 ha of agricultural land were

Fig. 1. Location of micro dams and monitored sites which are constructed under the Commission for Sustainable Agriculture and Environmental Rehabilitation in Tigray, CoSAERT.

147D. Teka et al. / Catena 110 (2013) 146–154

effectively irrigated in 1998 (Hagos et al., 1999). The construction ofthe micro dams did not proceed as planned because of practicalproblems such as reservoir siltation, excessive seepage, and a lackof appropriate dam sites (Haregeweyn et al., 2006; Yazew, 2005).The water harvesting system usingmicro dams is now generally con-sidered as a failure, and several dam sites are left behind. As an alter-native, other water harvesting schemes such as household ponds andshallow hand-dugwells have recently been promoted to supplementthe rainfed crop production in the region (Nyssen et al., 2004).

Baccari et al. (2008) noted that physical soil and water conservation(SWC) measures in the form of bench terraces built in the El Gouazinecatchment of Tunisia highly reduced runoff inflow to reservoirs whichgreatly limited the possibility of downstream irrigation. However,they focused on the degradation of the contour bench over time andnot on their impacts on water transfer to the reservoir. Lacombe et al.(2008) monitored the water level in a reservoir draining a catchmentof 1183 km2 for 17 years. They analyzed of possible sources for the ob-served runoff reduction such as climate change, land use/land coverchange, or losses to the aquifer. They concluded that SWC measures inthe catchment, such as contour ridges and hillside reservoirs, are themost likely cause for the reduction (41–50%) of the inflow to reservoirs.Similarly, Huang and Zhang (2004) concluded that check dams on smallgullies and terraces can significantly reduce surface runoff and signifi-cantly changed the hydrological regime of reservoirs in the loess pla-teau, China. However, these studies do not provide information on theeffects of the widespread application of stone bunds. The effect of mea-sures on the hillslopes have beendiscussed elsewhere, but their impactson the reservoirs and their potential for irrigation has not beenaddressed (Nyssen et al., 2010; Vancampenhout et al., 2006). Although,the aforementioned studies discuss the effects on inflow to the reser-voirs to some extent, they never addressed combined impact of SWCand the losses of water from the reservoir through seepage or evapora-tion on the performance of reservoirs for irrigation.

Previous studies have highlighted the inefficiency of the microdam projects, and attributed their failure to e.g. deep seepage in per-vious rock layers (Berhane, 2010; Yazew, 2005), and low inflow(Haregeweyn et al., 2006; Nyssen et al., 2004). However, there alsoexist several examples from successful micro dam projects in the re-gion. The overall objective of this study is to provide an assessment ofthe performance of two micro dam projects, based on new hydromete-orological data thatwere collected for twomicro dam sites in the Tigrayregion: Haiba and La'elay Wukiro.

2. Materials and methods

2.1. Site description

2.1.1. Geological settingIn the northern part of Ethiopia, the major lithological units are

metamorphic or basement rocks, sedimentary rocks, extrusive volcanicrocks, intrusive granitoids and subintrusive dolerites (Berhane, 2010).TheHaibamicro dam is located on theAntalo group (a sedimentary suc-cession of Jurassic age, composed of several layers of limestone, shale,marl, and minor amounts of gypsum and sandstone intercalations)with patches of Mekelle dolerites of Tertiary age (Bossellini et al.,1997). The upper most part of the catchment at the La'elay Wukiromicro dam is underlain by the basement (metamorphic basement in-truded by batholiths and stocks of Late Precambrian–Early Paleozoicage; Coltorti et al., 2007); while the lower most part is underlain byrocks of the Antalo group.

2.1.2. ClimateThe climate of the region is influenced by the seasonal oscillation of

the Inter Tropical Convergence Zone (ITCZ). Themovements of the ITCZare sensitive to variations in the Indian Ocean sea surface temperaturesand vary from year to year. Hence, the onset and duration of the rainy

Table 1Description of the micro dams (CoSAERT, Engineering feasibility study for Haiba andLa'elay Wukiro, unpublished report).

Haiba La'elay Wukiro

Mean annual rainfall (mm) 428 57075% dependable rainfall (mm) 362 314Design rainfall (100 yr return period) 91 95Design flood (m3 s−1) 134.5 39.2Reservoir capacity (Mm3) 3.11 0.847Seepage loss (m3 month−1) 762 1700Evaporation loss (m3 month−1) 35,566 17,401Catchment area (km2) 24.70 9.16Max. elevation in catchment (masl) 2789 2548Min. elevation in catchment (masl) 2242 2023Catchment slope (%) 9 21Dam height (m) 16.0 14.3Irrigated area (ha) 150 45Irrigation water requirement (Mm3) 2.35 0.759

148 D. Teka et al. / Catena 110 (2013) 146–154

seasons vary considerably between years, causing frequent droughts. Asa result, most of Ethiopia experiences one main wet season from midJune to mid September when the ITCZ is at its northernmost position.Parts of northern and central Ethiopia also have a secondary wet seasonof sporadic, and considerably less, rainfall from February to May.Rainfall distribution in the Tigray highlands is spatially highly vari-able and is largely dependent upon altitude and topographic aspect(Nyssen et al., 2005). In Tigray, rainfall varies from 1600 mm in thewestern highlands to less than 200 mm in the rain shadow areas,the eastern and northern part of the region along the escarpment(Fig. 1) and especially at selected stations in the areawhere themajorityof the dams are located it varies from 472 mm to 778 mm (Fig. 2).

2.1.3. Monitoring sitesMost of the small-scale irrigation schemes fed by micro dam reser-

voirs are located in the south-eastern part of Tigray (Fig. 1). All reser-voirs in the region are designed as gravity irrigation systems in whichirrigation is applied during the dry season and at the end of thewet sea-son. The dry season irrigation starts in January and ends in May; whilethewet season irrigation supplements the rainy season fromSeptemberto November. Cereals (wheat, maize, teff and barley), pulses (chick pea)and vegetables (cabbage, onion, tomato, pepper and potato) are themajor crops grown in the irrigation schemes.

This study was conducted at the Haiba and La'elay Wukiro microdams in Tigray, Northern Ethiopia (Fig. 1). The characteristics of thetwo micro dams are shown in Table 1. The major land use in the catch-ment at Haiba is cropland and at La'elay Wukiro is forest and cropland(Table 2). The two micro dams have comparable dam heights but thereservoir capacity and the irrigated area at Haiba is more than threetimes larger than at La'elay Wukiro. Both reservoirs still continue to ir-rigate during the dry season, though La'elay Wukiro suffers from fre-quent clogging up of the outlet pipe as a result of dam siltation.

2.2. Hydrological monitoring

Hydrological and meteorological data for the two dam sites werecollected by the Tigray Bureau of Water Resources (TBWR). At Haibathe hydrological and meteorological data was collected from 2001 to2004 and at La'elay Wukiro from 2001 to 2006 with some missingdata. The data series include precipitation data at 5 min interval (tip-ping bucket rain gauge), hourly air temperature and relative humidity(incomplete series), andwater levels in the two reservoirs at 15 min in-terval (Kedller Series-169 submersible pressure transducers, CampbellScientific, Alberta, Canada), The pressure transducers have an accuracyof 0.1% of the full scale. The error in the water balance as a result of the

0

50

100

150

200

250

Jan Feb Mar Apr May Jun

Mea

n m

on

thly

Rai

nfa

ll (m

m)

Hawzen Mekelle Hagereselam

Fig. 2. Mean monthly rainfall at stations in the neighborhood of the micro dams. The locat

accuracy of the water levels is estimated at 0.01 to 0.36 mm at Haibaand 0.10 to 0.20 mm at La'elay Wukiro.

2.3. Data analysis

2.3.1. Rainfall dataThe temporal and spatial variability of the rainfall at the two catch-

ments was analyzed in conjunction with the eight additional rainfallstations belonging to the National Meteorological Agency (Fig. 2). Theheterogeneity of rainfall depths is analyzed using the modified versionof Oliver's Precipitation Concentration Index (PCI; De Luís et al., 2001).According to Oliver (1980), PCI values below 10 indicate a uniformmonthly rainfall distribution throughout the year, whereas valuesfrom11 to 20point to seasonality in rainfall distribution. Values exceed-ing 20 correspond to climates with substantial monthly variability inrainfall depths (Eq. (1)).

PCI ¼ 100 � ∑12i¼1p

2i

∑121¼1pi

� �2 ð1Þ

where pi is the rainfall depth of the ith month.

2.3.2. Surface runoffRunoff during the months of July and August were selected for

analysis as these are the months with major runoff events that con-tribute to the inflow of the reservoirs. The runoff depths were ana-lyzed both on an event and monthly basis. First, the depth reading

Jul Aug Sep Oct Nov Dec

Adigudom Wukiro Adigrat Maichew

ion of the meteorological stations is given in Fig. 1 and the recording period in Table 3.

Table 2Runoff coefficient (C) and curve number (CN) for the catchments at La'elay Wukiro andHaiba.

Reservoir Land use Area(km2)

C CN

Haiba Cropland 14.33 N/A 79.5⁎⁎Range land 4.20 N/A 89.5⁎⁎Homestead 0.74 N/A 82.0 (design report)Miscellaneous 5.43 N/A 77.0 (design report)Weighted average 0.26 80.7

La'elay Wukiro Cropped land 3.08 0.40 79.9 (Without stone bunds)⁎⁎Forest land 4.50 0.35 66.6⁎⁎Range land 0.36 0.10 89.5 (design report)Homestead 0.52 0.30 82.0 (design report)Miscellaneous 0.70 0.35 77.0 (design report)Weighted average 0.33 74.5

⁎⁎ Source: Nyssen et al. (2010).

a

b

Fig. 3. Area–elevation and area–capacity curves and the Normal Pool Level (NPL) for(a) Haiba and (b) La'elay Wukiro micro dams (adopted from design report).

149D. Teka et al. / Catena 110 (2013) 146–154

of the pressure transducers was converted to corresponding waterheights. The water surface area and storage volumes corresponding towater height was estimated using the Area–Elevation–Capacity curve(A–E–C, Fig. 3) which was established by the TBWR during the dam de-sign. Then themass curve (a plot of the annualwater heights)was plot-ted; events that produce a rise in the reservoir level identified and therunoff depths computed with Eq. (2) for these events. Lastly, eventbased runoff depths are summed up to get the corresponding monthlyrunoff depths.

Q ¼ VA � 1000 ð2Þ

where Q is the runoff depth (mm), V is the runoff volume stored inthe reservoir during the rise of the mass curve (m3) and A is thearea of the catchment (km2). These measured runoffs were com-pared against the modeled runoff depth andmodel evaluation deter-mined by error index statistics using the Percent bias (PBIAS) andstandard regression (using the r2) as outlined by Moriasi et al.(2007). PBIAS is calculated from the observed (Qi

obs) and simulatedrunoff (Qi

sim; Eq. (3)):

PBIAS ¼∑n

i¼1 Qobsi −Qsim

i

� �� 100

∑ni¼1Q

obsi

24

35: ð3Þ

2.3.3. Soil Conservation Service-Curve Number method (SCS-CN)The design of the micro dams adopted the CN method (SCS, 1956,

1964, 1971, 1993, 2004) in conjunction with the triangular hydrographmethod to estimate the design flood, i.e. the peak runoff rate having areturn period of 100 year. The CN adopted for modeling the runoff isshown in Table 2.

Q ¼ P−Iað Þ2P−Ia þ Sð Þ if P > Ia ð4Þ

Q ¼ 0 if PbIa ð5Þ

Ia ¼ λS ð6Þ

CN ¼ 25;400254þ S

ð7Þ

where Q is the runoff depth (mm), P is the event rainfall (mm), Ia is theinitial abstraction [mm], λ the initial abstraction ratio and S (mm), is de-fined as the maximum possible difference between P and Q for thecatchment as P → ∞. The estimation of surface runoff using theSCS-CN model was based on CN adopted in the design report.

2.3.4. Rational methodThe design makes use of the rational formula (Chow et al., 1988)

for the estimation of the monthly and annual inflow volume to thereservoirs. The runoff coefficient, C, in the design report was adoptedfrom literature based on land use (Chow et al., 1988) and the range ofvalue for most of the micro dams in the region was 0.28–0.42. Therunoff coefficient adopted for modeling the runoff based on the ra-tional method is shown in Table 2. The hydrologic design proceduresadopted the runoff coefficients to estimate the monthly inflow to thereservoirs:

Q ¼ C⋅P ð8Þ

where Q is the monthly runoff [mm], C the runoff coefficient, P is thedependable monthly rainfall [mm].

18-J

ul28

-Jul

7-A

ug17

-Aug

27-A

ug6-

Sep

16-S

ep26

-Sep

6-O

ct16

-Oct

26-O

ct5-

Nov

15-N

ov25

-Nov

5-D

ec15

-Dec

25-D

ec4-

Jan

2001

300000

400000

500000

600000

700000

800000S

tora

ge (

m3 )

100

80

60

40

20

0

Rai

nfal

l (m

m)

Storage (m3)Ranifall(mm)Linear fit

A

B

C

a

18-J

ul28

-Jul

7-A

ug17

-Aug

27-A

ug6-

Sep

16-S

ep26

-Sep

6-O

ct16

-Oct

26-O

ct5-

Nov

15-N

ov25

-Nov

5-D

ec15

-Dec

25-D

ec4-

Jan

14-J

an24

-Jan

3-F

eb

2001 2002

1000000

2000000

3000000

4000000

5000000

Sto

rage

(m

3 )

100

80

60

40

20

0

Rai

nfal

l (m

m)

Storage (m3)Rainfall (mm)Linear fit

A

B

C

b

Fig. 4. Mass curve, hyetographs and fitted line (dotted line) to estimate combined evaporation and seepage loss (red) for (a) La'elay Wukiro and (b) Haiba.

150 D. Teka et al. / Catena 110 (2013) 146–154

2.3.5. Reservoir water balanceFrom themass curve, the losses (combined evaporation and seepage)

was analyzed and compared with the design conditions. To estimate theshare of evaporation from the combined losses, the evaporation from theopen water is estimated from the crop evapotranspiration according theHargreaves method (Allen et al., 2000; Eq. (9)):

ETo ¼ 0:0009384 � T max þ 17:8ð Þ � T max−T minð Þ0:5 � Ra ð9Þ

where ETo is the reference crop evapotranspiration (mm·day−1),Tmean is the mean daily air temperature (°C), Tmax is the maximumdaily air temperature (°C), Tmin is the minimum daily air tempera-ture (°C) and Ra is the incoming radiation (MJ m−2 day−1). We as-sume that the open water evaporation is 10% higher than the ETo(Dirk Raes, personal communication).

2.3.6. Time to concentrationThe time to concentration (Tc) for a catchment is a widely used pa-

rameter to estimate peak flow in hydrologic designs (Fang et al.,2008). It is the time taken for the water to travel from the most remotepart of the catchment to the outlet. Manymethods have been developedfor the estimation of Tc recognizing its importance in hydrologic designs.After comparing different methods of estimating Tc, Fang et al. (2008)concluded that the Kirpich and Haktanir–Sezen methods provide reli-able estimates of the mean variation of Tc. The hydrologic design of themicro damsused the Kirpich formula (Eq. (10)) to estimate the Tc (min):

Tc ¼ 0:0195 � L0:77 � S−0:385: ð10Þ

Table 3Precipitation concentration index (PCI) for selected stations.

Station Mean annualrainfall (mm)

PCI (%) Year of record

Mekelle 616.8 28.8 1959–1973; 1980–1985; 1992–2008Hagereselam 731.6 23.2 1973–1981; 1994–2008Hawzen 531.4 24.5 1971–1981; 1992–2008Wukiro 551.4 28.1 1974–1976; 1992–2008Adigudom 472.1 27.1 1975–1986; 1992–2008Adigrat 612.7 16.5 1970–1985; 1992–1999; 2001–2008Abyi-Adi 647.2 19.3 1966–1979; 1995–2008Maichew 778.0 18.2 1972–1987; 1992–2008

where L is the length of the main channel (m) and S is the slope of themain channel (m·m−1). The design Tc was compared with the actualTc thatwas estimated from the timedifference observed between the ef-fective rainfall hyetograph and the runoff hydrograph. Dingman (2002)noted that the Tc is the time interval between the end of the rainfall ex-cess and the hydrograph (event response). The hydrograph at 15 mininterval was established from the rise in the mass curve.

3. Results and discussion

3.1. Rainfall analysis

The hydrologic design of these ungauged catchments was basedon rainfall-runoff models developed in other parts of the world. Dur-ing the design phase, meteorological data from “nearby” stationswere used to estimate the rainfall depths at the dam sites on thebasis of similarity in annual rainfall depths. For most of the damsites that are located along the escarpment, rainfall data were de-rived from meteorological stations on the leeward (West) side andfew data were used from the windward side, as there is a strong alti-tudinal gradient to the east of the escarpment. This will result in anoverestimation of the rainfall on the catchments in the rain shadowof the escarpment (Fig. 1). As the rainfall pattern in the semi-aridcatchments is erratic both spatially and temporally (Zhou et al.,2005) care should be taken while transferring rainfall data toungauged catchments. To overcome these problems, for dam siteswhere there are limited rainfall records, it is recommended to alsocompare the PCI's rather than only compare the annual rainfalldepths. We analyzed the PCI (Eq. (1)) for several stations in orderto assess the feasibility of the transfer of rainfall data (Table 3). Ouranalysis showed two clusters of PCI: Adigrat, Aby-Adi and Maichewon one hand and Mekelle, Hagereselam, Hawzen, Wukiro andAdigudom on the other hand. For stations having similar annual rain-fall depths, a difference in PCI implies different runoff producing ca-pacities. In the design report we have observed that for dam siteshaving limited rainfall data, the designers in most cases transferredrainfall data from Mekelle because it has the longest rainfall record.However, the transfer would have been more reliable if they directlytransfer the rainfall data from other rainfall stations that have com-parable PCI to the dam site with limited records.

b

1:1 line

a

1:1 line

Fig. 5. Observed and predicted runoff producing events for (a) Haiba and (b) La'elay Wukiro.

151D. Teka et al. / Catena 110 (2013) 146–154

3.2. Inflow to the reservoir

3.2.1. Event responseThe analysis of the runoff into the reservoirs showed that, there

was no inflow problem for the Haiba reservoir, but there was a short-age of inflow into La'elay Wukiro, as the sharp peaks point to over-flow in Haiba at full capacity of c. 3 × 106 m3, while La'elay Wukirodid not reach its capacity of 0.8 × 106 m3 (Fig. 4). The surface runoffmodel evaluation at Haiba resulted in a PBIAS of −25% for the rationalmethod and 9% for the rational and SCS-CNmodels (n = 158; rational,r2 = 0.55 and SCS-CN, r2 = 0.67; Fig. 5a). At La'elayWukiro the surfacerunoff model evaluation resulted in PBIAS of −54.3% for the rationalmethod and −33.3 for the SCS-CN model (n = 101; rational, r2 =0.69 and SCS-CN, r2 = 0.56; Fig. 4b). Moriasi et al. (2007) noted thatPBIAS smaller than an absolute value of 10% indicates a very good per-formance rating.

3.2.2. Monthly responseFor the combined month of July and August the model evaluation

showed that the PBIAS at Haiba was −24% for the rational methodand −9% for the SCS-CN models (rational, r2 = 0.52 and SCS-CN,r2 = 0.67; Fig. 6a). The PBIAS at La'elayWukiro was−32.6% for the ra-tional method and−53.4% for the SCS-CNmethod (rational, r2 = 0.96and SCS-CN, r2 = 0.99; Fig. 6b). At Haiba the rational method showed a

a

1:1 line

b

Fig. 6. Observed and predicted monthly runoff at (a) Haiba and

trend to over predict the smaller and under predict the larger runoffevents unlike the SCS-CNmodel. At La'elayWukiro, the rational methodglobally over predicted (by a factor of 3.0).

The differences in the runoff responses between the two sites weredemonstrated using the response of themass curve for a specific rainfallevent (Fig. 7). For example, an event with 28.6 mm rainfall produced arise in themass curve that was translated to 5.4 mm of runoff inflow atHaiba, while 88.6 mm rainfall produced 6.3 mm of runoff inflow atLa'elay Wukiro.

Large differences exist in terms of SWC measures that wereimplemented at the two sites. There is little to no catchment rehabil-itation in the Haiba catchment, while a dense network of check damsand stone bunds was constructed in La'elay Wukiro. Previous re-search has documented the possible impact of check dams on therunoff response (Alemayehu et al., 2009; Haregeweyn et al., 2006;Hurni et al., 2005; Nyssen et al., 2004, 2007, 2010; Vanmaercke etal., 2010). Haregeweyn et al. (2006) noted that the inflow in somereservoirs was higher just after their construction, which also pointsto lower runoff amounts after the implementation of SWC measures.

In terms of the monthly runoff coefficient, the design runoff coef-ficients (0.26 for Haiba and 0.33 for La'elay Wukiro; Table 2) are inreasonable agreement with observed runoff coefficient (0.21; datanot shown) at Haiba, but are much higher than the observed one atLa'elay Wukiro (0.03). The runoff coefficients for most micro dams

1:1 line

(b) La'elay Wukiro based on rational and SCS-CN method.

a

b

Fig. 7. Identification of rise of the mass curve and the corresponding rainfall inputs for(a) Haiba and (b) La'elay Wukiro.

152 D. Teka et al. / Catena 110 (2013) 146–154

in the region are estimated to range between 0.28 and 0.42. Howev-er, Nyssen et al. (2010) noted that runoff coefficients in the Ethiopianhighlands show wide variation: 0.016 to 0.55. Furthermore, Nyssenet al. (2010) observed a drop in runoff coefficient from 0.08 to0.016 after land management practices had been implemented inthe May Zeg-Zeg catchment.

3.3. Evaporation and/or seepage from reservoirs

The area–capacity–elevation curves reveal that the capacities of thereservoirs in Haiba and La'elayWukiro are small and the surface area islarge, which causes high evaporation losses (Fig. 3). This can be seen inparticular from the shallow slope of the area–elevation curve at theLa'elay Wukiro reservoir. From the mass curves of the reservoirs(Fig. 4), three segments can be identified: segment AB reflects the risein water storage of the reservoir as a result of inflow; segment BC

shows the slow decline in the storage of the reservoir as a result ofwater loss. Finally, from point C onwards there is a sharp decline inthe storage of the reservoir as a result of combined water loss fromdry season irrigation, seepage and evaporation. The water loss duringsegment BC is mainly attributed to evaporation and seepage.

The combined seepage and evaporation losses have been esti-mated by fitting a linear least square regression line to the masscurve (dotted line) during the first months of the dry season (i.e.September–December) (r2 = 0.96 for Haiba and r2 = 0.85 for La'elayWukiro). This indicates that the actual average water loss encounteredduring this period was 6721 m3 day−1 at Haiba and 414 m3 day−1 atLa'elay Wukiro: equivalent to 4.4% and 19.4% of the reservoir capacity.According to the design reports, the combined evaporation and seepageloss were estimated at 1211 m3 day−1 at Haiba and 637 m3 day−1 atLa'elay Wukiro (Table 2). The evaporation loss during the end of thewet season as determined using Eq. (8) showed that the evaporationloss at Haiba was 4.2 mm day−1 (32.9 m3 day−1) and that at La'elaywukiro was 5.1 mm day−1 (11.4 m3 day−1). Design reports showedthat the average irrigation water requirement for most of the microdams in the region was between 10,000 to 15,000 m3 ha−1 in the dryseason. Hence, the observed water loss at Haiba during October–December caused a reduction of 50 ha in the area to be irrigated equiv-alent to one third of the total irrigated perimeter. Yazew (2005) notedthat up to 50% of water is lost through evaporation frommicro dam res-ervoirs in Tigray.

3.4. Time to concentration

The Tc for two inflow hydrographs corresponding to dry soils (i.e.having received less than 12.5 mm rainfall in the preceding 5 days)and wet soils (i.e. having receivedmore than 27.5 mm in the preceding5 days) (Figs. 8 and 9). These inflowhydrographs correspond to the riseof the reservoir as shown in Fig. 7. The Tc, was found to be 2:45 h atHaiba, and 2:18 h at La'elay Wukiro. While the estimated Tc for the de-sign based on the Kirpich formula (Eq. (10)) was 1:59 h at Haiba and1:02 h at La'elayWukiro. In both cases, the actual Tc has been under es-timated. Raghunath (2006) noted that peak runoff decreases due tohigher time to concentration and Bondelid et al. (1982) indicated thatas much as 75% of the total error in estimates of peak discharge couldresult from errors in the Tc estimation. The impact of Tc on the inflowhydrograph is such that a longer Tc results in a smaller peak discharge.The shorter Tc given in the design reports will result in over sizing thehydraulic structures such as the dam height and the width of thespillway.

3.5. Estimating inflow to reservoirs

The choice of rainfall-runoff modeling in semiarid regions can be on adiscrete event basis and a continuous basis e.g. averaged over a month(for e.g. Taylor et al., 1988). From our study, 73.3% at Haiba and 80.6% atLa'elay Wukiro of the rainfall depth did not produce inflow to the reser-voir, although these small rainfall events (5 mm) contribute to theplant available soil moisture (Sala and Lauenroth, 1982). Inflows to thereservoirs as can be seen from the mass curve (Fig. 7) correspond tofew events. Hence, to accurately estimate inflows to reservoirs, a modelthat takes into account event rainfall, among others, should be identified.Unfortunately, long term rainfall records (30 years) are often aggregatedto yield monthly values. Instead, daily values would bemore appropriateto estimate the inflow for reservoirs under construction.

4. Conclusions

The implementation of efficient water harvesting systems to achievefood self-sufficiency through boosting crop production is unquestionable.In Tigray the absence of hydromonitoring systems has led to a wide-spread adoption of empirical formulas and as a result the design of

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153D. Teka et al. / Catena 110 (2013) 146–154

micro dams has so far been based on a coarse meteorological networkand hydrological parameters derived from the literature. The transfer ofrainfall data from nearby stations should consider not only the rainfalldepth but also temporal pattern of rainfall e.g. the use of PCI. The analysisof the mass curve of the reservoirs shows that most of the runoff eventsare caused by the heavier storms, and hence models that underestimatethese storms will under estimate the design of the reservoir. It wasshown that in some cases the rational and SCS-CN method basedon runoff coefficients from the literature do not simulate the effectof conservation measures and as a result overestimate the inflowsto the reservoir (bias of 53%). In case such measures are lacking,the SCS-CN methods simulates the inflow to the reservoir correctly(bias of 9%) as opposed to the rational method (bias of 24%). Therefore,in design of newmicro damsmonthly inflow volumes can be estimatedusing SCS-CN method. In general, monitoring should continue to ad-dress the influence of soil and water conservation measures on runoffinto the reservoirs. Unforeseen high losses of stored water through acombination of evaporation and important seepage losses are themain reasons for the lower availability of irrigation water than

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expected. Due attention should also be given to reservoir shapes andsizes in order to maximize storage and reduce water surface area andhence evaporation losses. Furthermore, management options that willmaximize the use of the harvested water should be sought as largequantity of water has been lost in both micro dams before irrigationin the dry season started. This losswas equivalent to thewater requiredfor the irrigation of up to one third of the total irrigated perimeter. Thiscase-study illustrates that an integrated assessment of the hydrologicalresponse in the catchment is necessary for an adequate design of waterharvesting systems

Acknowledgements

The work is supported by Commission Universitaire pour leDéveloppement (CUD, Belgium) under the framework of MU-WAREPproject. We would like to acknowledge the Tigray Bureau of Water Re-source for their support in using the hydrometric data set and providingthe design reports of the micro dam reservoirs.

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re than 27.5 mm rainfall in the preceding 5 days) and (b) August 22, 2001 for dry soils

154 D. Teka et al. / Catena 110 (2013) 146–154

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