Estimating the actual evapotranspiration and deep ...

Agricultural Water Management 202 (2018) 42–56

Contents lists available at ScienceDirect

Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat

Estimating the actual evapotranspiration and deep percolation in irrigated soils of a tropical floodplain, northwest Ethiopia

T

⁎

Abebech Beyenea,b, , Wim Cornelisc, Niko E.C. Verhoestd, Seifu Tilahunb, Tena Alamirewe, Enyew Adgof, Jan De Puec, Jan Nyssena a

Gent University, Department of Geography, Gent, Belgium Bahir Dar University, Bahir Dar Institute of Technology, Bahir Dar, Ethiopia c Ghent University, Department of Soil Management, Gent, Belgium d Ghent University, Laboratory of Hydrology & Water Management, Gent, Belgium e Water and Land Resource Centre (WLRC), Addis Ababa, Ethiopia f Bahir Dar University, College of Agriculture and Environmental Sciences, Bahir Dar, Ethiopia b

A R T I C L E I N F O

A B S T R A C T

Keywords: Deep percolation Irrigation Soil water balance Hydraulic parameters Hydrus-1D Ethiopia

The deep percolation and actual evapotranspiration from flood irrigation in tropical floodplains were predicted using a numerical model, Hydrus-1D, and a bucket type water balance model. Field experiments were conducted on onion and maize crops grown from December 2015 to May 2016 in small irrigation schemes found in the Lake Tana floodplains of Ethiopia. Experimental fields were selected along a topographic transect to account for soil and groundwater variability. Irrigation volumes were measured using V-notches and irrigation depths (400–550 mm) were calculated, and daily groundwater levels were monitored manually from piezometers installed in the fields. The soil profiles were described at each field and physical properties (texture, FC, PWP, BD, and OM) were measured at each horizon which were used to derive model input parameters. Soil hydraulic properties (residual and saturated moisture content, saturated hydraulic conductivity, parameters related to: pore size distribution n, air entry α and pore connectivity l) were derived using KNN pedotransfer functions for tropical soils and fitted using Retention Curve Program for Unsaturated Soils, RETC. The seasonal actual evapotranspiration estimated by Hydrus and water balance models ranged from 320 to 360 mm for onion and from 400 to 470 mm for maize. The seasonal deep percolation estimated from both models was 12–41% of applied irrigation and with this flood irrigation management; the deep percolation is very high. Implementing precise irrigation and water saving practices that minimize deep percolation and unproductive excessive consumptive use are required to achieve the growing food demand with the available water. When less detailed information is available, the water balance model can be an alternative to predict deep percolation and actual evapotranspiration.

1. Introduction Irrigated agriculture accounts for 70% of the global freshwater withdrawals and 90% of consumptive water uses (Siebert et al., 2010). Due to growing food demand for the 7 billion world population, irrigated areas more than doubled from 1961 to 2009 (FAO, 2010). Irrigation is used to get out of poverty, alleviate the effects of drought and improve livelihoods (Belay and Bewket, 2013; Makurira et al., 2007). Improving irrigation efficiency for sustainable water management is the major objective of irrigated agriculture. This is especially important in Africa south of the Sahara where undernourishment is prevalent (Clarke

et al., 2017) and irrigated agriculture highly depends on the amount of nearby water sources (Belay and Bewket, 2013). Most smallholders in Africa, south of the Sahara use surface irrigation methods with greater risks of failure (Kay, 2001) and irrigation water management in the region is generally poor (Belay and Bewket, 2013). The majority of Ethiopian farmers use traditional, short closed end furrow irrigation (Geremew et al., 2008; Mintesinot et al., 2004) and flooding like other poorly resourced farmers in Africa. Data scarcity is one of the challenges of water management in the country in general and Lake Tana Basin (LTB) basin in particular, where only 3% of the potential irrigable area (which is 11%) is irrigated with surface water

⁎

Corresponding author at: Bahir Dar University, Bahir Dar Institute of Technology, Bahir Dar, Ethiopia. E-mail addresses: [email protected] (A. Beyene), [email protected] (W. Cornelis), [email protected] (N.E.C. Verhoest), [email protected] (S. Tilahun), [email protected] (T. Alamirew), [email protected] (E. Adgo), [email protected] (J. De Pue), [email protected] (J. Nyssen). https://doi.org/10.1016/j.agwat.2018.01.022 Received 13 June 2017; Received in revised form 25 December 2017; Accepted 25 January 2018 Available online 22 February 2018 0378-3774/ © 2018 Elsevier B.V. All rights reserved.


A. Beyene et al.

Fig. 1. Geographical location of the study area and irrigated fields in the floodplains. F1–F3 represent fields 1–3 and O and M indicate onion and maize crops. Data Source: ASTER GDEM, MoWRs and field survey; Geographic Coordinate System: GCS_WGS_1984 and Datum: D_WGS_1984.

agriculture as an integral part of the systems, was not investigated well, despite its role in water resources as well as in rural livelihoods. Floodplains are dynamic systems due to frequent erosion and sedimentation, flood inundations and complex groundwater–surface water interactions (Dessie et al., 2014a; Tockner et al., 2008). Floodplains have been exposed to intensive land use and management changes

(Worqlul et al., 2015). In LTB resources scarcity is critical to agricultural productivity of small farms (Clarke et al., 2017) and its land and water resources are not properly utilized (Derib et al., 2010; Setegn et al., 2008). Although few researches were conducted in LTB to understand rainfall and runoff processes (Collick et al., 2009; Dessie et al., 2014b,c; Kebede et al., 2011; Setegn et al., 2008), the irrigated

43


A. Beyene et al.

2. Materials and methods

(Krause et al., 2007). Furthermore, the new water resource management approach in Ethiopia is giving priories to protect floodplains (Kloos and Legesse, 2016). The LTB floodplains are selected as national growth corridor where irrigation will be intensified (McCartney et al., 2010). Therefore, besides government priorities, the complex exchanges in groundwater-surface water and its economic benefits to rural livelihoods made irrigated floodplains of LTB vital areas of research. To improve irrigation systems efficiency, the major hydrological processes in the system are needed to be identified and quantified. Understanding the important components of the water balance such as actual evapotranspiration and deep percolation under specific irrigation practice is crucial to develop irrigation management strategies. Under excessive irrigation applications, soil evaporation will be higher and deep percolation occurs if infiltrated irrigation exceeds the soil storage capacity (Bethune et al., 2008). Furthermore, excessive non-productive transpiration in poor irrigation management may be a substantial loss. In data scarce areas such as LTB and in situations where acquisition of field data is costly and time consuming, models are an alternative ways to manage systems once they are validated (Arnold and Allen, 1996). Water balance simulation using process-based models helps to analyse how efficient the water is used (Soldevilla-Martinez et al., 2014). On the other hand, process-based complex models that accurately simulate the water balance require large input data and parameters (Loosvelt et al., 2011) which needs intensive field and laboratory work. Determination of all required soil parameters is difficult in developing countries because of lack of equipment, skills and budget. Simple models can be an alternative tool for water management in data scarce regions and provide comparable results as those of processes based models. The direct application of physically based models at field level is restricted by extensive experimental information and several assumptions for their parameters (Rao, 1987). This paper examines evapotranspiration and deep percolation to understand the major processes that influence the hydrological cycle and addresses the challenges of traditional flood irrigation management through combined use of water balance model and the Hydrus-1D model (Šimůnek et al., 2005, 2008) in the LTB. The Hydrus-1D is based on the Richards equation, which is the standard model for variably saturated soil water balance simulations (Diamantopoulos et al., 2012; Šimůnek et al., 2003; Vereecken et al., 2016) and application of this model in tropical floodplains and comparison with simple models at field scale is substantial. Comparison of a conceptual deep percolation model and a Hydrus-1D model based on lysimetric experiments on different soils of irrigated pasture was conducted by Bethune et al. (2008) in Australia. However, ground water tables that fluctuate with time in response to irrigation/rainfall in the field conditions was not addressed in lysimeters (Bethune et al., 2008) and the study was done for temperate soils at an experimental station based on optimum irrigation management. More realistic results were found by incorporating groundwater in unsaturated zone modelling using Hydrs-1D in shallow water table regions (Kollet and Maxwell, 2008; Maxwell and Miller, 2005; Soylu et al., 2011). Modelling at field experiments with different soil types, groundwater depths fluctuations, crop types and irrigation amounts in tropical areas are important. Simulation of soil water balance using Hydrus-1D requires characterization of both top and sub-soils, groundwater monitoring and measurements of other time variable boundary conditions (Rezaei et al., 2016), which require money, skill and equipment. As the main interests in this study are inflow and outflow of water from the soil column, the internal distribution of soil-water content is not examined. This study sought (a) to estimate the deep percolation and actual evapotranspiration from farmers’ irrigated fields in tropical floodplain soils; and (b) to compare simple water balance model with the Hydrus1D for use in irrigation water use analysis in the study area.

2.1. Description of the study area LTB comprises the largest Ethiopian lake and extends from 36.7° to 38.2° E to 10.9° to 12.8° N (Fig. 1). It contains 15,321 km2 including 3064 km2 the lake area (McCartney et al., 2010). The mean annual rainfall is 1345 mm mostly occurring during the rainy season (kiremt) from June to September (Engida, 2010; Wossenie, 2015). The other months are dry and crop production is then only possible through irrigation. Temperature shows high diurnal but small seasonal variations with an annual average of 20 °C (Kebede et al., 2011). The basin consists of Tertiary and Quaternary igneous and Quaternary sedimentary rocks that include among others the floodplains (Dessie et al., 2014c). The major soil types are Luvisols, Fluvisols, Leptosols and Vertisols. About 90% of LTB has an elevation ranging from 1780 to 2500 m a.s.l., with the downstream mostly flat or gently undulating and makes a large floodplain around Lake Tana (Wossenie, 2015). The floodplains are found bordering the East and North part of the lake and are intensively cultivated, both under rain fed during kiremt and irrigation during the dry season. Irrigation has been practised in the area for a long time and the widely used irrigation methods are flooding and traditional short furrows. 2.2. Field experiment and data collection Two experimental sites called Bebeks and Shina were selected based on the soil physical characteristics and availability of information in the LTB floodplains for this. To get the soil variation, which is the most important parameter for water balance modelling, the two schemes were systematically selected after determining the soil texture of all schemes found in the floodplain of LTB (Table 1). Generally, the floodplain is uniformly clayey but we selected the two schemes with highest sand (Shina) and lowest sand (Bebeks) to interpolate the model results as far as the whole floodplain soil characters lay between these schemes. The Bebeks irrigation scheme is using a stone masonry diversion weir and is situated in the eastern tip of the floodplains. The Shina irrigation scheme uses water from an earthen micro-dam and is located near Lake Tana. The experimental sites and the other irrigated fields in the floodplains were surveyed and mapped using GPS and GIS (Fig. 1). The average slope of the experimental sites was 1–3%. For experimentation (from December 2015 to May 2016), three fields were selected per crop type in a transect perpendicular to the rivers (source of irrigation) to address soil and groundwater gradient variations. Hence, three fields were selected at Bebeks where onion (Allium cepa L.) was dominantly produced and six fields were selected in Shina where onion and maize (Zea mays L.), were cultivated during the experimental period using traditional flood irrigation. 2.2.1. Irrigation volumes and water table monitoring Irrigation application by farmers and the excess irrigation volume Table 1 Irrigation schemes in the floodplains of Lake Tana Basin. Scheme Name

Bebeks Guanta Shina Lomi dur Tankua Chan

44

Geographic location

Average soil texture

Latitude (°N)

Longitude (°E)

%sand

11.78 11.85 11.80 11.82 11.85 11.76

37.66 37.65 37.51 37.657 37.66 37.71

18 22 29 21 25 23

± ± ± ± ± ±

%clay 3 2 1 3 2 2

44 43 36 45 41 43

± ± ± ± ± ±

%silt 4 2 3 1 2 3

38 35 35 34 34 34

± ± ± ± ± ±

2 2 3 2 1 1


A. Beyene et al.

that might leave the field as surface runoff were measured using 60° Vnotches that were installed at the inlet from the irrigation canal and the outlet at the lower side of each field. The discharge through the Vnotches was determined by the V-notch formula (Shen, 1981):

Q= Cd

φ 8 2g tan h2.5 15 2

(1) 3 −1

where, Q is discharge [m s ] C is coefficient of discharge, g is acceleration due to gravity [m s−2], φ is angle between the sides of the notch [°] (in this case φ = 60°) and h is the surface of water level measured with respect to the vertex of the notch (m). The discharge coefficient Cd for V-notch angles between 20° and 120° (Greve, 1932) is given as:

φ −0.004 −0.03 Cd = 0.585 ⎛tan ⎞ h 2⎠ ⎝

(2) Fig. 2. Water balance components showing the various processes in irrigated fields.

3

The irrigation volume [m ] was determined by multiplying the discharge with irrigation duration [s] and depth [mm] was determined by dividing the volume by field size [m2], assuming evenly distributed application over the field. The depth of irrigation applied by farmers was mostly variable (non-steady) due to traditional water allocation methods that fluctuate even during one irrigation event. Consequently, both depth and duration of flow at every change of flow was recorded. Then, the V-notch equation above (Eqs. (1) and (2)) was applied for each depth (h) and time (t) to compute volume. Finally, each volume obtained was summed up to get the total volume of applied water per irrigation event. Farmers controlled all farm activities including the amount and timing of irrigation. Researchers had no role on the farm water management, except for measurement and data collection. The maximum root depth of onion is 60 cm (Allen et al., 1998) and effective root zone (the depth within which most crop roots are concentrated) for maize is estimated about 50–100 cm and likely 50% of the roots are found in the upper 20 cm (Fan et al., 2016). Similarly, the root length density of maize decreases with soil depth and almost 50% of the total root length occurs in the top 10 cm, another 30% within the next 20 cm, and the remaining 20% below 30 cm (Yu et al., 2007). Due to this fact and limitation of moisture sensor at deeper root zones, we focused on the most important portion of the root zone. the soil-water content at the top 30 and 60 cm depths were measured just before and 24 h after irrigation event using gravimetric method. Two manual rain gauges (one for each experimental site) were installed but no rainfall was observed during the experimentation period. The groundwater level was manually measured daily (at 9:00 am) from piezometers installed in the experimental fields, using stick and meter. The piezometers were prepared using 50 mm diameter PVC pipes, screened over a length of 1 m from the bottom and the screens were covered with a thin parachute cloth to prevent the entry of fine soil. The piezometers were inserted 1 m below the water table before the start of the irrigation season. The piezometer screens were packed with sand to prevent clogging with fine soil particles and the well points at the bottom were perforated to prevent false readings during nonrecharge periods. Five piezometers were installed diagonally and similarly, soil moisture was sampled at five points at each field to average the water table and soil moisture variations as result of infiltration profile variations due to differences in opportunity time. Surface water sources (streams and rivers) with electrical conductivity EC values of 0.05 dS m−1 and 0.09 dS m−1 for Bebeks and Shina, respectively, as measured with electrical conductivity meter (Rhoades, 1993; Richard, 1968) was used for irrigation. The average electrical conductivity of the groundwater for Bebeks and Shina were 0.053 ± 0.018 dS m−1 and 0.16 ± 0.07 dS m−1, respectively. With salinity levels below 0.7 dS m−1, the water is non-saline and suitable for irrigation (Ayers and Westcot, 1985; Rashid et al., 1993).

permanent wilting point, organic matter and particle density were determined by taking soil samples at each horizon. Texture was determined by hydrometer analysis. Field capacity and permanent wilting point were determined at pressures 33 kPa and 1500 kPa using pressure-plate extraction (Botula et al., 2012). Organic matter was determined by the Walkley-Black method (Walkley and Black, 1934) using the potassium dichromate dilution technique. Bulk density was determined from undisturbed soil samples taken at each horizon (using 49 mm diameter and 51 mm length core samplers). The soil hydraulic conductivity was determined using pedotransfer functions from texture, bulk density and organic matter according to (Wösten, 1997). 2.3. The water balance model A simple bucket type water balance model was developed to estimate the actual evapotranspiration and deep percolation components of the water balance in irrigated fields. Major inputs into the soil include irrigation, precipitation and capillary rise, whereas the outputs are evapotranspiration, surface runoff and deep percolation (Fig. 2). The amount of water added to and leaving out of the soil reservoir by lateral subsurface flow is demonstrated using Darcy’s law:

V = ks

Δh Δl

(3) −1

Δh Δl

is the slope (sub surface where, V is the lateral velocity [m day ], slope between the field boundaries) and ks is the saturated hydraulic conductivity [m day−1]. The average slope of the water table between the field boundaries is 0.02 based on the water table depths of the different fields and the distance between fields. Using values of ks (Table 2) and this slope, the lateral groundwater flow ranges from 0.006 to 0.019 m day−1. Hence, the contribution of this lateral subsurface flow is minimum as compared to vertical water flow, which is governed by gravity. Similar result was reported by Enku et al. (2016) for the floodplains. In general, the water balance equation can be represented as:

ΔS = P + I + C − R − Dp − AET

(4)

where, ΔS is the change in root zone soil water storage, P is precipitation, I is irrigation, Dp is deep percolation, R is surface runoff, C is capillary rise and AET is actual evapotranspiration. All components are expressed in mm and the water balance computation was conducted on a daily basis at each irrigation event during the growing period of the crops under consideration. For simplicity, capillary flow and deep percolation were merged as:

D = Dp − C

2.2.2. Field and laboratory procedures of soil characterization The soil physical properties such as texture, field capacity,

(5)

D, the net deep percolation, is negative if capillary rise dominates and it is positive if deep percolation dominates. There was no rainfall (P) 45


A. Beyene et al.

Table 2 The soil physical properties of each soil horizon. silt (%)

clay (%)

ρb (g cm−3)

OM (%)

FC (%)

saturated θsat (%)

PWP (%)

Ks (cm day−1)

I f (cm day−1)

Bebeks- onion field 1 0–13 29 13–113 27 > 113 59

40.1 44.8 24.8

30.9 28.2 16.2

1.24 1.2 1.13

2.3 1.11 0.8

34.0 40.15 31.14

48.5 49 45

21.2 25.1 22.6

47 40 102

24


35.8 38.8 32.8

47.2 22.2 30.2

1.12 1.00 1.26

2.14 0.90 0.90

37.61 36.81 44.70

53 48 45.5

22.8 21.15 22.8

63 44 37

20


35.2 36.8 28.8

46.8 32.2 30.2

1.20 1.21 1.25

1.70 1.209 0.66

39.75 38.75 40.22

50.7 47.3 46.5

24.03 20.89 21.82

57 46 84

30

Shina- onion field 1 0–17 33 17–47 35 > 47 33

31 31 33

36 34 34

1.39 1.35 1.31

1.54 1.30 1.19

34 35 34

45 45 43.8

22 21.9 18

60.6 55.2 90.3

40

Shina- onion field 2 0–20 33 20–75 29 > 75 35

35 33 32

32 38 33

1.21 1.20 1.27

1.86 1.04 0.74

39 38 36.8

49.5 51 48.5

21.5 22.3 22.5

62.8 31.2 79

32

Shina -onion Field 3 0–30 30 30–60 28

34 34

36 38

1.19 1.21

2.41 2.16

37.05 37.02

53 48

20.9 20.78

37.7 32

28

Shina-maize field 1 0–30 28 30–60 18

40 34

32 48

1.23 1.25

3.28 2.14

36.19 40.42

49.12 55.8

19.87 22.26

35.8 26.9

40

Shina-maize field 2 0–30 30 30–60 24

32 31

38 45

1.22 1.27

3.68 2.71

32.3 36.14

47.5 50.5

21.75 23.78

72.1 24.7

36

Shina- maize field 3 0–20 28 20–65 24 > 65 31

26 24 33

46 52 36

1.27 1.30 1.30

1.65 1.06 0.73

36.5 37 35.4

50.6 50.1 50.0

24 26 22.5

27.4 26.2 28.3

18

Depth (cm)

sand (%)

et al., 1998). Potential crop evapotranspiration is the maximum evapotranspiration of a given crop for the given weather conditions and at each stage of development it is expressed as (Allen et al., 1998; Rao, 1987):

observed during the irrigation (dry) season in the study area so that runoff generation did not occur. Thus, the runoff component in Eq. (4) corresponds to the excess amount of irrigation removed from the field as surface runoff. Finally, the field water balance Eq. (4) can be reduced to:

ΔS = I − D − R − AET

(7)

ETc = ETo*kc

(6)

Where, kc is the crop factor [−] with no water stress conditions (Allen et al., 1998) and CROPWAT 8.0 (FAO, 2009; Smith, 1992; Swennenhuis, 2009) was used for ETo and ETc calculations. The crop factors and growth stages for onion and maize were obtained from Allen et al. (1998). The ΔS for the top 60 cm soil was determined by subtracting soil water content measured before irrigation and from soil water content measured 24 h after the each irrigation event at 30 and 60 cm depths and weighting the values.

2.3.1. Water balance model inputs Measured irrigation depth (details in Section 2.2) during the experimental period were used as an input for the model. The region (study area) is suffering from sparse and unevenly distributed network of meteorological stations (Worqlul et al., 2017; Worqlul et al., 2018). There are three nearby meteorological stations (indicated in Fig. 1) to experimental sites, Bahir Dar station (about 50 km for both schemes), Hamusit (7 km from Shina) and Wereta (5 km from Bebeks). All the three stations are found on the same agro ecological zone; mid altitude (average 1800 m a.s.l.) and generally are flat areas. According to the World Meteorological Organization, the minimum station network density for tropical regions are 600–900 km2 per station for flat areas (Worqlul et al., 2017). Hence, the distance of stations from the experimental sites is fair to accept and several researchers used Bahir Dar station to calculate ET P-Monteith equation in the study area (Dessie et al., 2014a,c; Kebede et al., 2011). Thirty six years (from 1980 to 2016) daily values of temperature (maximum and minimum), relative humidity, sunshine hours and wind speed data of Bahir Dar, Woreta and Hamusit stations were obtained from Bahir Dar Branch office of National Meteorological Agency to estimate the daily reference crop evapotranspiration ET0 by the FAO Penman-Monteith method (Allen

2.3.2. Water balance model outputs Estimating the actual evapotranspiration (AET) is a critical element in water balance modelling to estimate deep percolation (recharge) (Jiménez-Martínez et al., 2009). AET will reach the maximum potential crop evapotranspiration when the soil moisture exceeds field capacity whereas the AET will be modelled for other moisture values as follows (Steenhuis and Van der Molen, 1986): θ.ET

⎧ AET = FC c : θ < FC ⎨ AET = ETc : θ ≥ FC ⎩

(8) −3

where, θ is the volumetric soil-water content [cm cm ], FC is the field capacity of the soil in volumetric basis [cm3 cm−3]. The measured FC values at each horizon (Section 2.2) was weighted and used for the top 60 cm soil for this calculation. 3

46


A. Beyene et al.

Deep percolation (water leaving below the root zone) is found by rearranging Eq. (6) and solving for D as it is the only unknown parameter in the water balance equation; of course, when D is greater than zero, AET = ETc.

∂θ ∂ ⎡ ∂h (θ) ⎞ ⎤ = k (h ) ⎜⎛ ⎟ + 1 ⎥ − S (h) ∂t ∂z ⎢ ⎝ ∂Z ⎠ ⎣ ⎦

(9)

where, h is the water pressure head [cm], θ is the volumetric soil-water content [cm3 cm−3], t is time [day], z is the vertical coordinate [cm] (positive upward and its origin is the soil surface). S is the sink term [cm3 cm−3 day−1] representing the plant water uptake by roots (transpiration) and k(h) is the unsaturated hydraulic conductivity function [cm day−1]. The sink term is determined in terms of the crop potential uptake rate and a water stress factor (Feddes et al., 1978). The unsaturated soil hydraulic properties; θ(h) and k(h) were derived using the van Genuchten-Mualem functional relationships (Mualem, 1976; Van Genuchten, 1980).

2.4. Hydrus model description The Hydrus-1D model (version 4.14) which is based on the Richards equation and simulates one-dimensional water movement in multilayered variably saturated soil was applied to predict deep percolation and actual evapotranspiration (Šimůnek et al., 2005, 2008). Heterogeneous conceptualizations of the soil layers were included for each field by specifying the soil profile and investigating the soil physical properties of each horizon. Since water movement between soil surface and water table (unsaturated zone) is dominantly vertical (one – dimensional) (Hillel, 2004; Meiwirth and Mermoud, 2004; Van Dam and Malik, 2003), Hydrus-1D can be used to study the soil-water dynamics (Van Dam et al., 2004). Several researchers such as Crevoisier et al. (2008), Forkutsa et al. (2009), González et al. (2015), Li et al. (2014) and Tafteh and Sepaskhah (2012) preferred Hydrus-1D and successfully applied it for unsaturated zone soil water dynamics. The modified Richards equation for variably saturated one-dimensional water flow in soil assuming the role of the air phase and water flow due to heat gradient being negligible is:

2.4.1. The Hydrus model input hydraulic parameter optimization Points on the soil water retention curves for each soil horizon were estimated with a k-Nearest Neighbour pedotransfer functions of tropical soils (Botula et al., 2013). The function uses measured particle size distribution, bulk density, organic carbon, soil-water content at field capacity and at wilting point data. The RETC parameter optimization program (Van Genuchten et al., 1991) was then used to fit the predicted data and to derive the independent unsaturated soil hydraulic conductivity parameters (θs, θr, α, n, Ks). RETC uses a nonlinear leastsquares optimization approach (Levenberg-Marquardt) to estimate the unknown model parameters from observed retention data (Van

Table 3 Soil hydraulic parameters used for Hydrus-1D simulation. Depth (cm)

θS (cm3 cm−3)

θr (cm3 cm−3)

n (cm−1)

α

R2

SSQ

Ks (cm day−1)

Bebeks onion field 1 0–13 13–113 113–350

0.487 0.494 0.454

0.20 0.21 0.19

1.15 1.11 1.41

0.088 0.047 0.0373

0.99 0.98 0.98

0.00034 0.00082 0.0011

68 73 97


0.537 0.486 0.463

0.23 0.20 0.22

1.202 1.22 1.17

0.0662 0.0318 0.0475

0.99 0.98 0.96

0.00062 0.00105 0.00204

46.7 58.3 30


0.505 0.471 0.465

0.248 0.211 0.214

1.10 1.134 1.14

0.0741 0.0261 0.016

0.99 0.98 0.97

0.00057 0.00086 0.00129

44.9 48.6 53.5

Shina onion field 1 0–30 30–60 60–300

0.496 0.550 0.504

0.227 0.237 0.228

1.11 1.09 1.107

0.0497 0.053 0.09

0.97 0.99 0.97

0.0013 0.00036 0.0018

78 31 33.9

Shina onion field 2 0–20 20–75 75–240

0.490 0.509 0.484

0.218 0.227 0.222

1.13 1.11 1.109

0.0383 0.115 0.0847

0.98 0.99 0.98

0.00089 0.00062 0.00073

23.8 22.5 37.9

Shina onion field 3 0–30 30–60 60–200

0.477 0.485 0.490

0.248 0.267 0.269

1.099 1.19 1.3

0.046 0.0694 0.0867

0.98 0.94 0.98

0.00063 0.00238 0.00087

20.9 17.9 38

Shina maize field 1 0–17 17–100 100–290

0.450 0.449 0.437

0.22 0.216 0.18

1.09 1.10 1.13

0.185 0.087 0.0594

0.99 0.98 0.98

0.00013 0.00056 0.00094

57 28 67.9

Shina maize field 2 0–30 30–60 60–250

0.477 0.501 0.509

0.237 0.262 0.237

1.18 1.32 1.11

0.034 0.0626 0.22

0.98 0.98 0.97

0.00079 0.00109 0.00148

43.2 88 69

Shina maize field 3 0–20 20–65 65–200

0.506 0.502 0.500

0.25 0.26 0.22

1.09 1.09 1.10

0.111 0.082 0.20

0.98 0.98 0.99

0.00083 0.00068 0.0003

43 18.2 50.3

47


A. Beyene et al.

temperature, humidity, sunshine hours and wind speed that are required to calculate potential ET by Penman-Monteith method (Allen et al., 1998) which were obtained from the nearby meteorological stations (see 2.3.1) were given to the Hydrus model. The latitude (to define the extra-terrestrial radiation) and altitude were also defined. Measured irrigation (event based and putting zero for non-irrigation dates because there was no rainfall) and daily records of groundwater level were given as time variable boundary conditions. The soil material was distributed based on the observed depths of each horizon. Three soil layers were considered in each field for simulations with the bottom layer extended to the depth of the water table before the onset of irrigation. The profile was discretized by assigning 1 cm equally spaced nodes for the whole profile depth and no hysteresis in water retention was considered. Observation nodes were placed at 30 cm and 60 cm depths to track moisture fluxes and compare with the observations of soil-water content, which were measured at 30 cm and 60 cm depths.

Genuchten et al., 1991). The value for l was set at 0.5, a default value for many soils. Sensitivity analysis was performed to find out the most important hydraulic parameters, on which the model results are most sensitive in the specific situations. Local sensitivity analysis (LSA) using a one-at-atime (OAT) approach was used to understand the effect of each parameter to the model output since this approach allows a clear identification of single parameter effects. We ran simulations of soil water balance after the Van Genuchten (1980) hydraulic property model was optimized. 2.4.2. Initial and boundary conditions and other input data to the Hydrus model Since the soil surface is subjected to the atmospheric boundary condition (BC), the upper BC was set equal to the atmospheric BC with Surface runoff whereas the variable pressure head was selected as the lower BC (daily records of groundwater level was input). Soil-water content during planting was used to define the initial conditions. The root water uptake was simulated using the Feddes root water uptake reduction model with no solute stress (the EC of soil water in the study area is less than 0.7 dS m−1). Crop information was derived from observations of crop height (measured crop height at each stage to obtain LAI, Leaf Area Index) and growth stages and values of rooting depth were taken from literature. The critical water pressure head values were taken from Wesseling et al. (1991), Feddes and Raats (2004) and Taylor and Ashcroft (1972), considering a high evaporative demand. The crop interception and throughfall contributions were ignored because flood irrigation was directly applied to the soil surface. Daily meteorological data including, minimum and maximum air

Ch a n g e in c u m. d e e p p e r c o la tio n (%)

2.4.3. Simulation results of the Hydrus model From Hydrus model simulation results, actual and potential transpiration, evaporation, soil-water content and bottom flux were the most important components of interest. A negative bottom flux indicates loss from the soil column in the form of deep percolation and positive values indicate gain to the system (capillary rise). Here, the daily and seasonal bottom flux, actual transpiration and soil evaporation were quantified and investigated to understand the soil water balance of the fields under different soil, crop, irrigation and water table depth conditions.

(b)

150 100 50 0 -30

-20

-10

0

10

20

30

40

-50 -100 -150

Change in

(a)

100 80 60 40 20 0 -40

-30

-20

-10

-20

0

10

20

30

-40 -60 -80 -100

Change in k s (%)

s(%)

100

(c)

Bebeks onion F1

80

Bebeks onion F2 60

Bebeks onion F3 Shina onion F1

40

Shina onion F2 Shina onion F3

20

Shina maize F1 0 0

5

10

15

20

25

30

35

Shina maize F2 Shina maize F3

Fig. 3. Sensitivity analysis of saturated hydraulic conductivity ks (a), saturated soil-water content θs (b) and pore size distribution parameter n (c).

48

40


A. Beyene et al.

fields and the seasonal values of D and AET estimated by the Hydrus model and by the water balance model are comparable (Table 7).

2.5. The Hydrus model validation The irrigation application and simulated soil-water content (daily) at 30 cm and 60 cm depths (at observation nodes) and the soil-water content taken just before and 24 h after each irrigation event at 30 cm and 60 cm depths were plotted and compared for validation. One of the common methods of estimating ETo is by converting the class-A pan evaporation into ETo by using a pan coefficient (Doorenbos and Pruitt, 1977; Grismer et al., 2002; Sentelhas and Folegatti, 2003). Hence, the Hydrus model was also validated by comparing the daily simulated potential ET by Hydrus model, calculated ETo (Eq. (11)) using class A pan evaporation data and also daily ETo simulated by the Cropwat 8.0 (Allen et al., 1998). Daily pan evaporation data was collected from Bahir Dar metrology station. The pan coefficient (Kp) was calculated to the specific climatic conditions from daily records of wind speed (U) and relative humidity (H) using the Cuenca (1989) equation:

3.3. Evapotranspiration The reference crop evapotranspiration (ETo) and the potential crop evapotranspiration (ETc) values are presented in Tables 4 and 5. Because of growth stage variations due to different planting dates, the potential ETc values in the same experimental site are different for the same crop, at different fields (Table 5). The computation of evapotranspiration as well as partitioning of evapotranspiration in to crop transpiration (crop water uptake by roots that is the sink term in Richards’s equation) and soil evaporation was done by Hydrus model (Fig. 5). The seasonal actual values of evapotranspiration for the crops growing periods simulated by the Hydrus and the water balance models are also listed (Table 7).

Kp = 0.475 − 2.4 × 10−4U2 + 5.16 × 10−3RH + 1.18 × 10−3F 3.4. Deep percolation

− 1.6 × 10−5RH 2 − 1.01 × 10−6F 2 − 8.0 × 10−9RH 2U2 (10)

− 1.0 × 10−8RH 2F

Field observations showed that the groundwater table depth varied from 2 m to 3.5 m, with a decreasing trend from fields 1 to 3 for both

−1

where, U2 is mean daily wind speed at 2 m height [km day ], RH is mean daily relative humidity [%] and F is upwind fetch of low growing vegetation from the pan which was commonly taken about 20 m. After determining Kp, the ETo was calculated as:

ETo = Epan × Kp

0.55

Bebeks onion F1

r = 0.735 at 30 cm ME=0.08 RMSE=0.091

0.50

(11)

8

0.45

3. Results 3.1. Soil hydraulic parameters

0.40

The soil hydraulic parameters required to simulate the Hydrus model are shown in Table 3. The most important hydraulic parameters on which the Hydrus model is most sensitive were saturated soil-water content θs, the parameter n and the saturated hydraulic conductivity, ks (Fig. 3). Since the values of n were small (Table 3), sensitivity analysis was performed starting from n = 1 (the minimum value in Van Genuchten), thus reduction in percentages of n were not considered (Fig. 3c).

0.35

10

6 4 2 0

0.30 0

20

40

60

80

100

120

Days from planting to harvest 0.55

r = 0.747 at 30 cm ME=0.071 RMSE=0.086

Shina onion F2

0.50

10 8

3.2. Model validation 0.45

The observed soil-water content before and 24 h after irrigation events and daily simulated soil-water content by the Hydrus model at 30 cm and 60 cm depths and farmers’ irrigation depth are given (Fig. 4). The soil moisture showed similar variation tendencies following irrigation as it rapid increase and decrease by visual comparison. The simulated and observed soil-water content also showed reasonably similar trends and the mean estimation error (MSE) ranged from 0.05 to 0.08. Similarly, the root means square error (RMSE) between observed and simulated soil-water content varied from 0.06 to 0.09. The simulated potential ET by Hydrus model and ETo simulated by CROPWAT 8.0 and from pan evaporation are also illustrated (Fig. 4). The RMSE between ETo from pan and potential ET from Hydrus was 0.69 and the ME was 0.45. Likewise, the RMSE and ME between estimated ETo by Cropwat 8.0 and potential ET from Hydrus model was 0.51 and 0.37 respectively. As expected, the ETo estimated from pan evaporation was generally higher than the simulated values specifically starting from March (Fig. 4). In addition, rainfall was not observed and there was no excess irrigation water leaving the fields during the experimental period. Hence, runoff was not included in the water balance model. The Hydrus model simulations also resulted in zero runoff in all

6

0.40

4

0.35

2 0

0.30 0

20

40

60

80

100

120

Days from planting to harvest

Irrigation Observed moisture at 60 cm observed moisture at 30 cm Simulated moisture at 30 cm

Fig. 4. Applied irrigation, observed and simulated (by Hydrus-1D) soil-water content at 30 and 60 cm depths; potential ET simulated by the Hydrus-1D and ETO calculated from the pan evaporation and Pen Man Monteith method.

49


A. Beyene et al.

Shina maize F1

4

experimental sites. The deep percolation (D) values estimated by the water balance and the Hydrus model for each field for the growing season and irrigation application are shown (Table 7). The highest D (184.7 mm) was predicted in Shina onion field 2 by the water balance model. The Hydrus model predicted the second highest D (139.4 mm) in Bebeks onion field 3. The smallest D (54.4 mm) was predicted in Shina maize field 2 by the Hydrus model. In general, the seasonal D values range from 12 to 41% of the applied irrigation.

2

3.5. Irrigation management and irrigation water consumption

r = 0.64 at 60 cm ME=0.058 RMSE=0.0675

0.50

8 0.45

6

0.40

0.35

0.30 0

20

40

60

80

100

120

140

The experimental period (December–May) is dry season and crop growth was possible by full irrigation applications. Rainfall was not observed in both sites during the experimental period. About 2.5% of the floodplain is currently irrigated which is 60 km2 (surveyed) and are found scattered in small-scale community managed irrigation schemes and farmer level pump irrigation systems from rivers and streams (surface water sources). The crop water requirements were also estimated using the FAO Penman-Monteith method based on the dual crop coefficient approach (Allen et al., 1998) using Cropwat 8.0 and soil properties. The farmers’ irrigation depth at each irrigation event (indicated in Fig. 4) and crop water requirement of each field estimated by Cropwat 8.0 based on planting dates and weather data are given (Table 6). In addition, irrigation scheduling is recommended for maize and onion crops planted at 10th December (most common planting week according to the local practice) in Shina and Bebeks taking the average soil properties of the scheme (Annex A, Annex B, Annex C). The minimum and maximum seasonal irrigation application by farmers were 397 mm and 526 mm respectively. Due to absence of water in the reservoir, maize fields were not irrigated starting from the second week of April (during maize maturity period). This indicated that irrigation amount in maize fields would be higher than the observed values in Table 6. Generally, onion fields were over irrigated than maize fields (Table 6 and Annex A, Annex B, Annex C). The irrigation water abstraction in the floodplain during irrigation seasons likely ranges from 23.8 to 31.5 Mm3 y−1.

0 160

Days from planting to harvest

Shina maize F2

r = 0.61 at 30 cm ME=0.054 RMSE=0.065

10 8

4

a

6

2 0 0

20

40

60

80

100

120

Days from planting to harvest 7 6 5 4

4. Discussion

3

4.1. Evapotranspiration

ETo from Penman

2

PET from Hydrus Model

ETo from Pan

The seasonal values of AET estimated by the Hydrus and the water balance models are comparable and correlated (Pearson r = 0.73). The Hydrus model estimated the actual transpiration and evaporation whereas the water balance model, where the internal physical processes are not considered but the system as a whole is treated (Xu et al., 2016), only estimated AET (merging evaporation and transpiration). Because of high vegetation cover of maize starting from its vegetative stages, soil evaporation was zero and AET was controlled by actual transpiration. In the onion fields, evaporation was high in early stages and reduced starting from vegetative stages but existed throughout the growing season, though here the dominant process was also transpiration (Fig. 5). The actual transpiration was high during most of the growth periods (except in maize fields during the late stage) in both crops due to high irrigation application in the early stages. However, irrigation was excess during most of the growth stages, absence of water for irrigation in maize fields during maturity period resulted stress, which was indicated by a rapid decrease of the actual transpiration during the period (Fig. 5). The water balance modelling results (Table 7) showed that AET is the dominant process of the hydrological cycle (72–94% of irrigation). This high AET was mostly close to the potential ET because the crops were not stressed in most of the seasons (except maize fields during maturity stage). In this particular case,

1 20

40

60

80

100

120

140

Fig. 4. (continued)

Table 4 Reference crop evapotranspiration of the experimental sites (Pen Man method). Month

Bebeks ETo (mm day−1)

Shina ETo (mm day−1)

January February March April May June July August September October November December Average

3.64 4.18 4.69 5.04 5.05 3.98 3.32 3.27 3.66 3.95 3.73 3.52 4.00

3.65 4.18 4.9 5.28 5.12 3.95 3.21 3.14 3.58 3.94 3.77 3.49 4.02

50


A. Beyene et al.

Table 5 Potential crop evapotranspiration for experimental crops (onion and maize) for the growing stages. Crop potential evapotranspiration, ETc (mm) Crops growth stages Growth stages

Initial Development Mid-season Late period Total

Bebeks

Shina

Number of days

Onion crop

Onion crop

onion

maize

Field 1

Field 2

Field 3

Field 1

Field 2

Field 3

Field 1

Field 2

Field 3

20 40 40 20 120

30 40 40 30 140

15.4 60.1 188.1 117.3 381

28.3 44.7 179.6 118.6 372

18.3 63.9 189.1 108.1 379.4

15.4 65.4 190.5 122.6 394

19.8 72.5 198 115.9 406

17 69 197.3 125.7 409

37 101.9 272.2 138.9 550

40.6 105 272.9 128.5 547

44.2 108.2 273.3 118.2 544

Maize crop

that the farmer’s irrigation application might have been more if the reservoir in Shina had not dried. Generally, it could be possible to save the available water and irrigate maize fields to avoid stress during maturity, by reducing early stage over irrigation. The D from irrigation (12–41% of irrigation) is the second important water balance component (next to AET) and the most important loss in the soil reservoir in the study area. Soil evaporation is minimum (Table 6) and drastically decreased during vegetative periods (Fig. 5). Hence, the most important loss in the irrigation system is the D to the groundwater. From the seasonal current irrigation consumption in the floodplains, 3–10 Mm3 y−1 of irrigation water is lost due to D and fed the groundwater. Previous studies of recharge from agricultural alluvial soils of arid climate also indicated that irrigation water is the most important source of groundwater recharge followed by rain water (Ebrahimi et al., 2016). Here, irrigation management strategies that minimize D and conservation practices that reduce excessive unproductive transpiration are crucial to irrigate more fields with the present available surface water resources to meet the growing food and fibre demand. Obviously, deep percolation from irrigation will feed the groundwater for future use for irrigation as well as for urban or rural domestic consumption. Coming to the irrigation consumption, pumping the groundwater for irrigation purpose has higher operation cost than using available surface water sources. If feasible for only some wealthy farmers to pump the groundwater, that would mean un equal resource distribution. Hence, for low income farming society like in the study area, minimizing deep percolation will bring a better and cheaper access to water among beneficiary farmers.

where the crops were fully irrigated (were not under stress), the numerical model might not had much added value, as the ET values were nearly the same in both models. 4.2. Soil moisture and deep percolation The fluctuations in measured and predicted (by Hydrus model) soilwater content before and after irrigation events are higher at 30 cm depth than at 60 cm (Fig. 4). This indicates a higher response to irrigation and evaporative demand as well as more root water uptake in the topsoil than in the subsoil. Soil moisture is generally high in onion fields than in maze fields due to high consumption of water by maize crops. The cumulative deep percolation (D) predicted by the Hydrus and the water balance models are generally comparable. Since in the water balance model, a uniform root depth was considered for all plots, depth to groundwater has no a recognized effect on the predicted D. The Hydrus model predicted higher D in fields with shallower groundwater than fields with higher groundwater depth. For instance, fields 2 and 3 have higher D than field 1 (the deepest water table field in both schemes) in both experimental sites and crops (Table 7). Generally, higher D was predicted by the simple water balance model (Table 7) due to smaller root zone consideration. The resulted showed that, D resulted in a significant loss of available surface water under the current irrigation management in the area of interest (12–41% of the applied water). 4.3. Irrigation water management

5. Conclusion The farmers in both irrigation schemes decide irrigating their fields based on weather conditions and access to water rather than on growth stage-based crop water demand. They apply the same amount of irrigation regardless of growth stages and crop types depending on access to water. Farmers apply more water in early crop stages even if the crop does not require irrigation, believing that applying more water will increase the crop yield (Fig. 4 and Table 6). The farmers over irrigate onion fields than maize fields (Table 6 and Annex A, Annex B, Annex C) because they irrigate regardless of crop type. Overuse of water during early stages caused the water reservoir in Shina to dry. This overuse at the early stage led to shortage of irrigation water for maize fields during maturity stages (maize was planted around 10th of December and harvested in the mid of May), causing stress during the last weeks of the crop stage with the rapid decrease of actual transpiration in maize fields (Fig. 5). Furthermore, this over irrigation during early stages might cause loss of water due to unproductive excessive transpiration in both crops. The farmers’ irrigation scheme uses over irrigation during early growth stages (Table 6 and Annex A, Annex B, Annex C). It can be noted

Both the Hydrus-1D and the water balance model provided reasonably good simulation results of actual evapotranspiration (AET) and deep percolation (D) from irrigated fields in tropical floodplain soils. The seasonal values of D and AET simulated by the Hydrus and the water balance models are comparable. The study showed that AET and D are the dominant processes in the hydrological cycle in the study area. The soil water balance simulation results of the Hydrus model were mainly controlled by the soil hydraulic parameters, irrigation depth, local weather conditions and depth to the groundwater. The saturated soil-water content, saturated hydraulic conductivity and pore size distribution parameter (i.e van Genuchten’s n) were found to be the most important parameter inputs to Hydrus model in the study area. The water balance model can be used as an alternative for numerical models in data scarce areas as the cumulative results obtained by both models are comparable. In general, the D appeared to be a significant loss of applied irrigation water (12–41%) in the floodplains. Farmers’ irrigation practice, in which irrigation was applied regardless

51


A. Beyene et al.

Fig. 5. Actual transpiration, soil evaporation and potential transpiration in the crops growing period (December 2015–May 2016).

52

73 115 197 85 470

15.4 60.1 188.1 117.3 381

98 69 144 109 420

28.3 44.7 179.6 118.6 372

CWR 92 104 138 96 430

I 18.3 63.9 189.1 108.1 379.4

CWR 75 110 162 70 417

I 15.4 65.4 190.5 122.6 394

CWR

I

I

CWR

Field 1 (1210)

Field 2 (995)

Field 1(1600)

Field 3 (1785)

Shina onion fields

Bebeks onion fields

68 119 173 86 446

I 19.8 72.5 198 115.9 406

CWR

Field 2 (644)

34 172 120 71 397

I 17 69 197.3 125.7 409

CWR

Field 3 (616)

73 177 161 95 506

I 37 101.9 272.2 130.2 541.3

CWR

Field 1 (2034)

Shina maize fields

60 136 230 33 459

I 40.6 105 272.9 119.7 538.2

CWR

Field 2 (1503)

53

Irrigation applied

470 420 430 417 446 397 526 459 517

Field

Bebeks OF1 Bebeks OF2 Bebeks OF3 Shina OF1 Shina OF2 Shina OF3 Shina MF1 Shina MF2 Shina MF3

357 347.5 357.8 352.5 332.9 321.5 398.72 424 415.53

342 360 345.4 380 401.5 373 465 426 470

331 339 339 338.7 327.6 333 691.25 532 563.2

H

H

W

Potential transpiration

Actual evapotranspiration H 319.1 310 321 319.2 299.5 287.6 386.62 410.3 400.53

W – – – – – – – – –

Actual transpiration

– – – – – – – – –

W

102.4 115.3 139.4 98.4 110.8 81.15 104.2 54.4 88.2

H

174 97.8 106.8 153.4 184.7 115 155.4 91 138

W

Cum. deep percolation

Table 7 Summary of seasonal water balance output (mm) per field (F1–F3 stands for fields 1–field 3) estimated with the Hydrus model (H) and the water balance model (W) and irrigation application by farmers (mm).

Initial Development Mid-season Late period total

Growth stages

Table 6 Farmers’ irrigation application (I) and crop water requirement (CWR) estimated by Cropwat model (mm). The values in brackets in front of each field indicate the size of each field in m2.

37.9 37.5 36.8 34 33.4 33.9 12.1 13.7 15

H

Soil evaporation

97 150 195 75 517

I

– – – – – – – – –

W

44.2 108.2 273.3 113.8 539.5

CWR

Field 3 (907)

A. Beyene et al.



A. Beyene et al.

thanks to Peer Science project (AID-DAA-A-11-00012) for logistic support. We acknowledge the local administration and agriculture offices for permitting and arranging the fieldwork and all landowners who permit experimentations on their lands. Special thanks are given to the data collectors Bantesew Muluye, Kiflie Wedaje, Mekuanent Ashagrie and Zenebe Hailu. We are grateful to Davy Loete who provided expertise that greatly assisted the research setup. We also thank colleagues in Bahir Dar Institute of Technology Soil Laboratory. Appreciations extend to Mekonnen Getahun, a pedologist and other staff in the soil laboratory of Amhara Design and Supervision Enterprise in Ethiopia.

of crop type and growth stage, must be scheduled according to the crop water demand (Annex A, Annex B, Annex C) in order to save water during early growth stages. Further research on water saving and irrigation management that minimize deep percolation and unproductive excessive transpiration are required to save the available water and cope with the growing food and fibre demand. Acknowledgements The research was financially and technically supported by the Ghent University funded BOF scholarship. Part of the logistics was availed by a VLIR-UOS project (Belgium-Ethiopia). We would like to give special Appendix

The following Annex A, Annex B, Annex C indicate irrigation scheduling for onion and maize: the scheduling was done for both crops planted on December 10 and the scheme average soil properties were used. Annex A. Onion crop grown in Shina.

Date

Day

Growth stage

Depletion %

Net Irrigation mm

Growth Irrigation mm

10-Dec 19-Dec 29-Dec 07-Jan 15-Jan 23-Jan 30-Jan 06-Feb 13-Feb 20-Feb 26-Feb 04-Mar 10-Mar 16-Mar 22-Mar 28-Mar 03-Apr

1 10 20 29 37 45 52 59 66 73 79 85 91 97 103 109 End

Init Init Dev Dev Dev Dev Mid Mid Mid Mid Mid Mid End End End End End

34 31 32 32 32 34 32 33 34 35 31 33 33 33 32 31 total

13.5 14.7 17.8 20.1 21.8 25.7 24.9 25.8 26.6 27.3 24.7 25.6 26 25.9 25.3 24.1 369.8

16.8 18.4 22.3 25.2 27.3 32.2 31.1 32.3 33.3 34.1 30.9 32 32.6 32.4 31.6 30.1 462.6

Annex B. Maize crop grown in Shina.

Date

Day

Growth stage

Depletion %

Net Irrigation mm


14-Dec 01-Jan 20-Jan 04-Feb 17-Feb 01-Mar 12-Mar 22-Mar 01-Apr 12-Apr 24-Apr 28-Apr

5 23 42 57 70 82 93 103 113 124 136 End

Init Init Dev Dev Dev Mid Mid Mid Mid End End End

41 40 48 56 61 65 64 61 63 69 71 total

32.4 31.8 37.9 43.6 48.2 51.2 50.7 48.2 49.2 54.2 55.9 503.3

40.4 39.7 47.3 54.5 60.2 64 63.3 60.3 61.6 67.7 69.9 628.9

54


A. Beyene et al.

Annex C. Onion crop grown in Bebeks.

Date

Day

Growth stage

Depletion %

Net Irrigation mm


10-Dec 19-Dec 29-Dec 08-Jan 17-Jan 25-Jan 02-Feb 09-Feb 16-Feb 23-Feb 02-Mar 09-Mar 16-Mar 24-Mar 01-Apr 03-Apr

1 10 20 30 39 47 55 62 69 76 83 90 97 105 113 End

Init Init Init Dev Dev Dev Mid Mid Mid Mid Mid Mid End End End End

34 32 30 32 33 33 33 31 32 32 32 32 30 34 33 total

13.8 15.2 16.9 20.4 23.4 25.6 26.9 24.6 25.3 25.5 25.5 25.5 24.2 27.1 26.8 346.7

17.3 19.1 21.1 25.5 29.2 32 33.6 30.8 31.7 31.8 31.8 31.8 30.3 33.9 33.4 433.3

Basin, Ethiopia. Université Joseph-Fourier-Grenoble I. Enku, T., Melesse, A.M., Ayana, E.K., Tilahun, S.A., Abate, M., Steenhuis, T.S., 2016. Groundwater-evaporation and recharge for a floodplain in a sub-humid monsoon climate in Ethiopia. Land Degrad. Dev. 28, 1831–1841. FAO, 2009. CROPWAT 8.0 Model. Food and Agriculture Organization, Rome, Italy. FAO, 2010. AQUASTAT – FAO’s Global Information System on Water and Agriculture. FAO, Rome, Italy. http://www.fao.org/nr/aquastat. Fan, J., McConkey, B., Wang, H., Janzen, H., 2016. Root distribution by depth for temperate agricultural crops. Field Crops Res. 189, 68–74. Feddes, R., Raats, P., 2004. Parameterizing the Soil–Water–Plant Root System. Unsaturated-Zone Modeling: Progress, Challenges, and Applications. Wageningen UR Frontis Ser 6. pp. 95–141. Feddes, R.A., Kowalik, P.J., Zaradny, H., 1978. Simulation of Field Water Use and Crop Yield. Centre for Agricultural Publishing and Documentation. Forkutsa, I., Sommer, R., Shirokova, Y., Lamers, J., Kienzler, K., Tischbein, B., Martius, C., Vlek, P., 2009. Modeling irrigated cotton with shallow groundwater in the Aral Sea Basin of Uzbekistan: I. Water dynamics. Irrig. Sci. 27, 331–346. Geremew, E.B., Steyn, J., Annandale, J., 2008. Comparison between traditional and scientific irrigation scheduling practices for furrow irrigated potatoes (Solanum tuberosum L.) in Ethiopia. S. Afr. J. Plant Soil 25, 42–48. González, M.G., Ramos, T.B., Carlesso, R., Paredes, P., Petry, M.T., Martins, J.D., Aires, N.P., Pereira, L.S., 2015. Modelling soil water dynamics of full and deficit drip irrigated maize cultivated under a rain shelter. Biosyst. Eng. 132, 1–18. Greve, F., 1932. Flow of Water Through Circular, Parabolic, and Triangular Vertical Notch-Ueirs. Purdue University. Engineering Experiment Station. Grismer, M., Orang, M., Snyder, R., Matyac, R., 2002. Pan evaporation to reference evapotranspiration conversion methods. J. Irrig. Drain. Eng. 128, 180–184. Hillel, D., 2004. Introduction to Environmental Soil Physics. Elsevier, Amsterdam, Netherlands. Jiménez-Martínez, J., Skaggs, T., Van Genuchten, M.T., Candela, L., 2009. A root zone modelling approach to estimating groundwater recharge from irrigated areas. J. Hydrol. 367, 138–149. Kay, M., 2001. Smallholder Irrigation Technology: Prospects for Sub-Saharan Africa. Food Agriculture Org. Kebede, S., Admasu, G., Travi, Y., 2011. Estimating ungauged catchment flows from Lake Tana floodplains, Ethiopia: an isotope hydrological approach. Isot. Environ. Health Stud. 47, 71–86. Kloos, H., Legesse, W., 2016. Water Resources Management in Ethiopia: Implications for the Nile Basin. Cambria Press. Kollet, S.J., Maxwell, R.M., 2008. Capturing the influence of groundwater dynamics on land surface processes using an integrated, distributed watershed model. Water Resour. Res. 44 W02402, 02410.01029/02007WR006004. Krause, S., Bronstert, A., Zehe, E., 2007. Groundwater–surface water interactions in a North German lowland floodplain–implications for the river discharge dynamics and riparian water balance. J. Hydrol. 347, 404–417. Li, Y., Šimůnek, J., Jing, L., Zhang, Z., Ni, L., 2014. Evaluation of water movement and water losses in a direct-seeded-rice field experiment using Hydrus-1D. Agric. Water Manag. 142, 38–46. Loosvelt, L., Pauwels, V.R.N., Cornelis, W.M., De Lannoy, G.J., Verhoest, N.E.C., 2011. Impact of soil hydraulic parameter uncertainty on soil moisture modeling. Water Resour. Res. 47 W03505, 03510.01029/02010WR009204. Makurira, H., Mul, M., Vyagusa, N., Uhlenbrook, S., Savenije, H., 2007. Evaluation of community-driven smallholder irrigation in dryland South Pare Mountains, Tanzania: a case study of Manoo micro dam. Phys. Chem. Earth Parts A/B/C 32, 1090–1097.

References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56, vol. 300. FAO, Rome, pp. D05109. Arnold, J., Allen, P., 1996. Estimating hydrologic budgets for three Illinois watersheds. J. Hydrol. 176, 57–77. Ayers, R., Westcot, D., 1985. Water Quality for Agriculture. FAO Irrigation and Drainage Paper 29 Rev. 1. Food and Agricultural Organization, Rome. Belay, M., Bewket, W., 2013. Traditional irrigation and water management practices in highland Ethiopia: case study in Dangila Woreda. Irrig. Drain. 62, 435–448. Bethune, M., Selle, B., Wang, Q., 2008. Understanding and predicting deep percolation under surface irrigation. Water Resour. Res. 44 W12430, 12410.11029/ 12007WR006380. Botula, Y.-D., Cornelis, W., Baert, G., Van Ranst, E., 2012. Evaluation of pedotransfer functions for predicting water retention of soils in Lower Congo (DR Congo). Agric. Water Manag. 111, 1–10. Botula, Y.-D., Nemes, A., Mafuka, P., Van Ranst, E., Cornelis, W.M., 2013. Prediction of water wetention of soils from the humid tropics by the nonparametric k-nearest neighbor approach. Vadose Zone J. 12. http://dx.doi.org/10.2136/vzj2012.0123. Clarke, N., Bizimana, J.-C., Dile, Y., Worqlul, A., Osorio, J., Herbst, B., Richardson, J.W., Srinivasan, R., Gerik, T.J., Williams, J., 2017. Evaluation of new farming technologies in Ethiopia using the Integrated Decision Support System (IDSS). Agric. Water Manag. 180, 267–279. Collick, A.S., Easton, Z.M., Ashagrie, T., Biruk, B., Tilahun, S., Adgo, E., Awulachew, S.B., Zeleke, G., Steenhuis, T.S., 2009. A simple semi-distributed water balance model for the Ethiopian highlands. Hydrol. Process. 23, 3718–3727. Crevoisier, D., Popova, Z., Mailhol, J.-C., Ruelle, P., 2008. Assessment and simulation of water and nitrogen transfer under furrow irrigation. Agric. Water Manag. 95, 354–366. Cuenca, R.H., 1989. Irrigation System Design. An Engineering Approach. Prentice-Hall, Englewood Cliffs, New Jersey. Derib, S., Descheemaeker, K., Haileslassie, A., Amede, T., Tischbein, B., 2010. Water Productivity as Affected by Water Management in a Small-Scale Irrigation Scheme in the Blue Nile Basin, Ethiopia [Abstract Only]. Dessie, M., Verhoest, N.E., Admasu, T., Pauwels, V.R., Poesen, J., Adgo, E., Deckers, J., Nyssen, J., 2014a. Effects of the floodplain on river discharge into Lake Tana (Ethiopia). J. Hydrol. 519, 699–710. Dessie, M., Verhoest, N.E.C., Admasu, T., Pauwels, V.R.N., Poesen, J., Adgo, E., Deckers, J., Nyssen, J., 2014b. Effects of the floodplain on river discharge into Lake Tana (Ethiopia). J. Hydrol. 519, 699–710. Dessie, M., Verhoest, N.E.C., Pauwels, V.R.N., Negatu, T.A., Poesen, J., Adgo, E., Deckers, J., Nyssen, J., 2014c. Analyzing runoff processes through conceptual hydrological modeling in the Upper Blue Nile Basin, Ethiopia. Hydrol. Earth Syst. Sci. 18, 5149–5167. Diamantopoulos, E., Iden, S., Durner, W., 2012. Inverse modeling of dynamic nonequilibrium in water flow with an effective approach. Water Resour. Res. 48. Doorenbos, J., Pruitt, W., 1977. Crop Water Requirements, FAO Irrig. Drain. Pap., 24. Food and Agriculture Organization, Rome. Ebrahimi, H., Ghazavi, R., Karimi, H., 2016. Estimation of groundwater recharge from the rainfall and rrrigation in an arid environment usinginverse modeling approach and RS. Water Resour. Manag. 30, 1939–1951. Engida, A.N., 2010. Hydrological and Suspended Sediment Modeling in the Lake Tana

55


A. Beyene et al.

engineering method to predict recharge. J. Hydrol. 84, 221–229. Swennenhuis, J., 2009. FAO, CROPWAT 8.0 Model. Food and Agriculture Organization. Water Resources Development and Management Service of FAO, Rome, Italy. Tafteh, A., Sepaskhah, A.R., 2012. Application of HYDRUS-1D model for simulating water and nitrate leaching from continuous and alternate furrow irrigated rapeseed and maize fields. Agric. Water Manag. 113, 19–29. Taylor, S.A., Ashcroft, G.L., 1972. Physical Edaphology. The Physics of Irrigated and Nonirrigated Soils. Tockner, K., Bunn, S.E., Gordon, C., Naiman, R.J., Quinn, G.P., Stanford, J.A., 2008. Floodplains: Critically Threatened Ecosystems, Aquatic Ecosystems. Trends and Global Prospects. Cambridge University Press, pp. 45–61. Van Dam, J., Malik, R., 2003. Water productivity of irrigated crops in Sirsa district. India Agric. Water Manag. 82, 253–278. Van Dam, J., De Rooij, G., Heinen, M., Stagnitti, F., 2004. Concepts and dimensionality in modeling unsaturated water flow and solute transport. International Symposium on Unsaturated Zone Modelling: Progress Challenges and Applications 3–5. Van Genuchten, M.T., Leij, F., Yates, S., 1991. The RETC Code for Quantifying the Hydraulic Functions of Unsaturated Soils. Citeseer. Van Genuchten, M.T., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898. Vereecken, H., Schnepf, A., Hopmans, J., Javaux, M., Or, D., Roose, T., Vanderborght, J., Young, M., Amelung, W., Aitkenhead, M., 2016. Modeling soil processes: review, key challenges, and new perspectives. Vadose Zone J. 15. http://dx.doi.org/10.2136/ vzj2015.2109.0131. Wösten, J., 1997. Pedotransfer functions to evaluate soil quality. In: Gregorich, E.G., Carter, M.R. (Eds.), Soil Quality for Crop Production and Ecosystem Health: Developments in Soil Science. Elsevier Science Publishers B.V., Amsterdam, pp. 221–245. Walkley, A., Black, I.A., 1934. An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Sci. 37, 29–38. Wesseling, J., Elbers, J., Kabat, P., van den Broek, B., 1991. SWATRE: Instructions for Input. Internal Note, Winand Staring Centre, Wageningen, The Netherlands. International Waterlogging and Salinity Research Institute, Lahore, Pakistan, pp. 29. Worqlul, A.W., Collick, A.S., Rossiter, D.G., Langan, S., Steenhuis, T.S., 2015. Assessment of surface water irrigation potential in the Ethiopian highlands: the Lake Tana Basin. Catena 129, 76–85. Worqlul, A.W., Yen, H., Collick, A.S., Tilahun, S.A., Langan, S., Steenhuis, T.S., 2017. Evaluation of CFSR, TMPA 3B42 and ground-based rainfall data as input for hydrological models in data-scarce regions: the upper Blue Nile Basin, Ethiopia. Catena 152, 242–251. Worqlul, A.W., Ayana, E.K., Maathuis, B.H., MacAlister, C., Philpot, W.D., Leyton, J.M.O., Steenhuis, T.S., 2018. Performance of bias corrected MPEG rainfall estimate for rainfall-runoff simulation in the upper Blue Nile Basin, Ethiopia. J. Hydrol. 556, 1182–1191. http://dx.doi.org/10.1016/j.jhydrol.2017.1001.1058. © 2017 Elsevier B.V. All rights reserved. Wossenie, M.D., 2015. Water Balance Modelling of the Lake Tana Area Ethiopia. Ghent University. Xu, X., Zhang, Q., Li, Y., Li, X., 2016. Evaluating the influence of water table depth on transpiration of two vegetation communities in a lake floodplain wetland. Hydrol. Res. 47, 293–312. Yu, G.-R., Zhuang, J., Nakayama, K., Jin, Y., 2007. Root water uptake and profile soil water as affected by vertical root distribution. Plant Ecol. 189, 15–30.

Maxwell, R.M., Miller, N.L., 2005. Development of a coupled land surface and groundwater model. J. Hydrometeorol. 6, 233–247. McCartney, M., Alemayehu, T., Shiferaw, A., Awulachew, S., 2010. Evaluation of Current and Future Water Resources Development in the Lake Tana Basin, Ethiopia. IWMI. Meiwirth, K., Mermoud, A., 2004. Simulation of herbicide transport in an alluvial plain. In: iEMSs 2004 International Congress: Complexity and Integrated Resources Management. iEMSs, Switzerland. Citeseer. pp. 951–955. Mintesinot, B., Verplancke, H., Van Ranst, E., Mitiku, H., 2004. Examining traditional irrigation methods, irrigation scheduling and alternate furrows irrigation on vertisols in northern Ethiopia. Agric. Water Manag. 64, 17–27. Mualem, Y., 1976. A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12, 513–522. Rao, N., 1987. Field test of a simple soil-water balance model for irrigated areas. J. Hydrol. 91, 179–186. Rashid, M., Bhatti, H., Nadeem, M., Gill, M., 1993. Use of saline water for crop production. Towards the Rational Use of High Salinity Tolerant Plants. Springer, pp. 157–169. Rezaei, M., Seuntjens, P., Joris, I., Boënne, W., Van Hoey, S., Campling, P., Cornelis, W., 2016. Sensitivity of water stress in a two-layered sandy grassland soil to variations in groundwater depth and soil hydraulic parameters. Hydrol. Earth Syst. Sci. 20, 487–503. Rhoades, J., 1993. Electrical conductivity methods for measuring and mapping soil salinity. Adv. Agron. 49, 201–251. Richard, L., 1968. Diagnosis and Improvement of Saline and Alkaline Soil. United States Salinity Laboratory Staff. Sentelhas, P.C., Folegatti, M.V., 2003. Class A pan coefficients (Kp) to estimate daily reference evapotranspiration (ETo). Revista Brasileira de Engenharia Agrícola e Ambiental 7, 111–115. Setegn, S.G., Srinivasan, R., Dargahi, B., 2008. Hydrological modelling in the Lake Tana Basin, Ethiopia using SWAT model. Open Hydrol. J. 2, 49–62. Shen, J., 1981. Discharge Characteristics of Triangular-Notch Thin-Plate Weirs. United States Department of the Interior, Geological Survey. Siebert, S., Burke, J., Faures, J.M., Frenken, K., Hoogeveen, J., Döll, P., Portmann, F.T., 2010. Groundwater use for irrigation – a global inventory. Hydrolol. Earth Syst. Sci. 14, 1863–1880. Šimůnek, J., Jarvis, N.J., Van Genuchten, M.T., Gärdenäs, A., 2003. Review and comparison of models for describing non-equilibrium and preferential flow and transport in the vadose zone. J. Hydrol. 272, 14–35. Šimůnek, J., Van Genuchten, M.T., Sejna, M., 2005. The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media. University of California-Riverside Research Reports 3, pp. 1–240. Šimůnek, J., van Genuchten, M.T., Šejna, M., 2008. Development and applications of the HYDRUS and STANMOD software packages and related codes. Vadose Zone J. 7, 587–600. Smith, M., 1992. CROPWAT: A Computer Program for Irrigation Planning and Management. Food Agriculture Org. Soldevilla-Martinez, M., Quemada, M., López-Urrea, R., Munoz-Carpena, R., Lizaso, J., 2014. Soil water balance: comparing two simulation models of different levels of complexity with lysimeter observations. Agric. Water Manag. 139, 53–63. Soylu, M., Istanbulluoglu, E., Lenters, J., Wang, T., 2011. Quantifying the impact of groundwater depth on evapotranspiration in a semi-arid grassland region. Hydrol. Earth Syst. Sci. 15, 787–806. Steenhuis, T., Van der Molen, W., 1986. The Thornthwaite-Mather procedure as a simple

56