Reference evapotranspiration is very important parameter in the hydrological, agricultural and environmental studies and is accurately estimated by the FAO Penman-Monteith equation (FAO-PM) under different climatic conditions. However, due to data requirement of the FAO-PM equation, there is a need to investigate the applicability of alternative ETo equations under limited data. The objectives of this study were to evaluate twelve mass transfer based reference evapotranspiration equations and determine the impact of ETo equation on long term water management sustainability in Tanzania and Kenya. The results showed that the Albrecht, Brockamp-Wenner, Dalto, Meyer, Rohwer and Oudin ETo equations systematically overestimated the daily ETo at all weather stations with relative errors that varied from 34% to 94% relative to the FAO-PM ETo estimates. The Penman, Mahringer, Trabert, and the Romanenko equations performed best across Tanzania and the South Western Kenya with root mean squared errors ranging from 0.98 to 1.48 mm/day, which are relatively high and mean bias error (MBE) varying from -0.33 to 0.02 mm/day and the absolute mean error (AME) from 0.79 to 1.16 mm/day. For sustainable water management, the Trabert equation could be adopted at Songea, the Mahringer equation at Tabora, the Dalton and/or the Rohwer equations at Eldoret, the Romanenko equation at Dodoma, Songea and Eldoret. However, regional calibration of the most performing equation could improve water management at regional level.
Evapotranspiration is an important parameter for climatological and hydrological studies as well as for agricultural water resources management [
[
While the aforementioned studies have been conducted at different parts of the world, extremely limited data and information is available on this very important topic in Eastern Africa, particularly in Tanzania and Kenya. Therefore, it is critical to assess the applicability of ETo models for sustainable water management. Thus, the objectives of this study were to: 1) evaluate twelve mass transfer based ETo equations with comparison to the FAO-PM method, and 2) determine the impact of ETo equation on long term water management sustainability in Tanzania and Kenya.
Climatic data required for evaluating the FAO-PM ETo equation and the selected ETo models were collected at five weather stations across Tanzania and four weather stations in South-Western Kenya, for the period of 1998-2012. Maximum and minimum air temperature (Tmax and Tmin), maximum and minimum relative humidity (RHmax and RHmin), solar radiation (Rs) and wind speed (u2) were collected from Dodoma, Morogoro, Songea, Kilimanjaro, and Tabora in Tanzania; and Nakuru, Jomo Kenyatta, Kisumu, and Eldoret in Kenya with the geographic coordinates presented in
Country | Weather station | Latitude (Degree North) | Longitude (Degree East) | Altitude(m) |
---|---|---|---|---|
Tanzania | Dodoma | −6.17 | 35.77 | 1120 |
Morogoro | −6.83 | 37.65 | 526 | |
Songea | −10.67 | 36 | 1036 | |
Kilimanjaro | −3.43 | 37.07 | 896 | |
Tabora | −5.08 | 33 | 1182 | |
Kenya | Nakuru | −0.16 | 36.6 | 1901 |
Jomo Kenyatta | −1.32 | 36.92 | 1624 | |
Kisumu | −0.09 | 34.73 | 1146 | |
Eldoret | 0.48 | 35.3 | 2120 |
northwestern highlands. The northern and eastern areas of Tanzania experience two distinct rain seasons; the short occurring during October to December and the long rains from March to May. However, the southern, western, and central parts of the country experience one wet season that continues from October through to April or May. The central plateau in Tanzania tends to be dry and arid throughout the year. Kenya’s spatial extent lies astride the equator and thus characterized by a tropical climate. Similar to Tanzania, Kenya’s coastal zone is hot and humid, has a temperate inland, very dry in the north and northeastern areas and the western area is hot and wet throughout the year [
1) Penman-Monteith model (FAO-PM)
Daily reference evapotranspiration was computed using the Penman-Monteith (FAO-PM) equation (ETo-Ref) [
ETo = 0.408 Δ ( R n − G ) + ( γ C n u 2 / ( T + 273 ) ) ( e s − e a ) Δ + γ ( 1 + C d u 2 ) (1)
where: ETo is the reference evapotranspiration (mm/day), Δ is the slope of saturation vapor pressure versus air temperature curve (kPa・˚C−1), Rn = net radiation at the crop surface (MJ・m−2・d−1), G = soil heat flux density at the soil surface (MJ・m−2・d−1), T = mean daily air temperature at 1.5 - 2.5 m height (˚C), u2 = mean daily wind speed at 2 m height (m・s−1), es = the saturation vapor pressure at 1.5 - 2.5 m height (kPa), ea = the actual vapor pressure at 1.5 - 2.5 m height (kPa), es − ea = saturation vapor pressure deficit (kPa), γ = psychrometric constant (kPa・˚C−1), Cn = 900˚C・mm・s3・Mg−1・d−1, Cd = 0.34 s・m−1 for grass, γ is the psychrometric constant (kPa・˚C−1). All parameters necessary for computing ETo were computed according the procedure developed in FAO-56 by [
Twelve mass transfer ETo equations were selected based on their applicability to regions with similar characteristics and compared with the FAO-PM equation for their accuracy in estimating daily ETo and to determine the best performing equations at each weather station.
2) [
ETo = ( 3.648 + 0.7223 u ) ( e s − e a ) (2)
3) [
ETo = 3.075 u ( e s − e a ) (3)
4) [
ETo = ( 0.375 + 0.05026 u ) ( e s − e a ) (4)
5) [
ETo = ( 3.3 + 0.891 u ) ( e s − e a ) (5)
6) [
ETo = 0.35 ( 1 + 0.98 / 100 u ) ( e s − e a ) (6)
7) [
ETo = ( 0.1005 + 0.297 u ) ( e s − e a ) (7)
8) [
ETo = 0.0018 ( T a + 25 ) 2 ( 100 − R H ) (8)
9) [
ETo = 0.543 u 0.456 ( e s − e a ) (9)
10) [
ETo = ( 1.298 + 0.934 u ) ( e s − e a ) (10)
11) [
ETo = 2.5 ( e m s − e a ) (11)
12) [
ETo = 0.15072 ∗ 3.6 u ∗ ( e s − e a ) (12)
13) [
ETo = 4.5 ∗ [ 1 + ( T m e a n 25 ) ] 2 ∗ ( 1 − e a e s ) (13)
where ETo is in mm/day, ems, ea and es in kPa, u in m/s, Tmean is mean daily temperature in (˚C), RH is daily mean relative humidity in %.
Comparisons were developed using graphics and simple linear regression. For further comparison, root mean squared error (RMSE), relative error (RE), mean bias error (MBE) and the absolute mean error (AME) were used to evaluate the simplified reference evapotranspiration models [
RMSE = ∑ i = 0 n ( P i − O i ) 2 n (14)
RE = RMSE ETomean × 100 (15)
MBE = n − 1 ∑ 1 n ( P i − O i ) (16)
AME = n − 1 ∑ 1 n | P i − O i | (17)
where, Pi is the estimated ETo with the radiation based ETo models; and Oi is ETo estimated with FAO-PM model with full dataset, at the ith data point and n is the total number of data points.
The evaluation of the twelve mass transfer based ETo equations showed different degrees of performance of the equations with comparably reasonable coefficient of determination across the study area. The Brockamp-Wenner equation obtained the highest ETo overestimation with the RMSE ranging from 3.18 to 6.19 mm/day, an average relative error of 95% of ETo estimates and the highest AME range of 1.78 - 5.47 mm/day (
Index | Locations | Alb | Pen | B-W | Dal | Mah | Mey | Tra | WMO | Pap | Roh | Oud | Rom |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Regression slope | Dodoma | 1.951 | 0.806 | 2.094 | 1.400 | 1.150 | 1.285 | 1.236 | 0.942 | 0.638 | 1.423 | 1.407 | 0.988 |
Morogoro | 0.929 | 1.061 | 1.524 | 1.317 | 0.806 | 1.281 | 0.867 | 0.680 | 0.750 | 1.267 | 1.601 | 1.090 | |
Songea | 1.458 | 0.895 | 1.813 | 1.266 | 0.983 | 1.186 | 1.056 | 0.682 | 0.627 | 1.263 | 1.429 | 0.996 | |
Kilimanjaro | 1.625 | 1.081 | 2.108 | 1.498 | 1.139 | 1.412 | 1.123 | 0.903 | 0.759 | 1.486 | 1.609 | 1.089 | |
Tabora | 1.397 | 1.106 | 1.992 | 1.472 | 1.068 | 1.403 | 1.149 | 0.812 | 0.779 | 1.444 | 1.684 | 1.188 | |
Nakuru | 1.121 | 0.923 | 1.642 | 1.218 | 0.879 | 1.164 | 0.945 | 0.688 | 0.560 | 1.192 | 1.404 | 0.902 | |
Jomo Kenyatta | 1.178 | 0.806 | 1.163 | 1.132 | 0.879 | 1.063 | 0.945 | 0.695 | 0.566 | 1.128 | 1.306 | 0.849 | |
Kisumu | 1.141 | 0.936 | 1.170 | 1.236 | 0.894 | 1.181 | 0.962 | 0.700 | 0.660 | 1.121 | 1.415 | 0.916 | |
Eldoret | 1.108 | 0.749 | 1.500 | 1.052 | 0.812 | 0.988 | 0.874 | 0.644 | 0.525 | 1.047 | 1.202 | 1.000 | |
Average | 1.323 | 0.929 | 1.667 | 1.288 | 0.957 | 1.218 | 1.017 | 0.750 | 0.652 | 1.263 | 1.451 | 1.002 | |
R2 | Dodoma | 0.55 | 0.70 | 0.69 | 0.75 | 0.68 | 0.76 | 0.78 | 0.66 | 0.69 | 0.73 | 0.71 | 0.65 |
Morogoro | 0.33 | 0.53 | 0.47 | 0.63 | 0.46 | 0.63 | 0.46 | 0.54 | 0.51 | 0.63 | 0.42 | 0.43 | |
Songea | 0.48 | 0.68 | 0.62 | 0.67 | 0.61 | 0.68 | 0.61 | 0.60 | 0.67 | 0.66 | 0.66 | 0.61 | |
Kilimanjaro | 0.55 | 0.80 | 0.72 | 0.79 | 0.71 | 0.80 | 0.71 | 0.70 | 0.79 | 0.78 | 0.77 | 0.67 | |
Tabora | 0.44 | 0.64 | 0.58 | 0.64 | 0.57 | 0.65 | 0.57 | 0.58 | 0.63 | 0.64 | 0.65 | 0.58 | |
Nakuru | 0.39 | 0.58 | 0.52 | 0.58 | 0.51 | 0.58 | 0.51 | 0.52 | 0.57 | 0.57 | 0.60 | 0.50 | |
Jomo Kenyatta | 0.55 | 0.73 | 0.70 | 0.74 | 0.69 | 0.74 | 0.69 | 0.67 | 0.72 | 0.73 | 0.74 | 0.68 | |
Kisumu | 0.41 | 0.60 | 0.55 | 0.60 | 0.54 | 0.61 | 0.54 | 0.54 | 0.59 | 0.59 | 0.62 | 0.52 | |
Eldoret | 0.42 | 0.67 | 0.57 | 0.62 | 0.56 | 0.64 | 0.56 | 0.53 | 0.67 | 0.60 | 0.70 | 0.70 | |
Average | 0.46 | 0.66 | 0.60 | 0.67 | 0.59 | 0.68 | 0.60 | 0.59 | 0.65 | 0.66 | 0.65 | 0.59 | |
RMSE | Dodoma | 5.95 | 0.81 | 6.19 | 2.40 | 1.45 | 1.79 | 1.81 | 1.09 | 1.99 | 2.56 | 2.35 | 0.80 |
Morogoro | 2.73 | 0.88 | 3.42 | 1.74 | 1.67 | 1.57 | 1.67 | 1.62 | 1.20 | 1.62 | 2.79 | 1.00 | |
Songea | 3.20 | 0.84 | 4.00 | 1.61 | 1.14 | 1.29 | 1.25 | 1.32 | 1.66 | 1.65 | 2.11 | 0.90 | |
Kilimanjaro | 4.36 | 0.83 | 5.77 | 2.67 | 1.49 | 2.24 | 1.79 | 1.21 | 1.26 | 2.67 | 3.07 | 0.96 | |
Tabora | 3.63 | 1.18 | 5.56 | 2.78 | 1.58 | 2.43 | 1.81 | 1.42 | 1.31 | 2.71 | 3.63 | 1.62 | |
Nakuru | 2.29 | 0.93 | 3.77 | 1.64 | 1.32 | 1.40 | 1.30 | 1.76 | 1.80 | 1.58 | 2.23 | 0.96 | |
Jomo Kenyatta | 2.29 | 1.05 | 3.18 | 1.08 | 1.02 | 0.86 | 0.96 | 1.55 | 1.99 | 1.10 | 1.59 | 0.91 | |
Kisumu | 2.27 | 0.89 | 3.80 | 1.66 | 1.25 | 1.41 | 1.25 | 1.68 | 1.73 | 1.60 | 2.28 | 0.92 | |
Eldoret | 2.87 | 1.39 | 3.21 | 1.32 | 1.52 | 1.14 | 1.47 | 1.97 | 2.27 | 1.39 | 1.39 | 0.86 | |
Average | 3.29 | 0.98 | 4.32 | 1.88 | 1.38 | 1.57 | 1.48 | 1.51 | 1.69 | 1.88 | 2.38 | 0.99 | |
RE | Dodoma | 113.81 | 15.43 | 118.50 | 45.95 | 27.67 | 34.26 | 34.57 | 20.92 | 38.00 | 48.92 | 45.02 | 15.26 |
Morogoro | 67.62 | 21.75 | 84.95 | 43.13 | 41.43 | 38.84 | 41.38 | 40.19 | 29.68 | 40.20 | 69.21 | 24.81 | |
Songea | 77.01 | 20.24 | 96.51 | 38.92 | 27.46 | 31.17 | 30.06 | 31.92 | 40.05 | 39.79 | 50.89 | 21.69 | |
Kilimanjaro | 94.81 | 18.06 | 125.48 | 58.13 | 32.47 | 48.59 | 39.00 | 26.19 | 27.36 | 58.07 | 66.69 | 20.81 | |
Tabora | 76.31 | 24.87 | 116.81 | 58.52 | 33.17 | 51.09 | 38.04 | 29.87 | 27.55 | 56.89 | 76.23 | 33.98 | |
Nakuru | 48.09 | 19.65 | 79.28 | 34.43 | 27.80 | 29.38 | 27.39 | 36.94 | 37.75 | 33.32 | 46.96 | 20.27 | |
Jomo Kenyatta | 52.39 | 23.96 | 72.74 | 24.65 | 23.32 | 19.62 | 21.98 | 35.40 | 45.46 | 25.16 | 36.38 | 20.75 | |
Kisumu | 48.39 | 18.97 | 81.16 | 35.40 | 26.76 | 30.17 | 26.63 | 35.85 | 36.82 | 34.15 | 48.69 | 19.54 | |
Eldoret | 63.02 | 30.49 | 70.55 | 28.94 | 33.46 | 25.04 | 32.19 | 43.15 | 49.90 | 30.51 | 30.54 | 18.77 |
Average | 71.27 | 21.49 | 94.00 | 40.90 | 30.40 | 34.24 | 32.36 | 33.38 | 36.95 | 40.78 | 52.29 | 21.76 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
MBE | Dodoma | 4.57 | −0.47 | 5.47 | 1.98 | 0.64 | 1.40 | 1.08 | −0.44 | −1.91 | 2.08 | 2.13 | -0.08 |
Morogoro | −0.67 | 0.27 | 1.78 | 1.22 | −0.97 | 1.10 | −0.74 | −1.40 | −0.98 | 0.99 | 2.52 | 0.41 | |
Songea | 1.56 | −0.48 | 3.12 | 0.98 | −0.21 | 0.67 | 0.08 | −1.03 | −1.58 | 0.95 | 1.75 | -0.06 | |
Kilimanjaro | 2.28 | 0.33 | 4.69 | 2.11 | 0.40 | 1.76 | 0.78 | −0.65 | −1.13 | 2.03 | 2.84 | 0.43 | |
Tabora | 1.47 | 0.43 | 4.36 | 2.07 | 0.12 | 1.77 | 0.49 | −0.91 | −1.10 | 1.92 | 3.18 | 0.80 | |
Nakuru | 0.36 | −0.41 | 2.86 | 0.94 | −0.68 | 0.70 | −0.38 | −1.56 | −1.69 | 0.81 | 1.90 | -0.48 | |
Jomo Kenyatta | 0.94 | −0.90 | 2.51 | 0.47 | −0.65 | 0.18 | −0.37 | −1.44 | −1.93 | 0.43 | 1.31 | -0.68 | |
Kisumu | 0.44 | −0.34 | 2.94 | 1.01 | −0.61 | 0.77 | −0.30 | −1.49 | −1.63 | 0.88 | 1.97 | -0.41 | |
Eldoret | 0.46 | −1.23 | 1.95 | 0.06 | −1.04 | −0.21 | −0.77 | −1.77 | −2.22 | 0.02 | 0.82 | -0.08 | |
Average | 1.27 | −0.31 | 3.30 | 1.20 | −0.33 | 0.91 | −0.02 | −1.19 | −1.58 | 1.12 | 2.04 | −0.02 | |
MBE | Dodoma | 4.98 | 0.67 | 5.57 | 2.04 | 1.15 | 1.49 | 1.48 | 0.86 | 1.91 | 2.17 | 2.14 | 0.63 |
Morogoro | 2.03 | 0.69 | 2.70 | 1.38 | 1.34 | 1.26 | 1.30 | 1.47 | 1.03 | 1.24 | 2.55 | 0.77 | |
Songea | 2.18 | 0.68 | 3.22 | 1.21 | 0.88 | 0.98 | 0.95 | 1.16 | 1.58 | 1.22 | 1.79 | 0.74 | |
Kilimanjaro | 2.91 | 0.63 | 4.76 | 2.18 | 1.10 | 1.83 | 1.30 | 1.02 | 1.15 | 2.11 | 2.85 | 0.74 | |
Tabora | 2.46 | 0.95 | 4.52 | 2.19 | 1.23 | 1.91 | 1.40 | 1.21 | 1.16 | 2.09 | 3.19 | 1.29 | |
Nakuru | 1.55 | 0.77 | 2.97 | 1.12 | 1.08 | 0.94 | 1.00 | 1.63 | 1.70 | 1.06 | 1.91 | 0.81 | |
Jomo Kenyatta | 1.61 | 0.93 | 2.56 | 0.80 | 0.88 | 0.65 | 0.80 | 1.46 | 1.93 | 0.81 | 1.35 | 0.78 | |
Kisumu | 1.52 | 0.72 | 3.03 | 1.15 | 1.02 | 0.96 | 0.95 | 1.56 | 1.63 | 1.08 | 1.97 | 0.76 | |
Eldoret | 1.84 | 1.29 | 2.19 | 0.90 | 1.36 | 0.85 | 1.26 | 1.85 | 2.22 | 0.96 | 0.96 | 0.62 | |
Average | 2.34 | 0.82 | 3.50 | 1.44 | 1.11 | 1.21 | 1.16 | 1.36 | 1.59 | 1.42 | 2.08 | 0.79 |
and 0.99 mm/day (
The results indicate that there is site specific adaptability of the ETo equations under this study. The Albrecht equation showed the best performance at Morogoro. The Penman equation seems to be non-applicable only at Dodoma, Jomo Kenyatta and Eldoret weather stations, however with site calibration the performance of the Penman equation could be improved and the adjusted Penman equation may be applicable across the study area. The Dalton equation showed its best performance at Eldoret while the Mahringer equation is adapted to the Songea station and the Trabert equation could be used at Songea, Nakuru, Jomo Kenyatta, and Kisumu stations. The Rohwer equation showed the best performance at Eldoret and the Romanenko equation performed satisfactorily at all weather stations, except Tabora and Jomo Kenyatta weather stations. Overall, the best four ETo equations can be ranked as Romanenko, Penman, Trabert and Mahringer with a decreasing performance levels. The Romanenko and Penman equations had almost similar performance in terms of RMSE, MBE and AME (
Reference evapotranspiration is a very important parameter for the hydrological, environmental and agricultural water management and it is much critical under water scarcity and changing climate conditions under semiarid and arid climates. Crop actual evapotranspiration (ETa) is indirectly estimated by the two steps method as a product of daily ETo and the crop coefficients Kc that is dependent on crop type and growth stages, climate, soil type, crop and water management practices and other environmental conditions (ETa = Kc*ETo) [
Locations | Alb | Pen | B-W | Dal | Mah | Mey | Tra | WMO | Pap | Roh | Oud | Rom |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Dodoma | 1671 | −171 | 1997 | 722 | 233 | 512 | 394 | −159 | −697 | 759 | 777 | −29 |
Morogoro | −245 | 99 | 649 | 444 | −355 | 403 | −271 | −511 | −360 | 362 | 919 | 151 |
Songea | 569 | −176 | 1141 | 357 | −78 | 245 | 31 | −375 | −576 | 346 | 639 | −24 |
Kilimanjaro | 833 | 121 | 1712 | 772 | 147 | 642 | 284 | −236 | −414 | 741 | 1037 | 158 |
Tabora | 536 | 156 | 1593 | 755 | 44 | 647 | 178 | −334 | −404 | 701 | 1160 | 291 |
Nakuru | 132 | −150 | 1046 | 344 | −249 | 255 | −137 | −571 | −619 | 297 | 693 | −174 |
Jomo Kenyatta | 345 | −327 | 917 | 171 | −238 | 67 | −136 | −527 | −706 | 159 | 478 | −247 |
Kisumu | 160 | −125 | 1075 | 369 | −222 | 281 | −110 | −546 | −594 | 322 | 718 | −150 |
Eldoret | 170 | −450 | 714 | 21 | −379 | −77 | −282 | −648 | −812 | 8 | 298 | −29 |
mm/day was obtained by the Romanenko ETo estimates. The original Romamenko mass transfer ETo equation can therefore be recommended for ETo estimation at Dodoma area for appropriate and sustainable water management. On the other hand our results suggest, that the Brockamp-Wenner, Albrecht, and the other ETo equations should be avoided when choosing ETo estimation method for the Dodoma area. The Penman equation provided the least annual ETo difference of 99 mm representing a reasonable daily ETo overestimation of 0.3 mm at Morogoro weather station and should be adopted for water management in Morogoro. Similarly, the Trabert and/or the Marhringer equations at Songea, the Marhinger, Penman at Kilimanjaro, the Mahringer equations at Tabora, the Meyer equation at Jomo Kenyatta and the Rohwer, Dalton and/or Romananko equations at Eldoret could be adopted at the respective locations with non-significant overestimation or underestimation of the annual ETo that could be detrimental to water resources management sustainability across Tanzania and the South Western Kenya. The least annual absolute ETo difference was 130 mm at Nakuru and 110 mm at Kisumu which represented 1300 and 1100 m3/ha. Site specific calibration of the best performing ETo equation (Trabert equation) should be performed to improve the performance of the equation under the Kisumu and Nakuru climate conditions. Moreover, other ETo estimation models should be investigated for accurate ETo estimation at Kisumu and Nakuru. Alternatively, the FAO-PM equation or the Valiantzas equation with complete data could be used in place even under limited data conditions as proposed by [
In cases of ETo overestimation, estimated irrigation water requirement becomes tremendously high and which will still increase when taking the low efficiency of the irrigation system into account. The Albrecht, Brockamp-Wenner, Dalton, Meyer, Rohwer and Oudin equation should be carefully investigated and adjusted through proper calibration across Tanzania and Kenya for sustainable water management since considerable discrepancies are revealed under long term water management as presented in
Performance of twelve mass transfer based reference evapotranspiration equations was investigated in comparison to the FAO-PM method for accurate ETo estimation across Tanzania and South Western Kenya and the impact of the use of each method on water management sustainability was assessed. The Albrecht, Brockamp-Wenner, Dalto, Meyer, Rohwer and Oudin ETo equations systematically overestimated the daily ETo at all nine weather stations with relative errors that varied from 34% to 94% relative to the FAO-PM ETo estimates. The Dalton, WMO, and Papadakis ETo equations systematically underestimated the daily ETo at all weather stations. The Penman, Mahringer, Trabert, and the Romanenko equations were revealed to be the best performing equations across Tanzania and the South Western Kenya however, the root mean squared errors were within the range from 0.98 to 1.48 mm/day, which are relatively high and MBE varying from −0.33 to 0.02 mm/day and the AME from 0.79 to 1.16 mm/day. For sustainable water management, The Trabert equation could be adopted at Songea, the Mahringer equation at Tabora, the Dalton and/or the Rohwer equations at Eldoret, the Romanenko equation at Dodoma, Songea and Eldoret. The 15-year absolute cumulative daily ETo differences compared to the FAO-PM ETo estimates were only 99 mm with the Penman equation at Morogogo, 21 mm with the Dalton equation at Eldoret, 44 mm with the Mahringer equation at Tabora, 67 mm with the Meyer equation at Jomo Kenyatta, 31 mm with the Travert equation at Songea, 8 mm with the Rohwer equation at Eldoret, and 29, 24 and 20 mm with the Romanenko equation at Dodoma, Songea and Eldoret, respectively. However, regional or sub-regional calibration of the best performing Penman, Mahringer, Trabert and the Romanenko equation could improve water management in Tanzania and Kenya under the conservative and sustainable agriculture. This study provides a pragmatic solution for the region that can be used as a guide to choose which method(s) would be a reasonable alternative to estimate ETo when all climatic data are not available at particular locations. However, other reference evapotranspiration equations including the radiation based and the combination equations should tested to determine the best alternative ETo equation to the Penman-Monteith equation for sustainable water management in Tanzania and Kenya. Given the current status of weather station networks in the region, the results of this study can enhance crop water use estimation and thus feeds into the decision making process for regional water resources planning by irrigators, water managers and other agricultural professionals.
Djaman, K., Koudahe, K., Sall, M., Kabenge, I., Rudnick, D. and Irmak, S. (2017) Performance of Twelve Mass Transfer Based Reference Evapotranspiration Models under Humid Climate. Journal of Water Resource and Protection, 9, 1347-1363. https://doi.org/10.4236/jwarp.2017.912086