Evaporation from a small water reservoir: Direct measurements and estimates

doi:10.1016/j.jhydrol.2007.12.012

Journal of Hydrology

Volume 351, Issues 1–2, 30 March 2008, Pages 218-229

https://doi.org/10.1016/j.jhydrol.2007.12.012 Get rights and content

Summary

Knowing the rate of evaporation from surface water resources such as channels and reservoirs is essential for precise management of the water balance. However, evaporation is difficult to measure experimentally over water surfaces and several techniques and models have been suggested and used in the past for its determination. In this research, evaporation from a small water reservoir in northern Israel was measured and estimated using several experimental techniques and models during the rainless summer. Evaporation was measured with an eddy covariance (EC) system consisting of a three-dimensional sonic anemometer and a Krypton hygrometer. Measurements of net radiation, air temperature and humidity, and water temperature enabled estimation of other energy balance components. Several models and energy balance closure were evaluated. In addition, evaporation from a class-A pan was measured at the site. EC evaporation measurements for 21 days averaged 5.48 mm day⁻¹. Best model predictions were obtained with two combined flux-gradient and energy balance models (Penman–Monteith–Unsworth and Penman–Brutsaert), which with the water heat flux term, gave similar daily average evaporation rates, that were up to 3% smaller than the corresponding EC values. The ratio between daily pan and EC evaporation varied from 0.96 to 1.94. The bulk mass transfer coefficient was estimated using a model based on measurements of water surface temperature, evaporation rate and absolute humidity at 0.9 and 2.9 m above the water surface, and using two theoretical approaches. The bulk transfer coefficient was found to be strongly dependent on wind speed. For wind speeds below 5 m s⁻¹ the estimated coefficient for unstable conditions was much larger than the one predicted for neutral conditions.

Introduction

One of the major problems in the management of surface water resources, such as water reservoirs or channels, is the estimation of all water budget components. Such knowledge is necessary for regulating water supply in response to demand, for identification and estimation of possible percolation through the reservoir or channel floor and for chemical analysis of reservoir water. In water reservoirs, for example, it may be relatively easy to measure inflow, outflow and precipitation. However, to identify whether percolation through the reservoir floor takes place, water balance closure is necessary which requires an accurate estimate of the evaporation losses. Therefore, evaporation from water bodies has been the topic of many studies throughout the years (see Lenters et al., 2005, and references therein).

A common and relatively simple approach for estimating evaporation is the measurement of standard meteorological parameters (net radiation, air temperature and humidity and wind speed) and the use of Penman (Penman, 1948) or Priestley–Taylor (Priestley and Taylor, 1972) equations (Brutsaert, 1982). The Penman equation or its Penman–Brutsaert variation (Brutsaert, 1982, Katul and Parlange, 1992) is an analytical solution of the combined heat and mass transfer and energy balance equations for a wet surface. Penman derived constants for water bodies based on research in lakes. However, to solve this equation, information is needed not only on the external meteorological conditions but also on the heat storage within the water body, which requires temperature profile measurements within the water (Stanhill, 1994). If the air has been in contact with the water surface for a very long fetch, equilibrium evaporation may be assumed where the air is saturated with water vapor (Brutsaert, 2005). The Priestley–Taylor equation utilizes equilibrium evaporation as the basis for an empirical relationship describing evaporation from a wet surface under minimal advection. For this case the Penman equation is simplified and only the radiative term is preserved and multiplied by an empirically determined constant. There are many other equations used to estimate evaporation. Warnaka and Pochop (1988) analyzed six different evaporation equations and Winter et al. (1995) evaluated 11 equations for estimating evaporation from a small lake in the north central Unites States. The latter study showed that three equations, i.e., modified DeBruin–Keijman, Priestley–Taylor and modified Penman, resulted in monthly evaporation values that agreed most closely with energy budget values.

Evaporation can be measured directly with the eddy covariance (EC) technique, where fluctuations of vertical velocity and vapor density are measured at high frequency. If applied under certain limitations, this technique is typically considered today as the most reliable and accurate for evaporation estimation (Itier and Brunet, 1996). In Israel, EC was used to measure evaporation from Lake Kinneret (Assouline and Mahrer, 1993, Assouline, 1993) with a measuring station situated about 200 m offshore, east of the western coast of the lake. EC has been used for evaporation measurements from other lakes and reservoirs (Sene et al., 1991, Stannard and Rosenberry, 1991, Allen and Tasumi, 2005). EC can also measure the sensible heat flux, a major component of the energy balance, and thus can give insight into the dynamics of the Bowen ratio (i.e. the ratio of sensible to latent heat flux), which is widely used in evaporation estimates.

Although some work has been published on evaporation from lakes and large water reservoirs, it appears that evaporation from small reservoirs has not been measured with EC techniques before and that current estimation techniques may not provide detail of the same order of magnitude as that of current monitoring of inflow and outflow rates of small reservoirs. Thus, the major goal of the present research was to determine evaporation from such a reservoir and to compare several determination approaches: the direct eddy covariance measurement technique, standard class-A pan measurements and evaporation models based on flux-gradient, energy balance, and mass transfer approaches.

Section snippets

Evaporation models

Several models were employed to estimate evaporation and compare with the direct measurements of the EC system. One type of model tested is based on a combination of the energy balance principle and the flux-gradient approach. Models of this type can be represented by (Brutsaert, 1982, Monteith and Unsworth, 1990): $λ E = \frac{Δ}{Δ + γ^{*}} (Rn - G) + \frac{γ^{*}}{Δ + γ^{*}} E_{A}$ where λE (W m⁻²) is the latent heat flux, Δ (Pa K⁻¹) is the rate of increase of saturation pressure with temperature, Rn (W m⁻²) is net radiation, G (W m⁻²) is the

Site description and methods

The research was carried out at the Eshkol reservoir (Bet-Netofa valley in northern Israel, 32°46’N; 35°15’E, 145 m.a.s.l), a part of the National Water Carrier system operated by Mekorot, the Israel National Water Company. This is a settling reservoir which receives water intermittently, by gravity flow in an open channel, from another upstream reservoir of the same system, the Tsalmon reservoir. Eshkol is a square reservoir with a 600 m side and 3.5 m depth. Water flows out of this reservoir

Data processing

The logarithmic wind profile in a neutral atmosphere is given by: $u (z) = \frac{u_{*}}{k} \ln (\frac{z - d}{z_{0 m}}),$ from which the friction velocity can be extracted: $\frac{u_{*}}{k} = u (z)/ \ln (\frac{z - d}{z_{0 m}}) .$ Appropriate values for d and z_0m were used, based on the surface characteristics. For water surfaces it is common to use d = 0 because if the water surface at dead calm is taken as the reference, then wave crests caused by wind are offset by the troughs in between; i.e. the average height of the surface is unchanged. The roughness length used here was

Turbulence characteristics of the boundary layer above the water surface

Friction velocity $(u_{*})$ calculated from measured wind components by Eq. (4.5) is compared in Fig. 1 with that calculated for the logarithmic wind profile model (Eq. (4.2)). The roughness length in the logarithmic model was estimated with Eq. (4.3). Linear regression of the 1176 data points yielded the relation: $u_{* meas} = 0.84 u_{* mod} + 0.059 (R^{2} = 0.7)$ Thus, reasonable agreement was obtained between measured and predicted data, which suggests that a logarithmic wind profile is common above the water surface.

Discussion

Direct measurements of evaporation from a small reservoir were carried out using an eddy covariance system deployed at the center of the reservoir. The system consisted of a three-dimensional sonic anemometer and a krypton hygrometer. Some of the measurements were compared with daily evaporation data measured by a class-A pan on the reservoir’s bank. Additional sensors were deployed to measure other components of the energy balance, i.e. sensible heat flux, net radiation and water heat flux and

Conclusions

Evaporation rates from a small water reservoir were obtained by direct eddy covariance measurement and estimated using several models. The following main conclusions can be drawn from this study:

1.
Best agreement between predicted evaporation and direct measurements was obtained using the models with elaborated wind functions (PMU and PB) as compared to the models with simplified wind functions (P, PDP) or a constant wind contribution (PT). Including the water heat flux term improved the agreement

Acknowledgements

The authors thank Shlomo Shoshani and the staff of the Watershed Unit of Mekorot Ltd., for their collaboration. The authors thank an anonymous reviewer whose comments improved the original version of this paper considerably. This research was funded by Mekorot, The Israel National Water Company, under research Contract No. 304-0333-05. This support is gratefully acknowledged.

Contribution of the Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization, No. 605/07.

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Evaporation from a small water reservoir: Direct measurements and estimates

Summary

Introduction

Section snippets

Evaporation models

Site description and methods

Data processing

Turbulence characteristics of the boundary layer above the water surface

Discussion

Conclusions

Acknowledgements

J. Hydrol.

J. Hydrol.

J. Hydrol.

J. Hydrol.

Agr. Forest Meteorol.

J. Hydrol.

Agr. Forest Meteorol.

Estimation of lake hydrologic budget terms using the simultaneous solution of water, heat and salt balances and a Kalman filtering approach – application to Lake Kinneret

Water Resour. Res.

Evaporation from Lake Kinneret. A. Eddy correlation system measurements and energy balance estimates

Water Resour. Res.

Evaporation into the Atmosphere: Theory, History and Applications

Hydrology: An Introduction

Wind stress on a water surface

Quart. J. Roy. Meteor. Soc.

A note on empirical wind-wave formulae

Quart. J. Roy. Meteor. Soc.