Eleven equations for calculating evaporation were compared with evaporation determined by the energy budget method for Williams Lake, Minnesota. Data were obtained from instruments on a raft, on land near the lake, and at a weather station 60 km south of the lake. The comparisons were based on monthly values for the open-water periods of 5 years, a total of 22 months. A modified DeBruin-Keijman, Priestley-Taylor, and a modified Penman equation resulted in monthly evaporation values that agreed most closely with energy budget values. To use these equations, net radiation, air temperature, wind speed, and relative humidity need to be measured near the lake. In addition, thermal surveys need to be made to determine change in heat stored in the lake. If data from distant climate stations are the only data available, and they include solar radiation, the Jensen-Haise and Makkink equations resulted in monthly evaporation values that agreed reasonably well with energy budget values. Jensen-Haise, cm d -• Makkink, cm d -• Mass transfer, cm d -• Papadakis, cm month -• Penman,* cal cm-2 d-• ß (eo -ea)] PET = (a/(a-1))1.141(7/(s + 7)) ß [(3.6 + 2.5(U3))(e0 -ea) ] PET = [SVP/(0.95SVP + 0.637)] '(Qn -Qx) PET = [0.55(D/12)2(SVD/100)]2.54 PET = {[((0.014Ta) -0.50)(Qs)]0.000673 }2.54 PET: [0.61(s/(s + 7))(Qs/L)] -0.012 cm d -• DeBruin [1978] DeBruin and Keijman [1979] Hamon [1961] McGuinness and Bordne [1972] McGuinness and Bordne [1972] Harbeck et al. [1958] McGuinness and Bordne [1972] Jensen et al. [ 1974] Stewart and Rouse [1976] McGuinness and Bordne [1972] PET = 0.5625[eomax-(e0min -2)] PET: (s/(s + 7))(Q,-Qx) + (7/(s + 7))[(15.36(0.5 + 0.01U2)) '(eo -ea)] PET = a(s/(s + 7))[(Qn -Qx)/L] PET = {[(0.0082Ta) -0.19] -(Q•/1500)}2.54 PET, for periods of 10 days or greater PET, daily PET, daily PET for periods greater than 5 days (Nebraska) PET, monthly (Holland) Evaporation, depending on calibration of N PET, monthly PET, for periods greater than 10 days PET for periods of 10 days or greater PET, monthly (Florida) Here, a = 1.26 is the Priestley-Taylor empirically derived constant, dimensionless; s/(s + 7) and 7/(s + 7) are parameters derived from the slope of the saturated vapor pressure-temperature curve at the mean air temperature; 7 is the psychrometric constant; Qn is net radiation (in calories per square centimeter per day); Q• is solar radiation (in calories per square centimeter per day); Qx is change in heat stored in the water body (in calories per square centimeter per day); U2 and U3 is wind speed at 2 or 3 m respectively, above surface (in meters per second); e 0 is saturated vapor pressure (in millibars); e a is vapor pressure at temperature and relative humidity of the air (in millibars); SVP is saturated vapor pressure at mean air temperature (in millibars per degree kelvin); SVD is saturated vapor density at mean air temperature (in grams per cubic meter); T a is air temperature, in degrees Fahrenheit for the Jensen-Haise and Stephens-Stewart equations; L is the latent heat of vaporization (in calories per gram)...
Evaporation from Williams Lake, computed by the energy budget method for the five open‐water seasons of 1982–1986, varied from a maximum seasonal rate of 0.282 cm/d in 1983 to a minimum seasonal rate of 0.219 cm/d in 1982. The pattern of monthly values of evaporation is not consistent from year to year. The normally expected pattern of low evaporation values in May, followed by increasing values in June to maximum values in July is true for only 3 of the 5 years. Comparison of annual evaporation calculated by the energy budget and mass transfer methods indicates that energy budget values varied from 13% greater to 11% less than mass transfer values. Furthermore, there is no seasonal bias in the pattern. Large differences exist in the magnitude of energy fluxes to and from Williams Lake. By far the greatest energy fluxes, having magnitudes of hundreds of watts per square meter, are incoming solar radiation, incoming atmospheric radiation, and outgoing long‐wave radiation emitted by the lake water. The least energy fluxes are related to advection, which generally have magnitudes less than 5 W m−2.
Best estimates of evaporation at Williams Lake, north central Minnesota, were determined by the energy budget method using optimum sensors and optimum placement of sensors. These best estimates are compared with estimates derived from using substitute data to determine the effect of using less accurate sensors, simpler methods, or remotely measured data. Calculations were made for approximately biweekly periods during five open water seasons. For most of the data substitutions that affected the Bowen ratio, new values of evaporation differed little from best estimates. The three data substitution methods that caused the largest deviations from the best evaporation estimates were (1) using changes in the daily average surface water temperature as an indicator of the lake heat storage term, (2) using shortwave radiation, air temperature, and atmospheric vapor pressure data from a site 110 km away, and (3) using an analog surface water temperature probe. Recalculations based on these data substitutions resulted in differences from the best estimates as much as 89%, 21%, and 10%, respectively. The data substitution method that provided evaporation values that most closely matched the best estimates was measurement of the lake heat storage term at one location in the lake, rather than at 16 locations. Evaporation values resulting from this substitution method usually were within 2% of the best estimates. The energy budget method commonly isused as the independent method for determining a mass transfer coefficient [Anderson, 1954; Harbeck et al., 1958; Gunaft, 1968; Harbeck and Meyers, 1970; Ficke et al., 1976; Sturrock, 1977; Spahr and Ruddy, 1983; Sturrock et al., 1992]. Any uncertainties associated with the energy budget method that are due to inadequate assumptions or inadequate data are propagated to the mass transfer coefficient. Therefore it is important to be aware of the effects of assumptions or data substitutions on the mass transfer coefficient computed with the energy budget technique. The following approaches for determining evaporation by the energy budget method are commonly used when on-site data are not available or when some values cannot be measured directly and/or accurately: (1) using empirical methods to calculate incident solar and/or atmospheric radiation [Ficke, 1972; Kirillova et al., 1973; Richter, 1973; 2473 176 pp., 1967. Keijman, J. Q., The estimation of the energy balance of a lake from simple weather data, Boundary Layer Meteorol., 5, 399-407, 1974. Kirillova, T. V., T. A. Ogneva, and L. V. Nesima, Determination of evaporation from lake and reservoir surfaces using the heat balance method, in Hydrology of Lakes Symposium, Proceedings of the Helsinki Symposium, IAHS Publ., 109, 269-275, 1973. Koberg, G. E., Energy-budget studies, U.S. Geol. Surv. Prof. Pap., 298, 20-29, 1958. Koberg, G. E., Methods to compute long-wave radiation from the atmosphere and reflected solar radiation from a water surface, U.S. Geol. Surv. Prof. Pap., 272-F, 107-136, 1964. Menne, M. J., The spatial and temporal...
Evaporation from Wetland P1 in the Cottonwood Lake area of North Dakota, USA was determined by the energy-budget method for 1982-85 and 1987. Evaporation rates were as high as 0.672 cm day -~. Incoming solar radiation, incoming atmospheric radiation, and long-wave radiation emitted from the water body are the largest energy fluxes to and from the wetland. Because of the small heat storage of the water body, evaporation rates closely track solar radiation on short time scales. The effect of advected energy related to precipitation is small because the water quickly heats up by solar radiation following precipitation. Advected energy related to ground water is minimal because ground-water fluxes are small and groundwater temperature is only about 7 °C. Energy flux related to sediment heating and thermal storage in the sediments, which might be expected to be large because the water is clear and shallow, affects evaporation rates by less than 5 percent.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.