- freely available
Water 2016, 8(11), 527; https://doi.org/10.3390/w8110527
2. Materials and Methods
2.1. Study Area
2.2.1. Data Sources
2.2.2. Data Quality Control
2.3.1. Brief Description of the E601 Pan
2.3.2. Derivation of the Model
2.3.3. Inter-Comparisons of Evaporation Models
3.1. Time Series of East Juyan Lake’s Daily Evaporation
3.2. Daily Evaporation Model for EJL
3.2.1. Parameterization and Validation of the Proposed Model
3.2.2. Inter-Comparison of Evaporation Methods
3.3. Estimation of EJL’s Evaporation during 2002–2015
4.1. Floating Pan Evaporation’s Representativeness and Reliability
4.2. Model Sensitivity and Reliability
4.3. Complementary Relationship Theory’s Adaptability in the Hyper-Arid Region
Conflicts of Interest
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|Study||Physical and Climate Settings/Site||Method/Highlights||Key Results|
|Sturrock et al. (1992)||Williams Lake in Minnesota, USA; 0.36 km2; mean depth of 5.2 m; within the continental climate of North America.||Energy budget method; land and raft weather stations are used; each energy budget component is measured or calculated.||1. Seasonal evaporation rate varied from 2.19 mm/day to 2.82 mm/day.|
2. Energy budget values of evaporation varied from +13% to −11% of mass transfer values.
3. Large differences exist in the magnitudes of energy budget components, with solar radiation being the greatest and advection the smallest.
|Winter et al. (1995)||Williams Lake in Minnesota, USA.||11 lake evaporation methods are evaluated; Data are obtained on a raft, bankside and a station 60 km away.||1. Modified DeBruin-Keijman, Priestley-Taylor and Penman equation estimated monthly evaporation that agreed most closely with energy budget values.|
2. Input data measured near the lake is essential to apply these methods, and if only distant data are available, Jensen-Haise and Makkink methods are preferable.
|Valiantzas (2006)||A weather station called Patrai in Greece.||Penman equation; Approximation of the equation’s components enables easy estimation of evaporation with basic data.||1. Penman equation is simplified for routine hydrologic applications and a more simplified version without wind speed is given and tested.|
2. The new open water evaporation formulas were adapted for calculating reference crop ET.
|Rosenberry et al. (2007)||Mirror Lake in New Hampshire, USA; 0.15 km2; annual precipitation and evaporation is 1220 and 490 mm.||15 evaporation methods are applied; BREB method is chosen as the standard; In situ measured data are used.||1. Priestley-Taylor, deBruin-Keijman and Penman method values compared most favorably with BREB’s (within 20% difference).|
2. Methods may be unduly sensitive to wind speed, when wind speed is neither substantially larger nor smaller.
3. Temperature-only methods compared remarkably well with BREB values.
4. Results of this research also serve well in other lakes with a similar physical and climatic setting.
|Masoner et al. (2008)||In a wetland at the U.S. Geological Survey Norman Landfill Research Site in Oklahoma, USA; an area of 8800 m2.||In situ measurement; Monthly evaporation data from Class A pan and floating pan are used.||1. Floating pan can better simulate actual physical conditions on the water surface than evaporation pan on land.|
2. Floating pan to land pan ratios varied in different months, ranging from 0.69 to 0.87.
|Vercauteren et al. (2009)||Lake Geneva (LG) in Switzerland; 582 km2; wind speed was smaller than 10 m/s and waves rarely exceeded 20 cm.||Bowen ratio; sensible heat (H) and basic synoptic data are used; Eddy covariance method is used.||1. A new method to estimate wet surface evaporation using easily obtained data was proposed based on Bowen ratio equation, avoiding the term of G in the energy budget that is hard to measure on wet surfaces.|
2. Validation by eddy covariance values indicated excellent accuracy when applied to LG.
|Granger and Hedstrom (2011)||Three lakes with fetch distances ranging from 150 m to 11,000 m in Canada.||Direct measurement of eddy covariance method; wind speed, air temperature and humidity data are measured on the lake.||1. Wind speed is the most significant factor governing lake evaporation, followed by land-water contrast of temperature and vapor pressure, while net radiation bears no relationship with lake evaporation in short time periods.|
2. Relationships were developed between the hourly rates of lake evaporation and the following significant variables and parameters, and a versatile model for estimating hourly lake evaporation rate is proposed with good accuracy.
|McGloin et al. (2014)||A reservoir in southeast Queensland, Australia; 0.17 km2; seasonal subtropical climate.||Floating weather station; hourly latent flux measured by eddy covariance; Mass transfer method; the hydrodynamics model-DYRESM.||1. Various modelling methods were used to estimate hourly latent heat fluxes and the theoretical mass transfer model performed the best, the Granger and Hedstrom the worst.|
2. Estimates by the DYRESM model tended to be greater than measured values.
3. Improvements can be made to the traditional mass transfer and DYRESM models for many applications, including modelling the effects small lakes have on regional weather.
|Shilo et al. (2015)||Lake Kinneret in northern Israel; 168.7 km2; annual evaporation of 1400 mm.||Aerodynamic method and energy balance method; Synoptic factors are measured at the station located near the center of the lake.||1. The primary factors determining lake evaporation are solar radiation, near surface air and water temperatures, relative humidity, wind speed and near surface atmospheric stability.|
2. Lake evaporation during exceptionally hot summer days was lower than normal when wind speed was much lower.
3. The weakening of the permanent synoptic Etesian winds and the descent of the marine inversion to a height of the topographic ridge are responsible for the reduction of the Mediterranean Sea Breeze and evaporation.
|Ma et al. (2016)||Nam Co Lake in the Tibetan Plateau; closed, semi-brackish lake; 2013 km2; a semi-arid sub-frigid monsoon climate.||The China Meteorological Forcing Dataset was used; E601 pan evaporation data was used to compare with CRLE model estimations without wind speed data.||1. The complementary relationship lake evaporation (CRLE) model behaved well in Nam Co Lake without wind speed input, but implicitly considered the wind effect via vapor transfer coefficient.|
2. From 1979 to 2012, the mean annual evaporation of Nam Co Lake is 635 mm, and the annual evaporation of Nam Co Lake expressed a very slight decreasing trend.
3. Evaporation decreasing was responsible for 4% of recent rapid Nam Co Lake expansion.
|Liu et al. (2016)||East Juyan Lake, northwestern China; 40 km2; in the hyper-arid region with ET0 > 1400 mm and annual P of 36.6 mm.||Floating E601 pan; bankside weather station; a semi-empirical daily lake evaporation model is proposed.||1. Total lake evaporation during the growing season was 1183.3 mm according to the floating evaporation pan on EJL, with the max, min and mean daily E of 8.1, 3.7 and 6.5 mm, respectively.|
2. A new daily lake evaporation model, derived from Dalton model, was proposed with simple inputs (Ta, u and RH) but moderate modeling accuracy.
3. The lower Heihe River’s ET0 represented by annual open water evaporation was 1471.3 mm.
4. Trend analysis of estimated evaporation proved evaporation paradox’s existence in this hyper-arid region and validated the complementary relationship theory’s adaptability.
|Index||R2||MAE (mm)||RMSE (mm)||MRE||Estimation Quality|
|Within ±5%||Within ±10%||Within ±20%||Within ±30%|
|Method||R2||MAE (mm)||RMSE (mm)||MRE|
|Air temperature||Y = 1.0256X − 0.8813||0.991|
|Wind speed||Y = 1.060X + 0.093||0.683|
|Relative humidity||Y = 0.939X + 7.340||0.883|
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