Evaporation at Jasper Ridge Biological Preserve Using Pan Data and Hydrometeorological Estimates
Xiaogang HE1 and David L. Freyberg2
Environmental Fluid Mechanics Laboratory, Department of Civil and Environmental Engineering, Stanford University
I Introduction and Background

III Evaporation Pan Data（2009）

Evaporation is quantity of water evaporated from an open water surface or from the ground.
Estimates both of evaporation from free water surfaces and from the ground are of great importanceto hydrological modeling and in hydrometeorological and agricultural studies. One example is theoperation of the century-old Searsville Reservoir, which is a significant resource for Stanford
University and its Jasper Ridge Biological Preserve (JRBP), but is now nearing the end of its usefullife as a result of continuing sedimentation. For the reason that the evaporation losses fromreservoirs will affect their water storage efficiency, so it is important to have good measurements ofthe evaporation. Scientists have developed several indirect ways to measure evaporation from water
bodies, for example, evaporation pans and some theoretical and empirical equations using
meteorological data from a weather station.

Class A evaporation pans are widely used as the basis for estimating lake evaporation and potential evapotranspiration. Pan
performance is affected by instrumental limits and operational problems such as the thermal properties of the pan, humanerrors, instrumentation errors, turbidity of water, watering of birds or other animals, as well as other maintenance problems,
which can affect the accuracy of evaporation measurements.

V Converting PAR to Solar Radiation
Evaporation
Net Radiation
Incoming — outgoing
Short wave + PAR + Long wave

In the P-M equation, net radiation is needed to estimate evaporation. The full spectrum ofincoming solar radiation contributes to the net radiation that drives evaporation. In some settings,
particularly in agriculture and ecology, deployed radiation sensors measure Photosynthetically
Active Radiation (PAR), which is that portion of the incoming radiation between 400 and 700
nm in wavelength. Thus, there is a need for methods for estimating total solar radiation fromPAR measurements. A number of models have been developed to reduce total solar radiation toPAR in specific locations, but there have been relatively few attempts to develop models toexpand PAR observations to full-spectrum solar radiation. Building on preliminary work by JunYoung Kim in Prof. Freyberg’s group, I have used simultaneous observations of PAR and total
solar radiation for a short time period at the Jasper Ridge site to construct a statistical model of
total solar radiation based on PAR to allow implementation of the Penman-Monteith model overthe much longer time period for which PAR data are available at Jasper Ridge.

Material and Data Analyses
Fig. 5. JRBP Pan Evaporation Raw Data

Fig. 6. JRBP Pan Evaporation Rate estimatedby differencing raw gage height data

Modeling

The data for this study werecollected using pyranometers atthe JRBP. We got the data setof total solar radiation (W/m2)
from 2003-2009 and the data
set of PAR [PhotosyntheticFig. 12. Pyranometer photon flux density (PPFD),(mol/m2/s)] from 1996-2009.

Fig. 3. Jasper Ridge Solar Station

Fractionofdaytimein thewholeyear

Fractionofremoved
PPFD

Fig. 1. Location of Jasper Ridge Biological
Preserve in San Francisco Bay Area

Fig. 13. Cleaning the PAR data in 1996: removing spurious
values based on sunrise and sunset (Pacific Standard Time,no shift for daylight saving time)

Simple regression model
The data set for the year 2003
shows an R2 value for PAR andsolar radiation that is relatively
low. Therefore, the data for2004 & 2005 are used todevelop Model 1 (Fig. 18).
Jasper Ridge is characterized
by strong seasonality, with arainy season from November
to March, and a dry seasonfrom April to October. So aseasonal model, using a dry
season submodel (Model 2D)
and a wet season submodel(Model 2W) is constructed.
(Model 2)

Fig. 7. JRBP Pan Evap Rate smoothed using a 30-min. moving averageFig. 18. Scatter plot for comparison between measured PPFD
and solar radiation for the year 2004&2005 (Model 1)

Multivariable regression model(Using stepwise method)

Regression coefficients

Jasper Ridge Weather Station&Evaporation Pan (Class A Pan)

No. ofvariables

PPFD

Air Tem(mol/m2/s)

(℃)

RH

Fig. 2. Jasper Ridge Biological Preserve

II Objective
The goal of my project is to estimate the evaporation and dosome comparison of the estimated evaporation pan data and thePenman-Monteith (P-M) model. In conducting this assessment, I
restricted my attention to: Data cleaning and data analysis of the evaporation pan data
 Meteorological data analysis: converting PAR(Photosynthetically Active Radiation) to solar radiation
 Penman-Monteith method Field work: data collection

Fig. 14. JRBP Hourly PPFD from 1996 to 2009

Fig. 8. JRBP Pan Evap Rate in July (Movingaverage of cleaned data) Dry season
Fig. 4. Data Collection

In order to compute water evaporation from vegetated surfaces, we use Penman-Monteith equation
which is based on the meteorological data, such as the net solar radiation, air temperature, wind speed,
ET0: grass reference evaportranspiration (mm/h)
relative humidity and air vapor pressure.

37γu 2 (e s − e a )∆ ( Rn − G )273.16+ Ta +ET0λ[∆ + γ (1 + C d u 2 )]∆ + γ (1 + C d u 2 )=Rn 0.77 Rs − Rnl

Jun Young Kim in the EFML constructed the initial PAR model and helped me a lot in my project this summer. Some graduate students have assisted me a lot,including Spencer Sawaske and Bing Wang. My mother also gives me a lot of support when I am abroad. This research is supported by Stanford 2010 SoE UGVR
program.

1. hexg@stanford.edu, 2. freyberg@stanford.edu

10.67445.91280.9672 56.6207

20.7020-2.439430.3168 0.9686

30.6880-5.0154 -129.951

172.6640.6898-4.9392 -129.194 -2.613 173.507 0.9708 53.4564

55.4150.9707 53.5469

We tried using three weather parameters, air temperature, relative humidity
and wind speed to improve estimation of solar radiation. The improvement isslight, so we just consider the simple regression model.

Model Validation
Fig. 9. JRBP Pan Evap Rate in November (Movingaverage of cleaned data) Wet season

Model 1

Fig. 15. Scatter plot for comparison between measured
PPFD and solar radiation for the year 2003

Model 2

Fig. 19. Scatter plot for comparison between measured solar radiation and estimated solar
radiation for the year 2006, using Model 1 & Model 2

Fig. 16.
Comparison ofPPFD and Rs fromJun 10~Jun 14 indry season

Fig. 10. JRBP Cumulative Pan Evap

VI References and Acknowledgements
Freyberg, D.L. and Cophen, PS. (2001). “Maintaining Open Water at Searsville Reservoir.” Unpublished Report to the Packard Foundation.
C.P. Jacovides, F.S. Timvios, et al. (2003). “Ratio of PAR to broadband solar radiation measured in Cyprus.” Agricultural and Forest Meteorology 121: 135-140.
S.O. Udo, T.O. Aro. (1999). “Global PAR related to global solar radiation for central Nigeria.” Agricultural and Forest Meteorology 97: 21-31.
Hossein Tabari, Safar Marofi, et al. (2010)“Estimation of daily pan evaporation using artificial neural network and multivariate non linear regression.” Irrigation
Science 28(5):399-406.

(m/s)

To clean the data we removed data points
suggesting negative evaporation, either
because of data errors or precipitation.

IV Penman-Monteith Model
Rn: net radiation (MJ/m2/h)
Rs: solar radiation (MJ/m2/h)
Rnl: net longwave radiation (MJ/m2/h)
G: soil heat flux density (MJ/m2/h)
Ta: air temperature (oC)u2: wind speed (m/s)
es: saturation vapor pressure (Kpa)
ea: air vapor pressure (Kpa)
γ: psychrometric constant (Kpa/ oC)
λ: latent heat of vaporization (MJ/kg)
Cd: bulk surface resistance and aerodynamic
resistance coefficient

RMSE
Intercept2Wind
Adj R (MJ/m2/h)(MJ/m2/h)
Vel

Fig. 17. Comparisonof PPFD and Rs fromDec 10~Dec 14 inwet season

Missing values from Jun 9 to Jun 22.
Fig. 11. JRBP Daily Pan Evaporation

Wet season

Dry season

Fig. 20. Comparison between Penman-Monteith ET0 estimates using measured solar
radiation and estimated solar radiation for 2006, using Model 1 & Model 2.

Model 2 is better, it does better in the wet season than the dry season,
maximum estimated ET errors are on the order of 1 mm.