Feld Washington 0250O 10900

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Representing atmospheric moisture content in the mountains: Examination using distributed

sensors in the Sierra Nevada, California

Shara I. Feld

thesis

submitted in partial fulfillment of the

re!uirements for the degree of

"aster of Science

#niversit$ of %ashington

&'(&

Committee:

)essica *. +und!uist

lan F. amlet

-rogram uthori ed to /ffer *egree:

Civil and Environmental Engineering


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#niversit$ of %ashington

Abstract

Representing atmospheric moisture content in the mountains: Examination using distributed

sensors in the Sierra Nevada, California

Shara I. Feld

Chair of the Supervisor$ Committee:

*r. )essica *. +und!uist

Civil and Environmental Engineering

tmospheric moisture content is a critical factor in both the 0ater balance and the energ$ balance for a river basin. *espite its importance to h$drolog$, atmospheric moisture is sparsel$

measured, particularl$ in the mountains. Since fe0 observations exist, numerous empirical

methods have been developed to estimate relative humidit$ 1R 2 or the de0point temperature.

o0ever, most of these algorithms 0ere developed in continental regions and ma$ have limited

accurac$ outside the region 0here the$ 0ere developed. Furthermore, future changes in

atmospheric moisture content ma$ reduce our abilit$ to rel$ on empiricall$ determined

relationships. lternative options include installing more in situ sensors, loo3ing at nearb$ free

air measurements, and4or running a numerical 0eather model.


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%e compared densel$5distributed measurements of de0point temperatures in t0o stud$

sites over three $ears in a semi5arid, maritime mountain range 1Sierra Nevada, California2

against: 1(2 simple empirical algorithms, 1&2 the -arameter5elevation Regressions on IndepenSlopes "odel 1-RIS"2 linear regression data sets based on observational data, 162 the %eather

Research and Forecasting 1%RF2 mesoscale model, and 172 radiosonde data. Empirical

algorithms that used onl$ one sea5level measurement of de0point to extrapolate to higher

elevations, on average overestimated moisture in the basin, displa$ing median biases of dail$

de0point temperatures up to ('.89C. hese algorithms 0ere sub;ect to errors both from

misrepresenting the linear rate of moisture loss 0ith elevation and, on some da$s, from assumin

the de0point temperature follo0ed a linear pattern at all. hese methods used assumptions that

0ere empiricall$5derived in other climates. -RIS" improved upon these methods b$ using local

observations to determine the local average lapse rate, 0ith median bias values of 5'.69C and

&.&9C in our stud$ sites.

Empirical algorithms that derived de0point from air temperature sho0ed a significant

seasonal variation in performance. ssuming uniform advection of moisture from the -acific

does not capture the moisture d$namics in the Sierra Nevada. Radiosonde readings sho0ed larg

biases from observations, and a 0ide range of da$ to da$ error. %RF improved on the free5air

data, performing 0ell in representing both the overall trends in the basin 10ith median biases of

5'.


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ACKNOWLEDGEMENTS

I 0ould li3e primaril$ to than3 m$ thesis advisor *r. )essica +und!uist for her guidance. I amfortunate to have received the opportunit$ to 0or3 0ith such an excellent mentor. In addition I

0ould li3e to than3 m$ committee member *r. lan amlet. Contributions to this 0or3 came

from Nicoleta Cristea in calibrating and running the * S=" model, "ar3 Raleigh and

Courtne$ "oore for help 0ith instrument deplo$ment and retrieval, Nic %a$and and "imi

ughes for help 0ith %RF data ac!uisition and processing and the #% mountain h$drolog$

research group for ans0ering research !uestions and paper revisions. Funding 0as provided b$

NSF through grant number CAE 5'B6B( , and b$ N/ through their $drometeorological

estbed and through the )oint Institute for the Stud$ of the tmosphere and /cean 1)IS /2

under N/ Cooperative greement Nos. N (DR)(&6& and N ('/ R76&'(7B. I 0ould

finall$ li3e to than3 m$ famil$ for their encouragement.


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TABLE OF CONTENTS

-age

+ist of Figures ..................ii

+ist of ables .. .....iii

(. Introduction ..............(&. Aac3ground: "etrics of tmospheric %ater ..... . ..86. "ethods ....................................B

6.(. Stud$ rea and *ata Sources ....................................B6.&. "ethods of Estimating *e0 -oint emperatures .. ...(&

6.&.(. Empirical lgorithms: -ro;ecting *e0point From a Aase Station (&6.&.& Fitting +apse Rates 0ith +ocal *ata: -RIS" .....................(76.&.6. Empirical lgorithms: Estimating *e0point From ir emperature lone.........(86.&.7. Free5air =ariations: Radiosonde *ata ....(6.&.8. -h$sicall$5based Free5air =ariations: %RF ...(D

6.6. $drologic "odel .....(B6.7. echni!ues for ssessing /bserved *e0point -atterns ......(osemite . .................................................................. &68. "odel performance histagrams............................................................................ &

. Aox plots of overall model performance.............................................................. &osemite National -ar3. %atersheds that arehighlighted include the North For3 merican River Aasin in the RA, the uolumne inthe >osemite area, and the #pper uolumne River Aasin above igh0a$ (&'. helocations of permanent meteorological to0ers 1 " stations in the RA and C*ECstations in >osemite2 and temporar$ sensors 1iAuttons in the RA and obos in>osemite2 are sho0n. he *ana "eado0s station 1* N2, used for h$drologic modelingimpacts 1described in section 6.62 is sho0n.


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Tab!e 1. /bservational *ata

Region "easurement -eriod of record10ater $ears2

Count ofstations

Elevation Range1m2

ARB $drometeorologicalestbed Stations

&''B L &'(' (8 D5&(''

ARB $grochron iAuttons &''B5&'(' 7 7&75&7&osemite area.* S=" is a ph$sicall$5based distributed h$drolog$ model that re!uires inputs of air

temperature, relative humidit$, 0ind speed, incoming short0ave and long0ave radiation and

precipitation at a three5hourl$ time step GWigmosta et al. , (


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6.7. echni!ues for ssessing /bserved *e0point -atterns

%e loo3 at observed de0point temperature trends and concurrent meteorolog$ to evaluate

reasons behind the performance of the models. o assess the fre!uenc$ of da$s 0ith and 0ithoua linear pattern, 0e calculated a best fit line bet0een dail$ de0point temperatures and elevation.

he root mean s!uared error 1R"SE2 bet0een observations and this best5fit line defines the

amount of scatter from a linear lapse rate, 0here a lo0 R"SE is a relativel$ linear de0point

lapse rate 0ith elevation 0hile a high R"SE sho0s scatter in de0point temperatures 0ith

elevation.

he impact of precipitation on de0point temperature trends 0as determined b$ appl$ing a

binar$ to total dail$ precipitation. %e assessed 0hen measured precipitation in a da$ 0as greate

than ero, as opposed to recording no precipitation at the permanent station.

o assess the influence of 0ind patterns 0e loo3ed at the N/ NCE-4NC R reanal$sis

data sets Ghttp:44000.esrl.noaa.gov4psd4data4composites4da$4 ccessed &D )ul$ &'(&H. h

can be used to build composite data sets to see large5scale meteorological patterns. =ector 0indcomposites 0ere created at the B8' mb and D'' mb geopotential levels to illuminate 0ind

patterns affecting the RA and >osemite area respectivel$. hese composites 0ere created for

da$s 0ith R"SE values Q(9C, (9C to &9C and 69C.


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7. Results

7.(. Case stud$ of Estimated *e0point emperatures in the Sierra Nevada

%e illustrate the performance of methods of generating de0point data 0ith a case stud$

in the RA 1Fig. 62 and >osemite 1Fig. 72. ere 0e sho0 modeled de0point temperatures

plotted against elevation. In both basins t0o da$s are sho0n, one da$ 1left column2 0ith a stron

linear trend bet0een de0point temperature and elevation, 0here better performance of the

algorithms is expected, and one da$ 1right column2 0ith a 0ea3 trend bet0een de0point

temperatures and elevation.

"ethods that estimate de0point temperatures from one point measurement in the basin

1Fig. 6 , Fig. 7 2 perform 0ell 0hen there is both a linear trend of observed de0point

temperatures in the basin, and the algorithm lapse rate matches that trend. Aoth the assumption

of a constant mixing ratio 0ith elevation GCramer , (< (H and of the almost e!uivalent 5(.&89C

3m5( lapse rate G Franklin , (


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stations, modeled data 0ill be sub;ect to compounded errors 0hen de0point temperatures are

pro;ected across the basin. Radiosonde data do not al0a$s capture the moisture variations in th

basin, indicating that humidit$ in the mountains cannot be 0ell5predicted b$ the vertical structuof humidit$ atmosphericall$ upstream 1Fig. 6C, Fig. 7C2. he %RF mesoscale model

GSkamarock and Klem) , &''BH is sho0n to perform 0ell in both cases in the RA and >osemite.


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Figure -. Case stud$ in the merican River Aasin of estimated de0point temperatures on a da$sho0ing a linear trend in de0point temperatures 0ith elevation 1left column2 and a da$ sho0inga 0ea3 trend of de0point temperatures 0ith elevation. /bserved de0point temperatures 1 *2and air temperature 1 2 are sho0n. 1 2 Estimation of de0point temperature from theSacramento 1S C2 airport station 1constant mixing ratio 0ith elevation GCramer , (< (H, 5(.&89C3m5( lapse rate G Franklin , (


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Figure /. s in Fig. 6, except for >osemite area.


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7.&. -erformance of "ethods of enerating *e0point emperatures in the Sierra Nevada

Figure 8 illustrates the performance of methods of generating de0point temperatures 1a2

0ithin the RA for 0ater $ears &''B5&'(' and 1b2 0ithin the >osemite area for 0ater $ears&''65&''8, using histograms of the bias bet0een dail$5averaged modeled data and dail$5

averaged observations. Aiases are calculated from the period for /ctober through )une to match

the %RF period of record.

%hen one measurement of de0point temperature is available 0ithin a basin, empirical

methods of extrapolation across a basin are dependent on both the choice of the base station, an

0hether the modeled lapse rate fits the observed lapse rate. *ue to the t$picall$ limited

availabilit$ of stations measuring de0point temperatures in mountain regions, 0e used the

Sacramento airport station to represent available data. his station is near sea5level, an elevatio

belo0 all stations 0ithin the basin. ssuming a constant mixing ratio 0ith elevation GCramer ,

(< (H results in a 0et bias, 0ith a median bias value of 7.D9C in the RA and ('.89C in the

>osemite area. he assumption of a Ll.&89C 3m5(

de0point temperature lapse rate from the basestation G Franklin , (osemite area. ssumptions of constant R

GWigmosta and Vail , (


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Since -RIS" data G #al& et al. , &''BH is produced in monthl$5average maps, 0e compare

these data to monthl$5averaged de0point temperatures. /n this course resolution, the -RIS"

data performs better than other empirical techni!ues in the RA. Figure 7 sho0s a median biasof 5'.6 in the RA and 6.79C in the >osemite area.

%hen no measurements of de0point temperature are available, the bias in de0point

temperatures estimated from the air temperatures depends on the overall aridit$ of the basin.

ssuming that the de0point temperature is e!ual to the nighttime minimum temperature

G Running et al. , (osemite area.he %RF model GSkamarock and Klem) , &''BH resolves atmospheric ph$sics and

d$namics, and matches observed de0point temperatures in both basins 0ell, 0ith a median bias

of 5'.


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Figure . -erformance of methods of generating de0point temperatures 0ith dail$5averaged datain 1a2 the RA and 1b2 the >osemite area. "ethods of spatiall$5extrapolating de0point from onmeasurement in the basin include constant mixing ratio 0ith elevation GCramer , (< (H, 5(.&89C3m5( lapse rate G Franklin , (


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he inter!uartile ranges highlight li3el$ errors in performance be$ond the median bias.

Figure sho0s a boxplot of the bias bet0een monthl$5averaged estimated data and monthl$5

averaged observations, and bet0een dail$5averaged estimated data and dail$5averagedobservations 1a2 0ithin the RA for 0ater $ears &''B5&'(', /ctober through )une data 1to match

the %RF period of record2, and 1b2 0ithin the >osemite area for 0ater $ears &''65&''8, /ctober

through )une data.

Smaller inter!uartile ranges of monthl$5averaged de0point bias indicate consistent mode

biases on average, 0ith a range of performance bet0een stations. o0ever, the larger

inter!uartile range in dail$5averaged de0point bias indicates that there is significant variabilit$ i

moisture trends both on a dail$ basis, and bet0een stations in this stud$ location. o highlight

this error, 0e consider that 0hile the Kunkel G(


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Furthermore, the performance of the algorithms and models varies seasonall$. Figure D

sho0s the biases in dail$5averaged modelled data for three seasons: the 0inter 1*ecember

through Februar$2, spring 1"arch through "a$2 and summer 1)une through ugust2. From this0e can observe that biases increase during the summer, dramaticall$ so in the case of radiosond

data. he >osemite area sho0s a larger amount of seasonal variation than the more

geographicall$ simple RA.


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Figure ). Aias bet0een generated de0point temperatures and observations in 1 2 the mericanRiver Aasin, monthl$5averaged data, 1A2 the merican River Aasin, dail$5averaged data, 1C2>osemite, monthl$5averaged data, 1*2 >osemite, dail$5averaged data. "ethods sho0n include pro;ections from the Sacramento irport 1constant mixing ratio 0ith elevation GCramer , (< (H, 5(.&89C43m lapse rate G Franklin , (


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Figure 0. Aias bet0een methods of estimating de0point temperature and observations in themerican River Aasin and >osemite 0ith dail$5averaged data during the 0inter 1*ec5Feb2

1 ,*2, spring 1"ar5"a$2 1A,E2 and summer 1)un5 ug2 1C,F2. "ethods sho0n include pro;ections from a lo0 elevation base station 1constant mixing ratio 0ith elevation GCramer ,(< (H, 5(.&89C 3m5( lapse rate G Franklin , (


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7.6. Factors that ffect Estimation of *e0point emperatures in the Sierra Nevada

s illustrated in Figures 6 and 7, the observed de0 point temperature patterns can var$

dramaticall$ from one da$ to the next, so 0e investigated the mean patterns and 0hich 0eather patterns lead to deviations from this mean. veraged annuall$, moisture declined linearl$ 0ith

elevation in the Sierra Nevada. "edian de0point temperature changed 58.D9C 3m5( during da$s

0ith precipitation and 58.(9C 3m5( during da$s 0ithout precipitation averaged over 0ater $ears

&''B through &'(' in the RA. "edian de0point temperature changed 5D.&9C 3m5( during da$s

0ith precipitation and 5 .osemite area. For reference, the average annual air temperature

displa$ed lapse rates bet0een 5 .6 9C 3m5(in the RA, and 5 .79C 3m5( up the 0est slope of

>osemite. *uring the summer, moisture changes 0ith elevation 0ere smaller than in the 0inter.

In both regions, de0 point temperature lapse rates are on average &.89C 3m5( less during the

summer than the 0inter. his means that there is a smaller moisture decline 0ith increases in

elevation in the summer.%hile monthl$ and annuall$5averaged de0 point temperatures varied linearl$ 0ith

changes in elevation, at shorter time periods de0 point temperatures often did not sho0 a linear

pattern. %e determined the fraction of time a lapse rate 0as a good description of the observed

pattern b$ calculating the R"SE of the observations to the best fit line through those

observations, for the RA, the total >osemite area, and the >osemite stations above (8''

meters. In the RA, de0point temperatures generall$ follo0ed linear trends 0ith elevation, 0ith

R"SEs less than (9C during 68.8@ of the stud$ period. In the >osemite area, R"SEs this small

onl$ occurred during ( .8@ of the stud$ period. %hile da$s 0ith R"SEs &9C occurred ('.6@

of the stud$ period in the RA, the$ occurred &8.(@ of the stud$ period in the >osemite area.


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*a$s 0ith R"SEs 69C occurred during &@ of the stud$ period in the RA, and @ of the stud

period in the >osemite area. =isual inspections of plots of elevation vs. de0point temperature

displa$ a brea3do0n bet0een the de0point trends at higher and lo0er elevation stations in the>osemite area 1Fig. 72. his is illuminated b$ comparing the R"SEs of the linear fit of all

stations in the >osemite area 0ith that calculated ;ust for stations above (8'' meters. %hen the

anal$sis 0as restricted to stations above (8'' m, R"SE values of less than (9C occurred during

&8@ of the stud$ period, an improvement over the previous inclusion of lo05elevation stations

%e examined ho0 0ell the data fit a linear approximation as functions of precipitation

1Fig. B 2, average relative humidit$ 1Fig. BA2, and dominant 0ind direction 1not sho0n2. *

0ith rain had smaller R"SE values 1Fig. B 2, 0ith median values of '.D9C in the RA 1&86

observations2 and '.osemite 1( < observations2 as compared to da$s 0ithout rain,

median values of (.69C in the RA 1B&' observations2 and (. 9C in >osemite 1osemite2 1Fig. BA2. In both the RA and >osemite, da$s 0ith a good linear fit bet0een

de0point temperatures and elevation 1R"SE Q(9C, 6< da$s in the RA, (B( da$s in >osemite2

occurred in con;unction 0ith strong 0esterl$ 0inds, 0hile da$s 0ith a 0ea3 linear fit bet0een

de0point temperatures and elevation 1R"SE 69C, ((6 da$s in the RA, &D8 da$s in >osemite

occurred during either 0ea3 0inds or 0inds off the desert from the east.


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Figure ,. 1a2 R"SE from a linear de0point temperature lapse rate on da$s 0ith and 0ithout rainin the RA and >osemite area. 1b2 R on da$s 0ith linear de0point lapse rates 0ith gains inelevation 1R"SE Q(9C2, to 0ea3 linear trends 0ith elevation 1R"SE &9C2 in the RA and>osemite.


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7.7. Impacts on $drolog$

%e demonstrate the impact of de0point temperature errors of ?&9C on sno0

disappearance date and annual streamflo0 0ith the * S=" model calibrated to the #pperuolumne River Aasin 1above igh0a$ (&'2 during the D5$ear period during 0ater $ears &''6 t

&''


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effects on streamflo0 and sno0 disappearance date. Since de0point estimation errors increased

during the summer, basins 0ith h$drolog$ driven in a larger part b$ evaporation 0ould see

increased model error.

Figure (. 1 2 verage annual long0ave radiation, latent heat fluxes and calculated sublimation, baseline values and de0point changes of ?&9C.1A2 imeseries of S%E at the *ana "eado0ssno0 pillo0 and 1C2 annual streamflo0 in the uolumne River above igh0a$ (&' 0ith a ?&9Cchange in de0point temperature.


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Tab!e /. verage annual changes in long0ave radiation, latent heat fluxes, dail$ sublimationrates, streamflo0 in the "(&' basin and sno0 disappearance date shift in the upper uolumnemeado0s in >osemite 0ith a ?&9C change in de0point temperature. Sublimation rates are presented for the ablation season.

De23#i&t C4a&ge 5 6C 7 6C

+ong0ave change &.7 % m5 5&.6 % m5

Sublimation change 5B.( cm $ear 5 .( cm $ear 5

Sno0 disappearance date,*ana "eado0s

6 da$s earlier 6 da$s later

Net annual uolumne Riverstreamflo0

(.6@ 5(.&@


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8. Summar$ and *iscussion

%e tested de0point estimation methods in the merican River Aasin and in the >osemite

National -ar3 area in the Sierra Nevada. Empiricall$5derived lapse rates are t$picall$ used toextrapolate one lo05elevation de0point measurement through the basin. Errors resulted 0hen

the lapse rates did not follo0 the moisture trends 0ithin the basin. Aoth the Franklin G(


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summer, both methods had 0et biases in the RA 1as large as a B.(9C median bias for the

Running et al." G(osemite. h

Kimball et al." G(osemite, Fig. D2. *uring the summer, high pressure and an inversion are common

over California, and air is not 0ell5mixed bet0een the -acific and the Sierra Nevada. lso,

transpiration li3el$ increases near5surface moisture relative to the free air at this time of $ear.%RF, 0hich used a reanal$sis product based on the /a3land sounding data for boundar$

conditions, greatl$ improved on the free5air data, performing 0ell in representing both the

overall trends in the basin 10ith median biases of 5'.


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his ma$ be due to lo0er level air being bloc3ed and channeled into a mountain5parallel barrier

;et G $arish , (


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separation sets up moisture influences at higher elevations that differ from lo0er elevations,

resulting in a brea3 in the linear lapse rate. hus in complex terrain, significant improvements

these modeling representations can be made b$ running %RF, 0hich captures these d$namics, o b$ including enough higher5elevation base stations to resolve the observed changes in the de0

point lapse rate.

%e tested the effects of de0point estimation errors of ?&9C on streamflo0 simulations in

a high elevation basin 1 & '' m2 0ithin our >osemite stud$ area. Aecause this area is sno0mel

driven, the primar$ impacts of de0point errors 0ere on the sno0pac3 simulation. igher

de0points increased estimates of do0n0elling long0ave radiation 1from the higher moisture

content and hence, emissivit$, of the atmosphere2 and decreased modeled sublimation 1b$

decreasing the vapor pressure deficit2, 0hich in turn, resulted in less cooling from the

accompan$ing latent heat flux. he net effect 0as an increase in melt rates and a shift in

streamflo0 timing to0ards earlier in the $ear 1Fig.


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estimating de0point temperatures increased in the summer for most methods 1Fig. D2, and thes

errors are li3el$ to have broader reaching impacts than those 0e illustrated here for sno0.


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. Conclusions

In sum, our results indicate that 1(2 empirical assumptions calibrated for other stud$ site

ma$ not be appropriate in the Sierra Nevada, 1&2 the assumption of a linear trend of de0pointtemperatures 0ith gains in elevation is not al0a$s appropriate in the Sierra Nevada, and 162 the

%RF model significantl$ improves on both free5air readings and empirical techni!ues in

representing de0point temperatures 0ithin the basin. he geographic differences bet0een the

t0o stud$ sites 0ere illuminated b$ the poorer performance of algorithms in the >osemite area.

/ur stud$ highlights the importance of both observations 0ithin a basin, and recogni ing

topographic limits on the use of simple models. If $ou are modeling a geographicall$ simple

basin such as the RA, one base station 0ithin the basin paired 0ith -RIS" lapse rates 0ill be

representative of overall moisture trends most of the time. o0ever, if the basin is more

geographicall$ complex, 0ith air masses not onl$ due to predominant 0eather patterns, but

micro5topograph$ effects and transport along the mountain range, a ph$sicall$5resolved model

such as %RF is necessar$ to represent de0point variations. If one is ;ust concerned 0ithreducing the average modeled bias in a basin, the simplest method is to add a high5elevation

station that records de0point temperatures and use a model that represents de0 point

temperature declining 0ith elevation.


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D. ppendix : tmospheric "oisture "etrics and Calculations

%e outline metrics used to determine atmospheric moisture content and ho0 0e

calculated de0point temperature. he actual amount of 0ater in the air can be vie0ed as themixing ratio 1r" g3kg or kg3kg 2, 0hich is the ratio of the mass of 0ater to the mass of dr$ air. he

amount of 0ater in the air can also be given as the actual vapor pressure 1e" $a 2 of 0ater in the

air. his relates to the mixing ratio and local air pressure 1 )" $a 2 through the e!uation GGlickman

and /merican 'eteorological Societ& , &'''H:

e )

er

= &&.' 1(2

t a given temperature, there is potential for the atmosphere to hold a given amount of

0ater. his maximum 0ater vapor that the air can hold, called the saturation vapor pressure 1e s "

$a, , is defined b$ the pressure and temperature dependence of the relation bet0een the li!uid

and gas phases of 0ater. large number of methods have been proposed to determine the

saturation vapor pressure from air temperature 1!" 4C 2 based on empirical or theoretical

derivations G Lawrence , &''8H. %e emplo$ the "agnus5 etens formula G 'urra& et al. , (< DH

0ith empiricall$ updated coefficients G /lducho2 and Eskridge , (


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In h$drological applications, 0e are often concerned 0ith the ratio of the amount of 0ater

in the atmosphere over the amount of 0ater that the atmosphere can hold. his ratio is called th

relative humidit$ 1 R(" 5 2, and can be defined as:

see

R( (''= 162

he de0 point temperature 1! d " 4C 2 is the temperature at 0hich the air 0ill be saturated

for a given amount of 0ater vapor. his can be calculated from the actual amount of 0ater vapo

in the air 1e2 as determined from relative humidit$ and the "agnus formulation for vapor

pressure at the de0point temperature:

=


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B. ppendix A: Reference for -rocessing $grochron *ata

In the h$grochron, temperature is measured 0ith a digital thermometer. For R readings, a

small hole covered 0ith a filter permits onl$ 0ater vapor to enter, 0hich is then measured 0ith acapacitance sensor. he instrument can be programmed to ta3e readings at specified intervals

ranging from one second to &D6 hours, 0ith an optional recording start dela$. he device has

storage capacit$ of B(


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%here ( corr is the humidit$ reading 0ith an applied soft0are correction algorithm that is

included in the /ne%ire=ie0er data processing, is the temperature inC, M is '.'6'D, is

'.''689C5(

, T is '.''''769C5&

, U is '.''''(9C5(

at temperatures greater than (89C and 5'.''''89C at temperatures less than (89C, and V is '.'''''&9C 5& as can be found in the

manufacturer s datasheet 1"axim datasheet, Report (


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u 1&''


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affen, *. )., and R. ). Ross 1(


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+a0rence, ". . 1&''82, he Relationship bet0een Relative umidit$ and the *e0pointemperature in "oist ir: Simple Conversion and pplications, %ulletin o the /merican

'eteorological Societ& , =? 1&2, &&8L&66, doi:('.((D84A "S5B 5&5&&8.

+iang, Y., *. -. +ettenmaier, E.F. %ood, and S.). Aurgess 1(1*((2, (L(8, doi:('.('&


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"ee3, *%, ).+. atfield 1(1*62, (LD,doi:('.('&


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