Understanding The Hydrologic and Socioeconomic Impacts of Water Use and Resource Allocation Under...
1
Understanding the Hydrologic and Socioeconomic Impacts of Water Use and Resource Allocation M. Maneta 1 , P Gardner 1 , J Kimball 2, K Jencso 3,4 , B Maxwell 5 , S Ewing 5 1 Geosciences, University of Montana; 2 Numerical Terradynamic Simulation Group, U. of Montana; 3 School of Forestry, U of Montana; 4 Montana Climate Office; 5 LRES, Montana State UIniversity Introduction Temperature The projected increase in the frequency and intensity of droughts over agricultural regions can reduce the water available for irrigation with important ecological and economic consequences. Information on how farmers accomodate water shortage is necessary for robust and efficient agricultural and water policy analysis. New algorithms to retrieve agricultural parameters from earth-observing satellites can be used for micro-level analysis of agricultural production. This information can be used in conjunction with economic models of agricultural activity to describe how farmers will behave when faced with water shortage or policy constraints. Here we present a hydroeconomic modeling framework that assimilates remotely sensed observations of agricultural activity (acreage, yields, crop mix, and evapotranspiration) into an agroeconomic model calibrated using Positive Mathematical Programming (PMP). PMP is widely used in agricultural policy analysis to calibrate models that precisely reproduce the observed economic activity levels of a base year. An important characteristic of models calibrated using PMP is that they reflect the actual behavior of farmers and their actual reaction to external factors, such as drought and risk, rather than the behavior that would be optimal from an agronomic point of view. We extend the classic PMP approach by implementing it within a data assimilation framework based on an ensemble Kalman Filter. The extension permits: Hydro-economic models: Objectives The modeling framework presented here permits the operationalization of new earth observing capabilities to predict changes in land and water use in agricultural regions, to predict the impact of such changes in the hydrologic network, and to inform agricultural and water policy. Specifically, the methods can give insight into the following questions: 1. How do droughts affect crop mix and land use devoted to agriculture? 2. How does agricultural change impact water availability and other water users? 3. What choices and management practices are prioritized, and how can that inform future policy options? 4. How can new remote sensing information be integrated in models to provide insight into socioeconomic dynamics? Remote sensing of agricultural activity Example for Ravalli county, Montana Hydro-economic model of agricultural activity Positive Mathematical Programming with the enKF Satellite products and algorithms available to inform resource allocation include: - Land allocation and crop mix: USDA-NASS Cropland Data Layer (CDL) (annual for conterminous US) - Crop yield: Estimated from radiation use efficiency (Doraiswamy et al, 2005; Lobell et al., 2003) - Supplemental Irrigation: ET estimates based on observed crop phenology, weather data and irrigation efficiency factors Text here... Hydro-economic model at simulation time Behavior under resource constraints is simulated by substituting the identified parameters into the production function and finding the resource matrix X that solves the net revenue maximization problem. To test the model we simulate farmer behavior under known baseline constraints. Conclusions and main findings Parameter distributions converge to a steady value in less than 5 assimilation cycles Predictive variance of the parameters is rapidly reduced from the initial values The parameter ensemble decreases but does not collapse to a single point (stability) Potential cause of bias Precipitation undercatch due to wind effects. Poor representativeness of the observation network from high grid-cell variability. Station location bias toward lower (and flatter) terrain. Response to reduced access to water The heart of the agronomic model is a parametric agricultural production function q i that calculates yield of crop i given the allocated farming resources j embeded in matrix X. The model assumes that farmers will allocate their available resources with the objective of maximizing their net revenues Text here... References Lobell, D. B., Asner, G. P., Ortiz-Monasterio, J. I. and Benning, T. L.: Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties, Agric. Ecosyst. Environ., 94(2), 205–220 Maneta, M.P. and Howitt (2014). Stochastic calibration and data assimilation in non-stationary hydro-economic models. Water Resources Research, 50(5):3976–3993 Maneta, M. P., Torres, M. O., Wallender, W. W., Vosti, S., Howitt, R. E., Rodrigues, L., Bassoi, L. H. and Panday, S.: A spatially distributed hydroeconomic model to assess the effects of drought on land use, farm profits, and agricultural employment, Water Resour. Res., 45, W11412 Roy et al. (2010). Web-enabled Landsat Data (WELD): Landsat ETM+ Composited Mosaics of the Conterminous United States, Remote Sensing of Environment, 114: 35-49. Construct the production function parameter ensemble by sampling from the smoothed mixture of Gaussian densities: Use the parameter ensemble, and decision variables ensemble to generate an ensemble of forecasted marginal revenues: Calculate Kalman gain and update the parameter ensemble: Use observed activity and physical constraints to solve the linear programming problem that yields the shadow prices of limiting inputs: subject to: Obtain modelled or observed estimates of available water B water and estimates of used water per crop Obtain satellite derived estimates of crop acreage yield for each crop k = k + 1 Obtain information on other inputs used (e.g. labor) Initial estimate of mean and covariance of parameter vector , and total land available Generate replicates of observed inputs: Generate replicates of prior guess of elasticity of subtitution parameter Members of the parameter ensemble that have been updated out of the feasible space are projected to the closest point in the boundary of the feasible space Use shadow prices and observed costs to generate the ensemble of marginal costs: [email protected] 1) The use of noisy observations of agricultural activity from satellites 2) The recursive estimation of the agroeconomic model parameters 3) A calibration that reflects long- or mid- term economic behavior 4) An estimation of the uncertainty about the model predictions 5) An assessment of the value of new satellite-based information Data Assimilation Framework Ensemble Kalman Filter Agroeconomic model Agronomic model parameters get sequentially updated with the latest observations Satellite Data: - Crop Acreage - Yield - Evapotranspiration Hydrologic Data: - Water available - Streamflows - Water quality - Diversion points - Well fields Additional Data: - Crop Prices - Irrigation technology - Crop Calendar Agricultural production function Net revenues Revenues Costs Price of crop i Cost of input j Calibration using Positive Mathematical Programming Subject to: Available land Available water Calibration using PMP consists in finding the production function parameters τ, σ, and β, and costs that maximize net revenues under the observed resource allocation and resource constraints. This occurs when marginal net revenues are equal to marginal costs of production. We approximate this maximization problem recursively using an ensemble Kalman filter. Water constraints Observations of resource allocation Dynamics of the parameter ensemble We assimilate seasonal retrievals on of allocated land, water. The enKF filter updates the production function parameters such that the expectation of marginal revenues are as close as possible to the expectation of marginal costs. Full catalog of Landsat and Modis images, geometrically corrected Precise geometric alignment with CDL (also in GEE) GEE premits easy atmospheric correction of Landsat suing LEDAPS Fusion algorithm based on least squre regression, very fast on GEE Water not constraining Main crops: alfalfa and wheat Example focuses on land and water Satellite-based, multi-sensor, annual reclassification of crops CONUS coverage since 2008 High spatial resolution, currently 30m Released in January of the previous year Focus on major summer crops but captures some double cropping Vertical red line is the observed allocation of resources to each crop We simulate the response of farmers to two water shortage scenarios: Scenario 1: 30% reduction in the available water (blue) Scenario 2: 50% reduction in the available water (green) Typical prediction accuracy is within 10% of observations Variance in the distribution of prediction is a direct reflection of parameter uncertainty Reduced water and land for alfalfa is reallocated to wheat, less resource intensive Larger reductions in allocated water than in land for alfalfa indicates stress irrigation Vertical red line indicates baseline allocations (i.e. no change) 1) The PMP method in conjuntion with enKF is an effective method to calibrate economic models of agricultural production using noisy data 2) The enKF also permits to calculate predictive uncertainty 3) Calibration is recursive and improved as new observations become available 4) Covergence is robust and opens the door to the use of remotely sensed estimates of crop acreage, yield, and irrigation For mor information see: Howitt and Maneta (2014). Stochastic calibration and learning in non-stationary hydroeconomic models. WRR Land cost parameter Observed yield Identification of production function parameters Grey lines are the 2-98, 5-95 and 32-68 percentiles of the ensemble (darker to lighter tones). Dashed line is the ensemble average Cropland Data Layer, 2007-2014 - Ravalli county Crop acreage Cropland Data Layer (USDA NASS) Landsat-MODIS fusion for agric. applications (using Google Earth Engine) Transformation of NDVI to yield Supplemental irrigation W j,k from NDVI Landsat-MODIS fusion using Google Earth Engine High-resolution, high-frequency NDVI composite Crop phenology Simulation of baseline conditions NDVI from fused Landat/MODIS, sample scene - Weather data Fused NDVI (30 m, 8 days) MODIS NDVI 250 m spatial res. every ~8 days Landsat 5 NDVI 30 m spatial res. every ~15 days
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Transcript of Understanding The Hydrologic and Socioeconomic Impacts of Water Use and Resource Allocation Under...
Und
erst
andi
ng th
e H
ydro
logi
c an
d So
cioe
cono
mic
Impa
cts
of W
ater
Use
and
Res
ourc
e A
lloca
tion
M. M
anet
a1 , P G
ardn
er1 , J
Kim
ball2,
K J
encs
o3,4 , B
Max
wel
l5 , S E
win
g5
1 Geo
scie
nces
, Uni
vers
ity
of M
onta
na; 2 N
umer
ical
Ter
rady
nam
ic S
imul
atio
n G
roup
, U. o
f Mon
tana
; 3 Scho
ol o
f For
estr
y, U
of M
onta
na; 4 M
onta
na C
limat
e O
ffice
; 5 LRES
, Mon
tana
Sta
te U
Iniv
ersi
ty
Intr
oduc
tion
Tem
pera
ture
The p
roje
cted
incr
ease
in t
he f
req
uency
and
in
tensi
ty o
f d
roug
hts
over
ag
ricu
ltura
l re
gio
ns
can
red
uce
the w
ate
r availa
ble
for
irri
gati
on w
ith
imp
ort
ant
eco
log
ical and
eco
nom
ic c
onse
quen
ces.
Info
rmati
on o
n h
ow
farm
ers
acc
om
od
ate
wate
r sh
ort
ag
e is
nece
ssary
for
rob
ust
an
d effi
cient
ag
ricu
ltura
l and
wate
r p
olic
y a
naly
sis.
New
alg
ori
thm
s to
retr
ieve a
gri
cult
ura
l p
ara
mete
rs f
rom
eart
h-o
bse
rvin
g
sate
llite
s ca
n b
e u
sed
for
mic
ro-l
evel analy
sis
of
ag
ricu
ltu
ral p
rod
uct
ion.
This
info
rmati
on c
an b
e u
sed
in c
onju
nct
ion w
ith
eco
nom
ic m
od
els
of
ag
ricu
ltu
ral
act
ivit
y t
o d
esc
rib
e h
ow
farm
ers
will
behave w
hen
face
d w
ith w
ate
r sh
ort
ag
e
or
polic
y c
onst
rain
ts.
Here
we p
rese
nt
a h
yd
roeco
nom
ic m
od
elin
g f
ram
ew
ork
that
ass
imila
tes
rem
ote
ly s
ense
d o
bse
rvati
ons
of
ag
ricu
ltura
l act
ivit
y (
acr
eag
e,
yie
lds,
cro
p
mix
, and
evap
otr
an
spir
ati
on)
into
an a
gro
eco
nom
ic m
od
el ca
libra
ted
usi
ng
Posi
tive M
ath
em
ati
cal Pro
gra
mm
ing
(PM
P).
PM
P is
wid
ely
use
d in a
gri
cult
ura
l
polic
y a
naly
sis
to c
alib
rate
mod
els
that
pre
cise
ly r
ep
rod
uce
th
e o
bse
rved
eco
nom
ic a
ctiv
ity levels
of
a b
ase
year.
An im
port
ant
chara
cteri
stic
of
mod
els
calib
rate
d u
sing
PM
P is
that
they r
eflect
the a
ctual b
eh
avio
r of
farm
ers
and
th
eir
act
ual re
act
ion t
o e
xte
rnal fa
ctors
, su
ch a
s d
roug
ht
and
risk
, ra
ther
than t
he b
ehavio
r th
at
would
be o
pti
mal fr
om
an a
gro
nom
ic p
oin
t
of
vie
w.
We e
xte
nd
th
e c
lass
ic P
MP a
pp
roach
by im
ple
menti
ng
it
wit
hin
a d
ata
ass
imila
tion f
ram
ew
ork
base
d o
n a
n e
nse
mb
le K
alm
an F
ilter.
The e
xte
nsi
on
perm
its:
Hyd
ro-e
cono
mic
mod
els:
Obj
ecti
ves
The m
od
elin
g f
ram
ew
ork
pre
sente
d h
ere
perm
its
the o
pera
tionaliz
ati
on o
f
new
eart
h o
bse
rvin
g c
ap
ab
iliti
es
to p
red
ict
chang
es
in land
and
wate
r use
in
ag
ricu
ltura
l re
gio
ns,
to p
red
ict
the im
pact
of
such
ch
ang
es
in t
he h
yd
rolo
gic
netw
ork
, an
d t
o info
rm a
gri
cult
ura
l and
wate
r p
olic
y.
Sp
ecifica
lly,
the m
eth
od
s ca
n g
ive insi
gh
t in
to t
he f
ollo
win
g q
uest
ions:
1.
How
do d
rou
gh
ts aff
ect
cro
p m
ix a
nd
lan
d u
se d
evote
d t
o
ag
ricu
ltu
re?
2.
How
does a
gri
cu
ltu
ral ch
an
ge im
pact
wate
r availab
ilit
y a
nd
oth
er
wate
r u
sers
?
3.
Wh
at
ch
oic
es a
nd
man
ag
em
en
t p
racti
ces a
re p
riori
tized
, an
d
how
can
th
at
info
rm f
utu
re p
olicy o
pti
on
s?
4.
How
can
new
rem
ote
sen
sin
g in
form
ati
on
be in
teg
rate
d in
mod
els
to p
rovid
e in
sig
ht
into
socio
econ
om
ic d
yn
am
ics?
Rem
ote
sens
ing
of a
gric
ultu
ral a
ctiv
ity
Exam
ple
for
Ravalli cou
nty
, M
on
tan
a
Hyd
ro-e
cono
mic
mod
el o
f agr
icul
tura
l act
ivit
y
Posi
tive
Mat
hem
atic
al P
rogr
amm
ing
wit
h th
e en
KF
Sate
llite
pro
duct
s and a
lgori
thm
s availa
ble
to info
rm r
eso
urc
e a
lloca
tion incl
ude:
- L
and a
lloca
tion a
nd c
rop m
ix:
USD
A-N
ASS C
ropla
nd D
ata
Layer
(CD
L) (
annu
al fo
r co
nte
rmin
ous
US)
- C
rop y
ield
: E
stim
ate
d f
rom
radia
tion u
se effi
ciency
(D
ora
isw
am
y e
t al, 2
00
5;
Lobell
et
al., 2
00
3)
- S
upple
men
tal Ir
rigati
on:
ET e
stim
ate
s base
d o
n o
bse
rved c
rop p
henolo
gy, w
eath
er
data
an
d irr
igati
on
effi
ciency
fact
ors
Text
here
...
Hyd
ro-e
cono
mic
mod
el a
t sim
ulat
ion
tim
e
Beha
vior
und
er re
sour
ce c
onst
rain
ts is
sim
ulat
ed b
y su
bstit
utin
g th
e id
entifi
ed
para
met
ers
into
the
prod
uctio
n fu
nctio
n an
d fin
ding
the
reso
urce
mat
rix X
that
sol
ves
the
net r
even
ue m
axim
izat
ion
prob
lem
. To
test
the
mod
el w
e si
mul
ate
farm
er b
ehav
ior
unde
r kno
wn
base
line
cons
train
ts.
Conc
lusi
ons
and
mai
n fin
ding
s
Para
mete
r d
istr
ibu
tions
con
verg
e t
o a
ste
ad
y v
alu
e in less
th
an 5
ass
imila
tion c
ycl
es
Pre
dic
tive v
ari
ance
of
the p
ara
mete
rs is
rap
idly
red
uce
d f
rom
the in
itia
l valu
es
The p
ara
mete
rense
mb
le d
ecr
ease
s b
ut
does
not
colla
pse
to a
sin
gle
poin
t (s
tab
ility
)
Pote
nti
al cau
se o
f b
ias
Pre
cip
itati
on u
nd
erc
atc
h d
ue t
o w
ind
eff
ect
s.
Poor
rep
rese
nta
tiveness
of
the o
bse
rvati
on n
etw
ork
fro
m h
igh g
rid
-cell
vari
ab
ility
.
Sta
tion loca
tion b
ias
tow
ard
low
er
(an
d fl
att
er)
terr
ain
.
Resp
on
se t
o r
ed
uced
access t
o w
ate
r
The h
eart
of
the a
gro
nom
ic m
odel is
a
para
metr
ic a
gri
cult
ura
l pro
duct
ion f
unct
ion q
i
that
calc
ula
tes
yie
ld o
f cr
op i g
iven t
he
allo
cate
d f
arm
ing r
eso
urc
es j em
beded in
matr
ix X
.
The m
odel ass
um
es
that
farm
ers
will
allo
cate
their
availa
ble
reso
urc
es
wit
h t
he o
bje
ctiv
e o
f
maxim
izin
g t
heir
net
revenues
Text h
ere...
Refe
renc
esLo
bell,
D. B
., A
sner,
G. P.
, O
rtiz
-Monast
eri
o, J. I. and B
enn
ing, T.
L.:
Rem
ote
sensi
ng o
f re
gio
nal cr
op p
roduct
ion
in t
he Y
aqui Valle
y, M
exic
o:
est
imate
s an
d u
nce
rtain
ties,
Agri
c. E
cosy
st. En
vir
on., 9
4(2
), 2
05
–22
0
Maneta
, M
.P. an
d H
ow
itt
(20
14
). S
toch
ast
ic c
alib
rati
on a
nd d
ata
ass
imila
tion in n
on
-sta
tionary
hydro
-eco
nom
ic
models
. W
ate
r R
eso
urc
es
Rese
arc
h,
50
(5):
39
76
–39
93
Maneta
, M
. P.
, To
rres,
M.
O., W
alle
nder,
W. W
., V
ost
i, S
., H
ow
itt,
R. E., R
odri
gues,
L., B
ass
oi, L
. H
. and P
anday,
S.:
A
spati
ally
dis
trib
ute
d h
ydro
eco
nom
ic m
odel to
ass
ess
the eff
ect
s of
dro
ught
on
land u
se, fa
rm p
rofits
, and
agri
cult
ura
l em
plo
ym
ent,
Wate
r R
eso
ur.
Res.
, 4
5, W
11
41
2
Roy e
t al. (
20
10
). W
eb-e
nable
d L
an
dsa
t D
ata
(W
ELD
): L
andsa
t ETM
+ C
om
posi
ted M
osa
ics
of
the C
on
term
inous
Unit
ed S
tate
s, R
em
ote
Sensi
ng o
f Envir
onm
ent,
11
4:
35
-49
.
Con
stru
ct t
he p
rodu
ctio
n fu
ncti
on p
aram
eter
ens
embl
e by
sam
plin
g fr
om t
he s
moo
thed
mix
ture
of
Gau
ssia
n de
nsit
ies:
Use
the
par
amet
er e
nsem
ble,
and
dec
isio
n va
riab
les
ense
mbl
e to
gen
erat
ean
ens
embl
e of
for
ecas
ted
mar
gina
l re
venu
es:
Cal
cula
te K
alm
an g
ain
and
upda
te t
he p
aram
eter
ens
embl
e:
Use
obse
rved a
cti
vit
y a
nd p
hysi
cal const
rain
ts t
o s
olv
e t
he lin
ear
pro
gra
mm
ing p
roble
m t
hat
yie
lds
the s
hadow
pri
ces
of
lim
itin
g inputs
:
subj
ect
to:
Obt
ain
mod
elle
d or
obs
erve
d es
tim
ates
of
avai
labl
ew
ater
Bw
ater
and
esti
mat
es o
f us
ed w
ater
per
cro
pO
btai
n sa
tell
ite
deri
ved
esti
mat
es o
f cr
op a
crea
geyi
eld
for
each
cro
p
k =
k +
1
Obt
ain
info
rmat
ion
on o
ther
inp
uts
used
(e
.g. l
abor
)
Init
ial
esti
mat
e of
mea
n an
dco
vari
ance
of
para
met
er v
ecto
r
, and
tot
al l
and
avai
labl
e
Gen
erat
e re
plic
ates
of
obse
rved
inp
uts:
Gen
erat
e re
plic
ates
of
prio
r gu
ess
of e
last
icit
y of
sub
titu
tion
par
amet
er
Mem
bers
of
the
para
met
er e
nsem
ble
that
hav
e be
enup
date
d ou
t of
the
fea
sibl
e sp
ace
are
proj
ecte
dto
the
clo
sest
poi
nt i
n th
e bo
unda
ry o
f th
e fe
asib
le s
pace
Use
sha
dow
pri
ces
and
obse
rved
cos
ts t
o ge
nera
te t
he e
nsem
ble
of m
argi
nal
cost
s:
mar
co.m
anet
a@um
onta
na.e
du
1) T
he u
se o
f noi
sy o
bser
vatio
ns o
f agr
icul
tura
l act
ivity
from
sat
ellit
es2)
The
recu
rsiv
e es
timat
ion
of th
e ag
roec
onom
ic m
odel
par
amet
ers
3) A
cal
ibra
tion
that
refle
cts
long
- or m
id- t
erm
eco
nom
ic b
ehav
ior
4) A
n es
timat
ion
ofth
e un
cert
aint
y ab
outt
he m
odel
pre
dict
ions
5) A
n as
sess
men
t of t
he v
alue
of n
ew s
atel
lite-
base
d in
form
atio
n
Data
Ass
imila
tion F
ram
ew
ork
Ense
mb
le K
alm
an F
ilter
Ag
roeco
nom
ic m
odel
Ag
ronom
ic m
od
el p
ara
mete
rsg
et
seq
uenti
ally
up
date
dw
ith t
he late
st o
bse
rvati
ons
Sate
llite
Data
:-
Cro
p A
creag
e-
Yield
- Evap
otr
ansp
irati
on
Hydro
logic
Data
:-
Wate
r availa
ble
- S
treamflow
s-
Wate
r q
ualit
y-
Div
ers
ion p
oin
ts-
Wellfield
s
Ad
dit
ional D
ata
:-
Cro
p P
rice
s-
Irri
gati
on t
ech
nolo
gy
- C
rop
Cale
ndar
Ag
ricu
ltu
ral p
rod
ucti
on
fu
ncti
on
Net
reven
ues
Reven
ues
Costs
Pric
e of
cro
p i
Cost
of i
nput
j
Calib
rati
on
usin
g P
osit
ive M
ath
em
ati
cal P
rog
ram
min
g
Su
bje
ct
to:
Avai
labl
e la
nd
Avai
labl
e w
ater
Calib
rati
on u
sing
PM
P c
on
sist
s in
find
ing
the p
rod
uct
ion f
unct
ion p
ara
mete
rs
τ, σ,
and
β,
and
cost
s th
at
maxim
ize n
et
revenues
und
er
the o
bse
rved
reso
urc
e
allo
cati
on a
nd
reso
urc
e c
onst
rain
ts.
This
occ
urs
when m
arg
inalnet
revenues
are
eq
ual
to m
arg
inal co
sts
of
pro
duct
ion.
We a
pp
roxim
ate
this
maxim
izati
on p
rob
lem
recu
rsiv
ely
usi
ng
an e
nse
mb
le K
alm
an fi
lter.
Wate
r con
str
ain
ts
Ob
serv
ati
on
s o
f re
sou
rce a
llocati
on
Dyn
am
ics o
f th
e p
ara
mete
r en
sem
ble
We a
ssim
ilate
seaso
nal re
trie
vals
on
of
allo
cate
d land
, w
ate
r. T
he e
nK
F filt
er
up
date
s th
e p
rod
uct
ion f
unct
ion p
ara
mete
rs s
uch
that
the e
xp
ect
ati
on o
f
marg
inal re
venues
are
as
close
as
poss
ible
to t
he e
xp
ect
ati
on o
f m
arg
inal co
sts.
Full
cata
log
of
Land
sat
and
Mod
is im
ag
es,
geom
etr
ically
corr
ect
ed
Pre
cise
geom
etr
ic a
lignm
en
t w
ith
CD
L (a
lso in G
EE)
GEE p
rem
its
easy
atm
osp
heri
c co
rrect
ion o
f La
nd
sat
suin
g L
ED
APS
Fusi
on a
lgori
thm
base
d o
n least
sq
ure
reg
ress
ion,
very
fast
on G
EE
Wate
r n
ot
const
rain
ing
Main
cro
ps:
alfalfa a
nd
wheat
Exam
ple
focu
ses
on lan
d a
nd
wate
r
Sate
llite
-base
d,
mult
i-se
nso
r, a
nnual re
class
ifica
tion o
f cr
op
s
CO
NU
Sco
vera
ge s
ince
20
08
Hig
h s
pati
al re
solu
tion,
curr
entl
y 3
0m
R
ele
ase
d in Jan
uary
of
the p
revio
us
year
Focu
s on
majo
r su
mm
er
crops
but
captu
res
som
e d
ouble
cro
ppin
g
Vert
ical
red
line
is th
e ob
serv
ed a
lloca
tion
of re
sour
ces
to e
ach
crop
We
sim
ulat
e th
e re
spon
se o
f far
mer
s to
two
wat
er s
hort
age
scen
ario
s:Sc
enar
io 1
: 30%
redu
ctio
n in
the
avai
labl
e w
ater
(blu
e)Sc
enar
io 2
: 50%
redu
ctio
n in
the
avai
labl
e w
ater
(gre
en)
Typ
icalp
red
icti
on a
ccura
cy is
wit
hin
10
%of
ob
serv
ati
ons
Vari
an
ce in t
he d
istr
ibuti
on o
f p
red
icti
on is
a d
irect
reflect
ion o
f p
ara
mete
r unce
rtain
ty
Red
uce
d w
ate
r an
d land
for
alfalfa is
reallo
cate
d t
o w
heat,
less
reso
urc
e inte
nsi
ve
Larg
er
red
uct
ions
in a
lloca
ted
wate
r th
an
in lan
d f
or
alfalfa
ind
icate
s st
ress
irr
igati
on
Vert
ical
red
line
indi
cate
s ba
selin
e al
loca
tions
(i.e
. no
chan
ge)
1) T
he P
MP
met
hod
in c
onju
ntio
n w
ith e
nKF
isan
eff
ectiv
e m
etho
d to
cal
ibra
te
econ
omic
mod
els
of a
gric
ultu
ral p
rodu
ctio
n us
ing
nois
y da
ta2)
The
enK
F al
so p
erm
its to
cal
cula
te p
redi
ctiv
e un
cert
aint
y3)
Cal
ibra
tion
is re
curs
ive
and
impr
oved
as
new
obs
erva
tions
bec
ome
avai
labl
e4)
Cov
erge
nce
is ro
bust
and
ope
ns th
e do
or to
the
use
of re
mot
ely
sens
ed e
stim
ates
of
crop
acr
eage
, yie
ld, a
nd ir
rigat
ion
For m
or in
form
atio
n se
e: H
owitt
and
Man
eta
(201
4). S
toch
astic
cal
ibra
tion
and
lear
ning
in n
on-s
tatio
nary
hyd
roec
onom
ic m
odel
s. W
RR
Land
cos
tpa
ram
eter
Obs
erve
d yi
eld
Iden
tific
atio
n of
pro
duct
ion
func
tion
par
amet
ers
Gre
y lin
es
are
the 2
-98
, 5
-95
an
d 3
2-6
8 p
erc
enti
les
of
the e
nse
mb
le (
dark
er
to
lighte
r to
nes)
. D
ash
ed
lin
e is
the e
nse
mb
le a
vera
ge
Cro
pla
nd
Data
Layer,
2007-2
014 -
Ravalli cou
nty
Crop acreage
Cro
pla
nd
Data
Layer
(US
DA
NA
SS
)
Lan
dsat-
MO
DIS
fu
sio
n f
or
ag
ric.
ap
plicati
on
s (
usin
g G
oog
le E
art
h E
ng
ine)
Tran
sfo
rmati
on
of
ND
VI
to y
ield
Su
pp
lem
en
tal ir
rig
ati
on
Wj,
k f
rom
ND
VI
Land
sat-
MO
DIS
fusi
on u
sing
Goog
le E
art
h E
ng
ine
Hig
h-r
esolu
tion
, h
igh
-fre
qu
en
cy N
DV
I com
posit
eC
rop
ph
en
olo
gy
Sim
ula
tion
of
baselin
e c
on
dit
ion
sN
DV
I fr
om
fu
sed
Lan
dat/
MO
DIS
, sam
ple
scen
e
- W
eath
er
data
Fuse
d N
DV
I
(30
m,
8 d
ays)
MO
DIS
ND
VI
25
0 m
spati
al re
s.
every
~8
days
Landsa
t 5
ND
VI
30
m s
pati
al re
s.
every
~1
5 d
ays
