\documentclass[gmd]{copernicus} % for 2-column papers



\begin{document}



\title{Using satellite-based estimates of evapotranspiration and
groundwater changes to determine anthropogenic water\hack{\newline} fluxes in land surface
models}


\Author[1,*]{R.~G.}{Anderson}
\Author[2,*]{M.-H.}{Lo}
\Author[3]{S.}{Swenson}
\Author[4]{J.~S.}{Famiglietti}
\Author[5]{Q.}{Tang}
\Author[1]{T.~H.}{Skaggs}
\Author[2]{Y.-H.}{Lin}
\Author[3]{R.-J.}{Wu}


\affil[1]{USDA, Agricultural Research Service, US Salinity
Laboratory, Contaminant Fate and Transport Unit,\hack{\newline} Riverside, CA,
USA}
\affil[2]{National Taiwan University, Department of Atmospheric
Sciences, Taipei, Taiwan}
\affil[3]{National Center for Atmospheric Research, Advanced Study
Program, Boulder, CO, USA}
\affil[4]{Jet Propulsion Laboratory, California Institute of
Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA}
\affil[5]{Key Laboratory of Water Cycle and Related Land Surface
Processes, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing, China}
\affil[*]{These authors contributed equally to this work.}

\correspondence{R.~G.~Anderson (ray.anderson@ars.usda.gov) and M.-H.~Lo
(minhuilo@ntu.edu.tw)}


\runningtitle{Using satellite-based estimates of evapotranspiration and
groundwater changes}
\runningauthor{R.~G.~Anderson et al.}

\received{6 February 2015}
\pubdiscuss{29 April 2015}
\revised{4 September 2015}
\accepted{14 September 2015}
\published{}

\firstpage{1}

\maketitle



\begin{abstract}
Irrigation is a widely used water management practice that is often poorly
parameterized in land surface and climate models. Previous studies have
addressed this issue via use of irrigation area, applied water inventory
data, or soil moisture content. These approaches have a variety of drawbacks
including data latency, accurately prescribing irrigation intensity, and a
lack of conservation of water volume for models using a prescribed soil
moisture approach. In this study, we parameterize irrigation fluxes using
satellite observations of evapotranspiration (ET) compared to ET from a suite
of land surface models without irrigation. We then incorporate the irrigation
flux into the Community Land Model (CLM)  and use a systematic
trial-and-error procedure to determine the ground- and surface-water
withdrawals that are necessary to balance the new irrigation flux. The
resulting CLM simulation with irrigation produces ET that matches the
magnitude and seasonality of observed satellite ET well, with a mean
difference of 6.3\,mm\,month$^{-1}$ and a correlation of 0.95. Differences
between the new CLM ET values and satellite-observed ET values are always
less than 30\,mm\,month$^{-1}$ and the differences show no pattern with
respect to seasonality. The results reinforce the importance of accurately
parameterizing anthropogenic hydrologic fluxes into land surface and climate
models to assess environmental change under current and future climates and
land management regimes.
\end{abstract}


\introduction

Agricultural irrigation is the dominant anthropogenic use of surface water
and groundwater globally (Postel et al., 1996; Siebert et al., 2010; Wisser
et al., 2008). Irrigation, and its associated movement, storage, and
depletion of surface and ground waters, can induce major changes in regional
hydrology (Ferguson and Maxwell, 2012; Haddeland et al., 2006; Tang et al.,
2008) and climatology (Kueppers et al., 2007; Lo and Famiglietti, 2013).
Irrigation demand has resulted in groundwater depletion across multiple
regions of the world (Famiglietti, 2014), including the western United States
(Famiglietti et al., 2011; Scanlon et al., 2012), the Middle East (Voss et
al., 2013), and India (Rodell et al., 2009). Globally, this depletion has a
net effect on continental runoff and sea level rise (Van Djik et al., 2014;
Wada et al., 2010). Given the impact of irrigation on hydrology, climate, and
food production, it is crucial to be able to accurately model irrigation in
current land surface models (e.g., Rodell et al., 2004; Xia et al., 2012a) in
order to assess potential land--atmosphere feedback mechanisms that may
impact future water availability for irrigation, municipal, and environmental
uses.

Current land surface models (LSMs), such as the Community Land Model (CLM;
Oleson et al., 2008), that are run without an irrigation parameterization
usually have unrealistically low evapotranspiration in agricultural regions
(Lei et al., 2015; Lo et al., 2013; Lobell et al., 2009; Sorooshian et al.,
2011; Ozdogan, 2010). Given that irrigation is predominantly used in
semi-arid to arid regions and/or regions where precipitation and growing
seasons are asynchronous, this lack of parameterization can be highly
significant for modeling regional hydrology. Some LSMs and their associated
regional climate models (RCMs) or global climate models (GCMs) prescribe
enhanced water availability in agricultural regions due to irrigation.
Representations vary considerably depending on the simulation; they include
(1) prescribing a static soil moisture at field capacity for all irrigated
crops (Kueppers et al., 2007), (2) prescribing a total flux based on a
prescribed estimate across the entire agricultural domain (Lo and
Famiglietti, 2013), (3) assigning a fraction of land surface to be irrigated
(Leng et al., 2013, 2014; Lobell et al., 2009; Tang et al., 2007), and (4)
assigning a seasonally based soil moisture curve to represent irrigation only
during the active irrigation season (Sooroshian et al., 2011). Each of these
approaches has significant disadvantages. The approaches that assign
irrigation based on soil moisture (approaches 1 and 4 above) do not consider
basin-scale limitations on available irrigation water (particularly during
dry years) and may overestimate the total amount of irrigation water as well
as the differential impacts between dry and wet years. The
prescribed/inventory-based flux (approach 2) has the advantage of a mostly
conserved water budget, but there are latency issues for much of the data
which are based on potentially outdated or incomplete national and regional
statistics. Assigning a fraction of land area to be irrigated (approach 3)
has the disadvantage of assuming a particular irrigation intensity, and this
approach cannot easily distinguish between full and deficit irrigation.
Finally, some prescribed flux approaches work primarily where groundwater is
the sole source for applied irrigation, and others based on irrigated area may
not account for irrigation intensity. While process differences in RCMs/GCMs
and LSMs can account for variations in the sensitivity of irrigation--climate
feedbacks and teleconnections, it should be noted that studies with different
irrigation parameterizations over the same region have had significantly
different climatic feedbacks and downwind impacts (Kueppers et al., 2007; Lo
and Famiglietti, 2013; Lo et al., 2013; Sooroshian et al., 2011).

Satellite remote sensing can be used to provide more robust, regional
observations of irrigation water consumption. Evapotranspiration (ET) is
routinely monitored over irrigated agriculture using observations of surface
temperature and vegetation greenness (Allen et al., 2007; Anderson et al.,
2007; Tang et al., 2009a). The precision and accuracy of satellite ET
algorithms, particularly those that incorporate land surface temperature
data, is sufficiently high to quantify water management and water rights
transfers (Cuenca et al., 2013; Steele et al. 2015; Tang et al., 2009b). When
combined with satellite gravimetry (Swenson and Wahr, 2003) and large-scale
meteorological products (Hart et al., 2009), the amount of irrigation water
coming from surface water supplies (Anderson et al., 2012) and net
groundwater depletion (Famiglietti et al., 2011) can be assessed. Together,
these satellite algorithms can provide a much more detailed and current
input data set for LSMs and RCMs/GCMs to assess irrigation--climate feedbacks.

In this study, we follow on the work of Lo and Famiglietti~(2013) by using
remote sensing observations of ET, surface water consumption, and total water
storage anomalies to infer surface water and groundwater fluxes, instead of using
a static surface water and groundwater irrigation inventory data set for
parameterization. We use these fluxes to improve and test an irrigation
parameterization in the Community Land Model (Lawrence et al., 2011; Oleson
et al., 2008) in a well instrumented basin with a large amount of irrigated
agriculture, the Central Valley of California. We use ET from an ensemble of
three satellite products, combined with gridded precipitation, to determine
the seasonality and interannual variability of additional ET from irrigation.
We then use an iterative recharge parameterization, combined with satellite
gravimetry, to determine relative amounts of irrigation applied from
groundwater and surface water. The results show the ability and value of
using diagnostic remote sensing observations and models for improving
prognostic algorithms necessary to increase LSM skill in predicting
hydrologic, biogeochemical, and climatic impacts and feedbacks under future
greenhouse gas emission and land use  change scenarios.

\section{Methods}

\subsection{Study region}

We evaluate our approach in the Central Valley of California, which is a
large ($\sim$\,54\,000\,km$^{2})$, low elevation ($<$\,200\,m\,a.s.l.) region
(Fig.~1). The Central Valley is a highly productive agricultural region, with
over 200 cultivated crops and an annual crop value of more than
USD\,35\,billion in 2012 (California Department of Food and Agriculture,
2014; USDA National Agricultural Statistics Service, 2014). Relevant aspects
of the Central Valley's geology (Planert and Williams, 1995; Faunt, 2009),
climatology (Zhong et al., 2004), hydrology (Scanlon et al., 2012), and
anthropogenic interbasin water transfers (Chung and Helweg, 1985;
Fischhendler and Zilberman, 2005) are extensively reviewed elsewhere. Average
(2004--2009 water years) blue water (surface water plus groundwater)
consumption was 2.03\,$\pm$\,0.02 X10$^{10}$\,m$^{3}$ as determined using an
inventory method (Anderson et al., 2012). Agriculture in the Central Valley
is heavily dependent upon irrigation from both surface water and groundwater,
with a large variation in the relative consumption of surface water and
groundwater due to high interannual variation in precipitation and an almost
complete lack of precipitation during the peak summer growing season
(Anderson et al., 2012; Scanlon et al. 2012). In particular, many farmers
have both surface- and groundwater irrigation infrastructure and will pump
more groundwater when surface water deliveries are insufficient. In addition
to its agricultural importance, the Central Valley has multiple attributes
that are useful for developing and validating new model processes to better
represent anthropogenic impacts on regional hydrology and climatology. These
include (a) well-understood hydrogeology, surface water use, and extensive
\textit{in situ} meteorological observations (Hart et al., 2009; Faunt, 2009;
Planert and Williams, 1995); (b) well constrained groundwater systems with
little to no subsurface outflow to the ocean (Faunt, 2009); (c) well-gauged
and modeled surface water flows into and out of the valley (Anderson et al.,
2012); and (d) anthropogenic hydrologic processes (irrigation, crop
evapotranspiration, and drainage) that have a very distinct seasonality from
the winter precipitation and spring-runoff-dominated natural processes that
occurred prior to irrigation and agricultural development (Lo and
Famiglietti, 2013).

%f1
\begin{figure}[t]
\includegraphics[width=7cm]{gmd-2015-28-f01.jpg}
\caption{Map of the Central Valley, California. \textbf{(a)} Underlying
normalized differential vegetation index (NDVI) from the MODerate resolution
Imaging Spectroradiometer (MODIS) 250\,m, 16-day product (July~2006)
illustrating irrigated regions of the Central Valley (black outline). Darker
green indicates higher NDVI and vegetation cover. \textbf{(b)} Map of the
United States with the inset area of \textbf{(a)} outlined in red.}
\end{figure}

Previous remote-sensing-based and mechanistic modeling studies have shown
sustained and substantial depletion of groundwater in the Central Valley
(Famiglietti et al., 2011; Faunt, 2009), which has accelerated in the most
recent drought from 2012 to present (Borsa et al., 2014; Famiglietti, 2014).
This reliance on remote sensing and modeling is due, in part, to the
historically minimal well reporting requirements from the State of
California, resulting in a relative paucity of publicly available groundwater
extraction data. Recent groundwater regulation legislation will likely
restrict future groundwater pumping differentially across groundwater basins
(Harter and Dahlke, 2014), making alternative irrigation methods and
strategies, such as drip and deficit irrigation, more common and potentially
altering the amount and seasonality of irrigation. The potential for rapid
hydrologic changes in the Central Valley (such as sudden restrictions on
groundwater pumping or whole-scale conversions in irrigation method) is one
reason why a potentially dynamic, satellite-based irrigation parameterization
would be useful for land surface modeling.

\subsection{Evapotranspiration, precipitation and total water observations}

We calculated the monthly mean and standard deviation of evapotranspiration
(ET) using an ensemble of three products. One is a surface energy balance
product (Anderson et al., 2012) based on the SEBAL algorithm (Surface Energy
Balance Algorithm for Land; Bastiaanssen et al., 1998) that is applied to the
Central Valley at 250\,m resolution using a 250\,m vegetation index and 1\,km
thermal data from the MODerate resolution Imaging Spectroradiometer (MODIS)
in conjunction with gridded meteorology. The second product (Tang et al.,
2009a) uses the scatter plot relationship between the vegetation index and
surface temperature (VI--Ts) to estimate the evaporative fraction (EF) and ET
at 0.05{\degree} resolution using MODIS vegetation and thermal data in
conjunction with Geostationary Operational Environmental Satellite (GOES)
surface radiation products. The third product (Jin et al., 2011), uses the
Priestley--Taylor equation (Priestley and Taylor, 1972) with the coefficient
term ($\alpha )$ optimized using AmeriFlux data and net radiation and ground
heat flux parameterized from the MODIS and Clouds and the Earth's Radiant
Energy System (CERES) instruments to estimate ET at 1\,km resolution. All
three products were clearly able to distinguish peak summertime ET in the
Central Valley, which is asynchronous with largely winter precipitation and
which is a characteristic sign of irrigation. Other ET products (e.g.,
Miralles et al., 2011; Mu et al., 2011; Jung et al., 2010) were not used as
they were either too coarse in resolution
($>$\,0.25{\degree}\,$\times$\,0.25{\degree} cell size) or were unable to
detect irrigation in the Central Valley.

Monthly precipitation (approximately 4\,km spatial resolution) was obtained
using the Parameter-elevation Regressions on Independent Slopes Model
(PRISM), which interpolates station precipitation data, accounting for
orography (Daly et al., 1994, 2008). Observations of total water changes were
obtained from the Gravity Recovery And Climate Experiment (GRACE) mission
(Tapley et al., 2004) for the entire Sacramento and San Joaquin river basins
(including the usually endoheric Tulare Lake basin). Using the methodology of
Famiglietti et al.~(2011), groundwater changes were obtained by removing
snow, soil moisture, and surface reservoir storage variations from the total
water storage anomalies from GRACE. Groundwater changes in the combined
basins were assumed to have occurred entirely within the Central Valley where
major agricultural and municipal wells exist rather than in the
non-irrigated, sparsely populated, mountainous regions surrounding the
valley.

\subsection{Land surface models}

For intercomparison with satellite-observed fluxes and determination of
additional water application in CLM, we use an ensemble (nine members) of
three North American Land Data Assimilation System outputs (NLDAS-2 --
Mitchell et al., 2004; Xia et al., 2012b), four Global Land Data Assimilation
System (GLDAS-1 -- Rodell et al., 2004) outputs, and two CLM simulations. For
NLDAS-2 and GLDAS-1, we used the Noah, Mosaic, VIC (Variable Infiltration
Capacity), or CLM models from each system with the primary NLDAS-2 and
GLDAS-1 forcings. Along with the NLDAS/GLDAS outputs, we also include outputs
from different versions of the CLM (including CLM3.5 and CLM4) with the
GLDAS-1 atmospheric forcings. Our intention with including this number of
permutations of LSMs and LSM forcings was to increase our confidence in the
mean and uncertainty of non-irrigated ET. In addition, we evaluated the CMIP5
control outputs (Taylor et al., 2012) to assess the larger performance of
climate models in assessing latent heat fluxes across agricultural regions.
Details about the CMIP5 models and simulations are provided in
Supplement~Sect.~1. For our study, CLM is run at 0.125{\degree} by
0.125{\degree} grid cells with 30\,min temporal resolution.

The water budget for the soil layer and groundwater in CLM can be written
as
\begin{align}
&\Delta \mathrm{SM}=P-\mathrm{ET}-Q_\mathrm{S}-q_\text{recharge},\\
 &\Delta
\mathrm{GW}=q_\text{recharge}\, -\, Q_\mathrm{d},
\end{align}
where $\Delta $SM is soil moisture change, $P$ is precipitation, ET is
evapotranspiration, $Q_\mathrm{S}$ is surface runoff, $q_\text{recharge}$ is
groundwater recharge, $\Delta $GW is groundwater storage changes, and
$Q_\mathrm{d}$ is groundwater discharge. However, Eqs.~(1) and (2) only
reflect the natural hydrology and neglect the substantial contribution of
irrigation in major agricultural regions as previously discussed. A more
reasonable equation should include the aforementioned irrigation water from
surface (river) water ($\text{SW}_\text{WD})$ and from groundwater withdrawal
($\text{GW}_\text{WD})$ as shown in Fig.~2 and Eqs.~(3) and (4). We will
incorporate the estimated irrigation water use into the CLM version 4 and the
withdrawn water in the irrigation process will be treated as an extra water
input (effective precipitation).
\begin{align}
&\Delta \mathrm{SM}=P-\mathrm{ET}-Q_\mathrm{S}-q_\text{recharge}+\mathrm{GW}_\mathrm{WD}+\mathrm{SW}_\mathrm{WD}\\
&\Delta \mathrm{GW}=q_\text{recharge}\, -\,
Q_\mathrm{d}-\mathrm{GW}_\mathrm{WD}
\end{align}

%f2
\begin{figure*}[t]
\includegraphics[width=14cm]{gmd-2015-28-f02.png}
\caption{Conceptual schematic of land hydrological processes, modified from
Oleson et al.~(2008). Blue dashed and green lines indicate the irrigation water
fluxes applied in the CLM. In the Central Valley, the aquifer is variably
confined with some regions having no confinement.}
\end{figure*}


\subsection{CLM groundwater and surface water application parameterization}

We use the difference ($\Delta $ET) between remote-sensing-observed ET
(ET$_\text{obs})$ and the original model-parameterized ET (ET$_\text{om})$ to
estimate total applied surface water and groundwater as shown in Eq.~(5).
\begin{equation}
\Delta \mathrm{ET}=\mathrm{ET}_\text{obs}\, -\,
\mathrm{ET}_\text{om}=\mathrm{SW}_\mathrm{WD}+\mathrm{GW}_\mathrm{WD}
\end{equation}
$\Delta $ET in Eq.~(5) is determined as an interannual (2004--2009) mean
difference between satellite-observed and modeled ET. Water is applied evenly
in CLM4 throughout the primary growing and irrigation season (May--October).
We can partition the total withdrawn irrigation water into
$\mathrm{SW}_\mathrm{WD}$ and $\mathrm{GW}_\mathrm{WD}$ by requiring that
Eqs.~(3) and (4) are both satisfied by the CLM4 simulation. A systematic,
trial-and-error procedure is used to determine the necessary partitioning
using groundwater recharge since it is a common variable to both equations.
For each trial, a value of $q_\text{recharge}$ is guessed.
$\mathrm{GW}_\mathrm{WD}$ is then determined from rearranging Eq.~(4), with
$\Delta $GW and $Q_\mathrm{d}$ being set to average values derived from
processed GRACE $\Delta $GW and the baseline simulations for the study period
(2004--2009), respectively. $\mathrm{SW}_\mathrm{WD}$ is then found as a
residual from Eq.~(5), and CLM4 is run. The model run generates a simulated
recharge (Eq.~3). If the trial (or ``parameterized'') recharge value and the
simulated recharge value agree, then Eqs.~(3) and (4) are satisfied and the
partitioning is accepted. Equation 5 notes that all abstracted water
eventually contributes to ET. While this assumption may be violated at a
field scale, it likely holds at a regional scale in the Central Valley where
extensive conjunctive use and reuse of water occurs (Canessa et al., 2011).

To find the correct recharge and withdrawal partitioning, we ran a series of
trials in which the parameterized recharge was increased in
5\,mm\,year$^{-1}$ increments, from 20 (the first point in the left in Fig.~5
and the minimum value of recharge necessary to generate the baseline
$Q_\mathrm{d}$ of 20\,mm\,year$^{-1}$) to 115\,mm\,year$^{-1}$. With the
average $\Delta $GW and $Q_\mathrm{d}$ (Sect.~3.1), this corresponds to a
$\mathrm{GW}_\mathrm{WD}$ range of 60--155\,mm\,year$^{-1}$. The procedure
assumes only minimal differences exist in $Q_\mathrm{d}$ computed for the
baseline and trial simulations, an assumption that we verified by inspecting
irrigation simulation outputs. Since the Central Valley aquifer system is a
combination of unconfined and confined aquifers, we assume that groundwater
withdrawals are equally distributed between both types of aquifers (Fig.~2).
Because the CLM lacks a confined aquifer component, confined withdrawal is
taken from a hypothetical water store, which is constrained together with the
unconfined aquifer using Eq.~(4) and GRACE-estimated groundwater. Unconfined
withdrawals were taken from the saturated zone of the soil.

\section{Results and discussion}

\subsection{Existing model parameterizations and satellite-observed
hydrologic fluxes}

Monthly satellite-observed and simulated ET for the Central Valley showed
strong and differing seasonality (Fig.~3a). Satellite-observed monthly ET
ranged from 13\,mm month$^{-1}$ (December~2009) to 106\,mm (July~2005).
Seasonal maxima and minima of ET coincided with seasonal maxima and minima of
regional solar radiation and temperatures that control potential ET (solar
radiation and temperature data not shown). Over the entire 2004--2009 study
period, mean ($\pm$ 1 standard deviation) satellite-observed ET was
54.6\,$\pm$\,12.8\,mm\,month$^{-1}$ (655\,mm\,year$^{-1}$). The GLDAS-1,
NLDAS-2, and CLM-simulated ET was substantially lower than satellite-observed
ET (Fig.~3a), with a mean simulated ET of 23.3\,$\pm$\,5.0\,mm\,month$^{-1}$
(280\,mm\,year$^{-1}$). Simulated ET ranged from 19\,mm\,month$^{-1}$
(September~2008) to 69\,mm\,month$^{-1}$ (April~2006).
GLDAS-1/NLDAS-2/CLM-simulated seasonal maxima and minima of ET coincided with
maximal and minimal natural soil moisture availability following the end of
the winter rainy season and at the end of the dry summer season (Fig.~3c). On
an average seasonal basis, satellite-observed ET showed the greatest
difference from simulated ET in July, when satellite ET was
79\,mm\,month$^{-1}$ larger. In winter (November--February), observed ET
exceeded simulated ET by less than 10\,mm\,month$^{-1}$ (Fig.~3c).


%f3
\begin{figure}[t]
\includegraphics[width=8.3cm]{gmd-2015-28-f03.pdf}
\caption{\textbf{(a)} the comparison between the remote-sensing-estimated ET,
and nine GLDAS, NLDAS, and CLM models. The lines indicate the ensemble mean
while the shading indicates uncertainty around the ensemble mean,
\textbf{(b)} annual precipitation for the Central Valley, and \textbf{(c)}
monthly climatology for satellite-observed and modeled ET.}
\end{figure}

While the seasonality of satellite-observed and simulated ET was different,
the annual patterns of ET matched annual precipitation well, although
satellite-observed ET had considerably lower interannual variation than
simulated ET (Fig.~3). Annual precipitation ranged from 202 (2007 calendar
year) to 416\,mm\,year$^{-1}$ (2005 calendar year). Mean ($\pm$ 1 standard
deviation) calendar year precipitation for 2004--2009 was
315.8\,$\pm$\,84.8\,mm\,year$^{-1}$. Annual changes in groundwater vary
considerably from year to year, with a maximum increase of
120\,mm\,year$^{-1}$ in 2006 and a maximum decrease of 220\,mm\,year$^{-1}$
in 2007 (Fig.~4). Mean groundwater decrease across the entire study period is
approximately 60\,mm\,year$^{-1}$. Annual precipitation and groundwater
change are well correlated ($r=0.78$), with the largest groundwater decrease
occurring in one of the driest years in California history (2007) and the
largest increase in 2006 following a succession of wet years. Mean annual
satellite-observed ET showed less variation than precipitation, ranging from
624 in 2009 to 690\,mm\,year$^{-1}$ in 2005. Since precipitation in the
surface water source regions for the Central Valley (Sierra Nevada mountains)
is very well correlated with precipitation in the valley (Daly et al., 1994,
2008), variations in precipitation are also assumed to be variations in
surface water availability. Together, this lower variation in ET in spite of
higher variation in precipitation and surface water availability and the
inverse relationship between groundwater level change and precipitation is
consistent with the relatively steady water demand from Californian
agricultural crops, many of which are perennial crops with large, multi-year
investments (Ayars, 2013; Blank, 2000), and the long-standing practice of
increasing groundwater use to compensate for deficits in surface supplies and
precipitation (Howitt, 1991).



%f4
\begin{figure}[t]
\includegraphics[width=8.5cm]{gmd-2015-28-f04.pdf}
\caption{Annual groundwater change for the Central Valley derived from
GRACE.}
\end{figure}


%f5
\begin{figure}[t]
\includegraphics[width=8.5cm]{gmd-2015-28-f05.pdf}
\caption{Parameterized (guessed) groundwater recharge versus recharge
simulated in CLM 4 (see Sect.~2.3). The $x$ axis represents the trial
recharge used in Eq.~(4) to obtain GW$_{WD}$ and the $y$ axis represents the
output recharge from Eq.~(3). The intersection of the parameterized values
with simulated values (55\,mm\,year$^{-1}$) represents where recharge comes
to convergence  and is the value of recharge used to separate total water use
into ground- and surface-water pumping components.}
\end{figure}

\subsection{Application of groundwater and surface water in CLM and impact
on CLM-simulated ET}

The mean amount of additional water that is consumed or transpired under
irrigation in the Central Valley is 376\,mm\,year$^{-1}$ (satellite-observed
ET minus the mean GLDAS-1/NLDAS-2/CLM ensemble-simulated ET). The parameterized
recharge estimates plotted against CLM-simulated recharge are shown in
Fig.~5. Simulated recharge ($q_\text{recharge}$) showed a more dampened
response to a wide range of parameterized recharges, with simulated recharge
ranging from 47 to 66\,mm\,year$^{-1}$ across the parameterized recharge space
(20--115\,mm\,year$^{-1}$). The parameterized and simulated recharge comes to
convergence at approximately 55\,mm\,year$^{-1}$ (Fig.~5), which is the value
we used to partition applied surface water and groundwater. Using equation 4,
we calculated mean applied groundwater ($\mathrm{GW}_\mathrm{WD})$ as
95\,mm\,year$^{-1}$ over the 2004--2009 study period. Mean applied surface
water ($\mathrm{SW}_\mathrm{WD})$ was 281\,mm\,year$^{-1}$.

The model-optimized $\mathrm{SW}_\mathrm{WD}$ compares well with previous
remote sensing and high resolution inventory estimates of surface water
consumption in the Central Valley. For the 2004--2008 water years, Anderson
et al.~(2012) found a mean ($\pm$ uncertainty) surface water consumption of
291\,$\pm$\,32\,mm\,year$^{-1}$ using remote sensing and
308\,$\pm$\,7\,mm\,year$^{-1}$ using an inventory approach calculated from
dam releases into the Central Valley, canal exports to coastal basins to the
south, and outflow through the California Delta. The close comparison of
these values to $\mathrm{SW}_\mathrm{WD}$ gives us further confidence in our
optimization method and its underlying assumptions.

Figure~6 shows the impact of the irrigation water parameterization on
CLM-simulated ET compared to observational data. With the new parameterization,
monthly CLM-simulated ET ranged from a minimum of 10\,mm (December~2008) to a
maximum of 96\,mm (June~2006), with a mean of 48.3\,mm. The differences
between CLM-simulated ET and satellite-observed ET (CLM minus satellite)
ranged from $-$30 to 11\,mm\,month$^{-1}$ with a mean difference of
$-$6.3\,mm\,month$^{-1}$. There was low correlation between seasonality
(month) and the discrepancy between satellite-observed and non-irrigated
simulated ET ($r<$\,0.5) as assessed with a geometric mean regression.
Conversely, the relationship between satellite-observed monthly ET and CLM-simulated ET was excellent ($r=$\,0.95, slope\,$=$\,0.94,
intercept\,$=$\,$-$3.1\,mm\,month$^{-1}$).


%f6
\begin{figure}[t]
\includegraphics[width=8.5cm]{gmd-2015-28-f06.pdf}
\caption{Monthly ET from CLM 4 with the improved irrigation parameterization
when compared to observations. Lines indicate model or ensemble mean while
shading indicates uncertainty of the satellite-observed ET.}
\end{figure}

With respect to other hydrologic fluxes, simulated groundwater baseflow
($Q_\mathrm{d})$ changed little with irrigation over the 2004--2009 study
period (27\,mm\,year$^{-1}$ in experimental run versus 18\,mm\,year$^{-1}$ in
control -- data not shown). Surface runoff ($Q_\mathrm{S})$ changed more
considerably (68\,mm\,year$^{-1}$ in experimental run versus
38\,mm\,year$^{-1}$ in control), which is an expected consequence due to the
wet soil from irrigation leading to higher surface runoff. The small change
in $Q_\mathrm{d}$ despite additional irrigation concurs with GRACE-derived
groundwater changes, simulated reductions in groundwater in CLM, and previous
hydrogeologic observations that many rivers and streams in the Central Valley
are now losing streams due to long-term groundwater depletion, with some
wells in the southern Central Valley being over 1000\,m deep (Planert and
Williams, 1995). The larger increase in $Q_\mathrm{S}$ may reflect on the
ground spatial differences in cropping patterns and water management within
the Central Valley. For example, the northern part of the Central Valley
(Sacramento Valley) has extensive rice production that results in multiple
flooding and drainage events in the course of a production season (Hill et
al., 2006). Much of this water is reused further downstream (south). Other
cropping systems, particularly those in parts of the southern Central Valley
(San Joaquin Valley) affected by drainage issues, use tail water recovery
systems as required by state and local regulations, which minimize surface
runoff from irrigation (Schwankl et al., 2007).

%f7
\begin{figure}[t]
\includegraphics[width=8.5cm]{gmd-2015-28-f07.pdf}
\caption{Mean seasonal cycle from the CMIP5 suite of models compared against
satellite-observed ET. Solid line shows mean value of CMIP5 model members and
shaded region shows uncertainty (2 standard deviations around mean).}
\end{figure}

\subsection{Impact of parameterizations of irrigated agriculture in land
surface modeling}

The significant underestimation of peak growing season ET in irrigated
agricultural regions is not confined to the NLDAS/GLDAS and default CLM
models. Figure~7 shows the mean climatology of ET for the control runs of the
CMIP5 models over the Central Valley compared to satellite-observed ET. The
mean ($\pm$ 1 standard deviation) ET is
45.9\,$\pm$\,15.8\,mm\,month$^{-1}$. While the peak ET of the mean of the
CMIP5 ensemble is higher (68 vs. 48\,mm\,month$^{-1}$) and later (May vs.
April) than the NLDAS/GLDAS/CLM ensemble, the CMIP5 ET still is more than
100\,mm\,year$^{-1}$ lower than satellite-observed ET (550 vs.
655\,mm\,year$^{-1}$) and exhibits minima and maxima characteristic of the
natural hydrologic cycle. Furthermore, some of the improved closure between
CMIP5 and satellite-observed ET compared to NLDAS/GLDAS/CLM could be due to
substantially higher CMIP5-modeled ET during the winter. Despite the
relatively large uncertainty of the CMIP5 models over the Central Valley, the
satellite-observed ET for over half of the year is significantly outside of
the CMIP5 envelope.



Compared with previous parameterizations of irrigation water in the Central
Valley, our remote-sensing-based approach resulted in a lower consumed amount
of water than the soil moisture-based parameterizations (Kueppers et al.,
2007; Sorooshian et al., 2011) and a slightly higher amount of consumed water
than a global-inventory-based approach (Siebert et al., 2010; Lo and
Famiglietti, 2013). For the summer months of May--August, a high soil
moisture parameterization at field capacity (Kueppers et al., 2007) resulted
in an annual summer irrigation water consumption of 612\,mm\,summer$^{-1}$
whereas a variable soil moisture parameterization (Sorooshian et al., 2011)
resulted in a summer irrigation water consumption of 430\,mm\,summer$^{-1}$.
These values do not include potential water consumption from the shoulder
irrigation months of April, September, and October. The inventory data of
Siebert et al.~(2010) used in the Lo and Famiglietti~(2013) parameterization
was only about 25\,mm lower (350\,mm\,year$^{-1}$ vs. 376\,mm\,year$^{-1}$)
than our remote sensing parameterization, but the amount of consumed water
from groundwater (140\,mm\,year$^{-1}$) was substantially higher than our
applied groundwater (95\,mm\,year$^{-1}$). Furthermore, our
satellite-ET-derived estimate is also likely to be a lower envelope estimate
of applied water due to the slight increase in surface runoff observed in
CLM. The overestimation of ET and latent heat fluxes with the soil moisture
parameterization suggests challenges in using this type of parameterization;
however, soil moisture parameterization may become significantly more
feasible with precise and accurate regional and global soil moisture
observations from upcoming missions such as the Soil Moisture Active Passive
satellite, whose outputs are specifically designed to improve inputs to
numerical weather prediction and land surface models (Entekhabi et al.,
2010).

Currently, both inventory and remote-sensing-based approaches have
sufficiently low spatial and temporal resolution so that irrigation water
parameterization is typically done on interannual timescales for large
basins. This temporal resolution for water parameterization works well for
accurately modeling the hydrology of the Central Valley, likely due to the
lower amount of interannual variation in ET and the use of groundwater to
compensate for surface water deficits. However, it is unclear how well this
approach will work in irrigated regions where ET may be more variable due to
a lack of supplemental reservoirs and thus a necessary fallowing of land
during dry periods. Current and future missions (GPM, SMAP, SWOT,
GRACE Follow-On/GRACE II) have the potential to sufficiently improve the
resolution of satellite hydrologic products to enable annual quantification
of surface water and groundwater application at higher spatial resolution
(Biancamaria et al., 2010; Entekhabi et al., 2010; Smith et al., 2007; Zheng
et al., 2015). These higher resolution parameterizations may enable better
quantification of hydrologic impacts of changing management and cropping
patterns, including shifts in irrigation regimes and changes between annual
and perennial crops. Parameterizations from inventory methods may improve if
public monitoring and reports requirements become more widespread (similar to
those for Arizona's Active Management Areas -- see Jacobs and Holway, 2004).

\conclusions[Summary and conclusion]

We used satellite-based estimates of evapotranspiration (ET) and groundwater
change combined with precipitation data to constrain and parameterize the
additional water applied to a major irrigated agricultural region (Central
Valley, California, USA) for simulation of land surface fluxes using the
Community Land Model (CLM) version~4. We evaluated the baseline amount of
consumed water using a suite of nine land surface models/forcing data sets
and estimating the additional water consumed as a residual of current
satellite observations. We used an iterative solution of parameterizing and
then simulating groundwater recharge to partition the total water withdrawals
among ground and surface water. The additional water parameterization
resulted in CLM tracking the total amount and seasonality of ET closely. The
remote sensing parameterization of irrigation water consumption results in a
smaller total amount of water being consumed than in previous soil-moisture-based parameterizations.

The results emphasize the need for irrigation parameterization in land and
climate models to accurately assess land-atmosphere energy and mass fluxes
in regions with major anthropogenic modifications. Given the potential for
intense irrigation to modify regional climate (Kueppers et al., 2007) and to
enhance convection precipitation in downwind regions (Lo and Famiglietti,
2013), it is important that the additional water consumption from irrigation
is properly represented to better model the local and more distant impacts
of anthropogenic land surface modification. Particular emphasis should be
placed on evaluating irrigation impacts in less-developed regions with fewer
surface data constraints and different cultivation and irrigation practices
than the Central Valley. An improved parameterization will also be useful
for assessing regional climatic impacts of possible future changes in
irrigated agricultural regions due to increased logistical, political,
and/or economic restrictions on groundwater pumping or changes in surface
water use.

\Supplementary{pdf}

\begin{acknowledgements}
The GLDAS and NLDAS data used in this study were acquired as part of the
mission of NASA's Earth Science Division and archived and distributed by the
Goddard Earth Sciences (GES) Data and Information Services Center (DISC). A
portion of this research was conducted at the Jet Propulsion Laboratory,
California Institute of Technology, operated under contract with NASA. This
study was supported by the Ministry of Science and Technology, Taiwan,
(grants MOST 103-2111-M-002-006 and MOST 100-2119-M-001-029-MY5), by the
United States Department of Agriculture, Agricultural Research
Service\footnote{Note: The US Department of Agriculture (USDA) prohibits
discrimination in all its programs and activities on the basis of race,
color, national origin, age, disability, and where applicable, sex, marital
status, familial status, parental status, religion, sexual orientation,
genetic information, political beliefs, reprisal, or because all or part of
an individual's income is derived from any public assistance program. (Not
all prohibited bases apply to all programs.) Persons with disabilities who
require alternative means for communication of program information (Braille,
large print, audiotape, etc.) should contact USDA's TARGET Center at (202)
720-2600 (voice and TDD). To file a complaint of discrimination, write to
USDA, Director, Office of Civil Rights, 1400 Independence Avenue, S.W.,
Washington, DC 20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382
(TDD). USDA is an equal opportunity provider and employer.}, National Program
211: Water Availability and Watershed Management (project no.
2036-61000-015-00), and by the University of California Multicampus Research
Programs and Initiatives (MRPI). \hack{\newline} \hack{\newline} Edited by:
W.~Hazeleger
\end{acknowledgements}



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