Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications

Agricultural Water Management 227 (2020) 105854

Contents lists available at ScienceDirect

Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat

Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications

T

⁎

Ramesh Dhungela, , Robert Aikenb, Xiaomao Lina, Shannon Kenyonc, Paul D. Colaizzid, Ray Luhmanc, R. Louis Baumhardtd, Dan O’Brienb, Seth Kutikoffa, David K. Brauerd a

Kansas State University, Department of Agronomy, Kansas, USA Northwest Research-Extension Center, Kansas State Univ., Colby, KS, 67701, USA c Northwest Kansas Groundwater Management District #4, USA d USDA-ARS, Conservation and Production Research Laboratory, P. O. Drawer 10, Bushland, TX, 79012, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: BAITSSS water rights irrigation scheduling remote sensing NLDAS advanced geospatial modeling next generation evapotranspiration Ogallala Aquifer crop water use GMD4 Sheridan 6 LEMA

Research that incorporates information from satellites into conventional biophysical models has great importance and interest. Comprehensive crop water algorithms can help track crop stress, schedule irrigation, and acquire water right information for effective water management and increased productivity in semi-arid and arid environments. Overall objective was to utilize the automated biophysical surface energy balance model BAITSSS (Backward‐Averaged Iterative Two‐Source Surface temperature and energy balance Solution) to understand critical agricultural water management issues. BAITSSS served as an advanced digital landscape crop water tracker and irrigation scheduler to simulate hourly landscape evapotranspiration (ET) at 30 m spatial resolution. North American Land Data Assimilation System (NLDAS) weather data and Landsat-based vegetation indices were inputs of BAITSSS to simulate surface energy balance components along with irrigation (Irr). Two agricultural-dominated groundwater regions of northwest Kansas, USA within a section of the Ogallala aquifer were studied during a five-year period (2013–2017). We compared model-simulated irrigation to reported within water right management units (WRMU). The sum of reported irrigation and precipitation (P), representing inseason water supply, was also compared to model simulated ET as an indicator of well-watered ET. The model was able to simulate reasonably ET values, and irrigation quantities, and to differentiate various spatial distribution patterns of crops within WRMU. However, unknown water management, within WRMU, constrained explicit inference of actual ET and irrigation amounts. The model appears suitable for quantifying the upper bound of in-season water supply (irrigation plus P) expected for well-watered crops in the U.S. Central High Plains. A WRMU exhibiting significantly different in-season water supply than the simulated ET may present opportunities to modify irrigation rates or to gain inference about deficit irrigation.

1. Introduction Groundwater depletion, in the coming decades, is expected to pose challenges to effective water management by state and local governments, and water managers (Cantor et al., 2018; Kiparsky et al., 2016; Kirchhoff and Dilling, 2016; Niles and Wagner, 2017; Peck, 2015). A response to this challenge in Kansas, USA was the creation of Local Enhanced Management Areas (LEMA) approved by the 2012 Legislature of Kansas (K.S.A. 82a-1041 (a)) (Deines et al., 2019). The Northwest Kansas Groundwater Management District #4 (GMD4) implemented restrictions in water allocations to reduce groundwater depletion by 20% for a five-year period (Whittemore et al., 2016).

⁎

Similarly, the Sustainable Groundwater Management Act (SGMA) of California is designed to prevent overdraft and bring groundwater basins into balanced levels of pumping and recharge (Aladjem and Sunding, 2015; Kiparsky, 2016; Leahy, 2015). The largest consumption of fresh water is by the agriculture sector (Famiglietti, 2014), which provides food for the growing population and maintains the livelihood of farmers. Effective water management in the agricultural landscape is required to simultaneously cope with frequent drought events and declining groundwater while sustaining agricultural production. The Ogallala Aquifer region serves as an example of how water management for agricultural production needs to adapt if irrigation is going to be sustainable (Araya et al., 2019; Basso et al., 2013; Butler et al., 2018;

Corresponding author. E-mail address: [email protected] (R. Dhungel).

https://doi.org/10.1016/j.agwat.2019.105854 Received 1 June 2019; Received in revised form 7 October 2019; Accepted 8 October 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Deines et al., 2019). A critical need to accomplish effective water management is a means to effectively monitor water management at a larger scale with accuracy. Ideally, these efforts should benefit farmers, water managers, and policymakers. Various large-scale remote sensing based evapotranspiration (ET) algorithms are frequently used to quantify crop water use and provide water use/rights information (Allen et al., 2005, 2003; Anderson et al., 2012; Calera et al., 2017; Irmak et al., 2012; Melton et al., 2012; Senay et al., 2016). Most of these instantaneous ET models were constructed through atmospheric interaction processing, but lack water balance and irrigation components in the energy balance algorithm. Other limiting factors include time-consuming and data-intensive ET modeling, and a lack of field scale data (Du and Sun, 2012; Tang et al., 2010). In summarizing the impact of U.S. irrigated agriculture, Schaible and Aillery (2012) observed that 7.5% of all domestic crop and pasture land was irrigated to produce ∼55% of all crop value. Nearly half of that water was supplied from aquifers. The High Plains or Ogallala Aquifer underlies eight Great Plains states from Texas to South Dakota and accounts for ∼ 27% of all U.S. irrigated land with ∼30% of the total U.S. groundwater withdrawals (Dennehy, 2000). Our research objective was to utilize a landscape scale energy balance algorithm to understand and address agricultural water management issues in NW Kansas. Some of these issues are:

Cp ρa ⎛

LEc =

⎜

γ

⎝ rac

e o c − ea ⎞ + rah + rsc ⎠ ⎟

(2)

where γ is psychrometric constant, ras is aerodynamic resistance between the substrate and canopy height (d + zom), d is zero plane displacement, zom is roughness length of momentum. eos and eoc are saturation vapor pressure at the soil surface and canopy, respectively, and ea is ambient vapor pressure.

Hs =

Hc =

ρa cp (Ts − Ta) (3)

rah + ras ρa cp (Tc − Ta)

(4)

rah + rac

where Ts and Tc are simulated soil surface temperature and canopy temperature, respectively, ρa is atmospheric density, cp is specific heat capacity of moist air, rah is aerodynamic resistance between d + zom and measurement height of wind speed (uz), and rac is bulk boundary layer resistance of vegetative elements in the canopy. Canopy resistance is calculated as such:

rsc =

R c _ min LAI fc

F1 F2 F3 F4

(5) −1

where Rc_min (40 s m ) is the minimum value of rsc, LAI is leaf area index, fc is fraction of canopy cover, and weighting functions representing plant response to solar radiation (F1), air temperature (F2), vapor pressure deficit (F3), and soil moisture (F4), each varying between 0 (infinite resistance) to 1 (no resistance). The minimum canopy resistance (Rc_min) for all irrigated crops is assumed as 40 s m−1 (Dhungel et al., 2019a; Kumar et al., 2011) for most of the simulation period; rsc of each pixel is computed in a surface energy balance based on remote sensing based vegetation indices. Canopy senescence is represented by increased Rc_min for a short period in the late season (eight days: DOY 250–258) (Dhungel et al., 2019a). Other simplifications include use of a constant albedo (α) of 0.2 for soil and 0.15 for the canopy and constant emissivity (ε) of 0.98 for both soil and canopy as per earlier discussion (Dhungel et al., 2019a). The composite surface energy balance equations for LE and H are shown in Eqs. (6) through (9).

1 evaluate water use with full irrigation of crops within the constraints imposed by a water right. 2 potential economic and policy implications of surface energy balance model results. 2. Methodology 2.1. BAITSSS – advanced automated landscape digital crop water tracker and irrigation simulator We used BAITSSS (Backward-Averaged Iterative Two-Source Surface temperature and energy balance Solution) as a tool for tracking crop water use. BAITSSS (Dhungel et al., 2019a,b, 2016) is a two-source energy balance and two-layer soil water balance biophysical ET algorithm, driven by weather variables and remote sensing based canopy formation. BAITSSS is different from widely used remote sensing based instantaneous ET models such as METRIC and SEBAL (Allen et al., 2007; Bastiaanssen et al., 1998) that utilize extreme pixel concepts (that is, hot and cold pixel) to compute landscape ET. BAITSSS iteratively solves energy and soil water balance along with irrigation at a 30 m spatial resolution in an hourly step, providing comprehensive and detailed surface energy balance algorithms (refer Dhungel et al. (2019a, b), 2016 for complete set of equations). The modeling scheme of latent heat (LE, subscripts s for soil and c for canopy) and sensible heat flux (H) of the aerodynamic equations in BAITSSS are shown in Eqs. 1–4 (Fig. 2a), respectively. The surface temperatures (Ts; soil surface temperature and Tc; canopy temperature) in BAITSSS are iteratively solved at each time step inverting Eqs. 3 and 4. Unlike the majority of remote sensing-based ET models, BAITSSS does not utilize thermal-based surface temperature as an external input in surface energy balance. The pixel scale variations of surface roughness (zom, zoh, Z1), zero plane displacement (d), and the height of canopy (hc) in BAITSSS are estimated based on vegetation indices as described by Choudhury and Monteith (1988). BAITSSS uses a Jarvis-type formulation (Eq. 5) to compute canopy resistance (rsc, Jarvis, 1976; Kumar et al., 2011) to quantify the transpiration (T) and a simplified soil surface resistance (rss) formulation (Sun, 1982) to quantify evaporation from the soil surface (Ess).

LE = LEc fc + LEs (1 − fc )

(6)

H = Hc fc + Hs (1 − fc )

(7)

L=−

cp Ta ρa u *3 kgH

(8)

where L is Monin-Obukhov length, u* is the friction velocity, g is acceleration due to gravity, k is von Karman constant i.e. 0.41. The aerodynamic resistance (rah) of combined surface energy balance is computed from Eq. 9.

rah

⎡ln = ⎣

( ) − ψ ⎤⎦ ⎡⎣ln ( ) − ψ z−d z om

m

z−d Z1

h

+ ψh

( ) ⎤⎦ Z1 L

k 2 uz

(9)

where ψm is stability correction of momentum, ψh is stability correction of heat, zom is roughness length of momentum, Z1 is integration constant. The water balance at the soil surface (θsur; 100 mm) and root (θroot; 100 mm–1000 mm) are computed using Eq.s (10) and (11), respectively as described by Dhungel et al. (2016). Fig. 2b shows the volume of the soil water is controlled by soil and vegetation parameters, where both θsur and θroot are restricted to θfc.

θsur = θsur (i − 1) +

(P + Irr − Srun ) − Ess − Te − DPe + CR e dsur

(10) 3

LEs =

Cp ρa ⎛ ⎜

γ

⎝ ras

e o s − ea ⎞ + rah + rss ⎠

−3

where i is current time step, i-1 is previous time steps (m m ), Srun is surface runoff (mm), dsur is soil surface depth (mm), DPe is deep percolated water from the upper soil layer to the root zone (m3 m−3), CRe

⎟

(1) 2

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

evaporation calculation from a bare soil surface (Dhungel et al., 2019a). Irrigation (Irr) will be applied when θroot gets below a given threshold moisture content (θt; Eq. 14) as shown in Fig. 2c. Threshold moisture content (θt) is computed based on readily available water (RAW) and θfc as described by Dhungel et al. (2016). The total available water (TAW) in Fig. 2c is the soil moisture between θfc and θwp.

is the capillary rise from the root zone into the 1st soil surface, and Te is transpiration from the soil surface layer (mm). CRe and Te are neglected to simplify the soil water balance.

θroot = θroot (i − 1) +

(P + Irr − Srun ) − T − Ess − DP + CR droot

(11)

where droot is rooting depth (mm), DP is deep percolation below the root zone (m3 m−3), and CR is the capillary rise from the 3rd layer to the root zone. Capillary rise (CR) from the 3rd layer into the bulk layer (2nd) is also neglected. The fc is computed using NDVI (Eq. 12):

fc =

NDVI − NDVImin NDVImax − NDVImin

( θfc − θroot (i) ) droot if θroot (i) < θt Irr (i) = ⎧ ⎨ ⎩ 0 if θroot (i) ≥ θt

The irrigation amounts and frequency are generally modeled (Pereira et al., 2003) as an unknown variable in large-scale ET simulation (Er-Raki et al., 2010). Sensitivity analysis using BAITSSS at the Bushland, Texas (TX) lysimeter site for fully irrigated corn with drip irrigation showed negligible effects of irrigation rules in the final ET simulation (Dhungel et al., 2019b). In this study, we assumed a full irrigation (i.e. a MAD fraction of 0.5, see below) with no attempt to distinguish deficit, non-uniform distributions of water within management units (i.e. ‘split-pivot irrigation, (Klocke et al., 2011, 2006) nor dryland conditions, though these are common crop water management practices in northwest Kansas (Kisekka and Aguilar, 2016, others). Full irrigation is likely to increase ET as compared to deficit irrigation, but current structure of BAITSSS provided critical information about crop water requirements based on vegetation indices and growth stages. Irrigation was simulated for all pixels based on irrigation rules that maintained a management allowable depletion (MAD) fraction of available soil water storage capacity. We assumed irrigation was applied to the surface layer and infiltrated into the root zone, to mimic the behavior of sprinkler irrigation, which is common in this region (Lilienfeld and Asmild, 2007). We assumed rooting depth as 1000 mm and a MAD fraction of 0.5 based on KanSched (Clark et al., 2000) guidelines for all crops and growth stages inside the study area. The adopted MAD value partially delays irrigation developing a framework of deficit irrigation. The current study doesn’t differentiate MAD and rooting zone for each field and crop as well as albedo because such information is not readily available for all fields. The rooting depth and MAD may vary among the crops, growth stages and may influence the timing of irrigation, however, final ET is less likely to be affected in full irrigated treatment (Dhungel et al., 2019b). The maximum application of irrigation water was limited to 40 mm in a single event as adapted previously by Eheart and Tornil (1999). If the deficit in soil moisture is not fulfilled by 40 mm and soil moisture is still below θt, the rest will be applied in the next time step. Even in fully irrigated lands, the irrigation rules actually used by farmers are likely to differ from those assumed here.

(12)

Maximum (NDVImax) and minimum (NDVImin) values of NDVI was taken as 0.85 and 0.15, respectively for entire images. BAITSSS utilizes an empirical LAI equation (Eq. 13) (Allen et al., 2012), where SAVI is soil adjusted vegetation index:

11. SAVI 3 for SAVI ≤ 0.817 LAI = ⎧ ⎨ ⎩ 6 for SAVI > 0.817

(14)

(13)

The automated BAITSSS tool utilized Python-based libraries (bindings of OGR, NumPy, scipy, and GDAL Geospatial Data Abstraction Library, (“http://www.gdal.org” n.d,))) with a package manager conda (“https://docs.conda.io/en/latest/” n.d) and shell scripting for the various operations on the file system. BAITSSS is capable of computing landscape ET along with irrigation within a pixel (i.e. 30 m spatial resolution) throughout the USA using Landsat based vegetation indices (Fig. 1a) and NLDAS weather input (Fig. 1b), and soil parameter from SSURGO (θfc and θawc, Fig. 1c). 2.2. Initial soil moisture and irrigation The BAITSSS model was utilized to quantify expected crop water use in agricultural landscapes. Identical initial conditions and irrigation rules were applied throughout the simulation control volume. The control volume refers to the soil and root layers. These assumptions were applied due to lack of information about soil water distribution (at surface and root-zone) and irrigation in the spatial and temporal extent and resolution (i.e. 30 m, hourly). In this study, the initial volumetric water content at the soil surface profile (top layer) was considered to be relatively dry (0.05 m3 m−3) while root zone soil moisture was considered to be at field capacity (θfc) as reported in previous research (Lamm et al., 2017). Earlier study indicated that relatively dry soil profile helped to curb the uncertainty, i.e., overestimation in

Fig. 1. Illustration of automated BAITSSS primary inputs from a) LAI from Landsat (July 21, 2017; L8), b) hourly air temperature (Ta) from NLDAS (21 May 2016 (17:00) in oC), c) aviable water content (θawc) from SSURGO (m3 m−3). 3

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Fig. 2. Modeling scheme for a) latent heat (LE), sensible heat flux (H) of BAITSSS surface energy balance components, b) soil water balance of soil surface control volume (dashed line), root zone control volume (thick solid line) that includes the soil surface and c) typical irrigation sub-model.

combinations of these crops (e.g., under ‘split-pivot’ management) were grown under irrigation during the simulation period in the study area.

2.3. Study area and data The study area was located in northwest Kansas in a semi-arid, advective environment within a section of Ogallala aquifer. Fig. 3 shows two areas of interests (AOI) in the study area: Township (TWP) 9S-41 W (∼ 58 km−2) and Sheridan 6 (SD-6) LEMA (∼ 483 km−2). These AOIs represent larger groundwater depletion (TWP 9S-41 W) (Order of decision returning the local enhanced management plan with proposed modifications, 2017) and restricted irrigation allocations (SD6) (Order of designation approving the Sheridan 6 local enhanced management area within groundwater management district no. 4, 2013). The in-season ET aggregation period was between May 10 (DOY 130) and September 15 (DOY 258) for 2013 to 2017, which represents the typical irrigated corn growing season, the major focus of this study. In this case, ‘in-season’ assumes that the reported annual irrigation amount was applied during this period of time. We recognize that applications could occur prior to planting or after harvest, within the calendar year. Other crops were also present in the agricultural landscape, with differing onsets and durations of the growing seasons. Calculating the seasonal ET of other crops would require information about their respective growing seasons, which likely differ from that expected for irrigated corn production. Farm-reported data revealed that corn, soybean, wheat, grain sorghum, sunflower, barley, oats, and

2.3.1. Water right information Water right parcels were identified by the Water Information Management and Analysis System (WIMAS) (Kansas Department of Agriculture and Kansas Geological Survey, 2013) (“http://hercules.kgs. ku.edu/geohydro/wimas/index.cfm” n.d). Water right here defined as reported applied water within the specified boundaries for legal purpose, for instance, within a center pivot. Inspection of Landsat imagery revealed 152 parcels (SD-6) and 13 parcels (TWP (S-41 W) which had clearly identified boundaries (as indicated by NDVI values). Farm-reported records associated with the water right parcels identified in SD-6 and TWP 9S-41 W (Fig. 3) are considered to be representative of the region. The farm-reported irrigation application was computed by dividing the reported total acre-feet of water diverted from groundwater (pumpage quantity) by the reported area associated with the WRMU. These WRMU were annotated with arbitrary index values and used to extract simulated mean ET values from BAITSSS output (presented in section 3.3). The WRMU were consistent with the boundary of the center pivot sprinkler systems. Water distribution within these water right units are not explicitly known, though, in some cases, ‘split-pivot’ management can be inferred from Landsat imagery. 4

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Fig. 3. Study area with water rights units at Sheridan 6 (SD-6) and Township (TWP) 9S-41 W of Groundwater Management District (GMD), and State of Kansas.

from NLDAS (https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/ NLDAS_FORA0125_H.002/1979/006/ n.d.). Parameters included wind speed (uz) at 10 m, air temperature (Ta) at 2 m, specific humidity (qa) at 2 m, incoming solar irradiance (Rs↓), precipitation (P), and surface runoff (Srun) at the surface (Figs. 1b shows an illustration of NLDAS data). The available soil water capacity (θawc) and soil volumetric water content at field capacity (θfc) metadata, associated with spatially referenced soil mapping units, were acquired from SSURGO from SSURGO (https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/ survey/geo/, n.d) (Fig. 1c for illustration). The final output of landscape ET and other variables from BAITSSS are at 30 m matching Landsat spatial resolution and hourly temporal resolution.

2.4. Satellite, soil and surface climate data Landsat 7 (L7) and 8 (L8) (path 31 and row 33) images were utilized to provide the information required to calculate vegetative indices (Table 1). Inference of daily canopy formation was provided by linear interpolation between successive Landsat images. The quality of the seasonal canopy formation and vegetation indices was influenced by the availability of images due to factors including cloud cover and failure of the scan line corrector (SLC) component of Landsat 7 (Chen et al., 2011). Stripes of missing values in Landsat 7 imagery were filled by interpolation (Kramber et al., 2011) before proceeding with surface energy balance calculations. The number of Landsat 7 scenes was minimized to avoid missing values. Weather data (hourly, ∼ 12.5 km spatial resolution) were taken

Table 1 Acquired Landsat images of path 31 and row 33 and dates for SD-6 and TWP 9S-41 W. SD-6

TWP 9S-41W

Year

Dates and Landsat

Year

Dates and Landsat

2013

April 21 (L8), April 29 (L7), June 16 (L7), June 24 (L8), July 10 (L8), August 27 (L8), September 28 (L8) May 10 (L8), June 11 (L8), July 05 (L8), July 29 (L8), August 30 (L8), September 23 (L7) April 27 (L8), April 29 (L8), June 30 (L8), July 16 (L8), August 01 (L8), August 25 (L7), September 10 (L7), October 12 (L7) April 13 (L8), May 23 (L7), May 31 (L8), June 16 (L8), July 26 (L7), August 27 (L7), September 28 (L7) May 10 (L7), June 19 (L8), July 05 (L8), July 21 (L8), August 30 (L7), September 15 (L7)

2013

April 07 (L8), June 08 (L8), June 24 (L8), July 10 (L8), July 26 (L8), August 27 (L8), September 28 (L8) May 10 (L8), June 11 (L8), June 19 (L7), August 06, (L7), August 30 (L8), September 23 (L7) April 27 (L8), June 06 (L7), June 22 (L7), July 16 (L8), August 01 (L8), September 02 (L8), October 12 (L7) April 13 (L8), May 23 (L7), June 16 (L7), July 18 (L8), August 03 (L8), September 28 (L7) April 24 (L7), May 26 (L7), June 19 (L8), July 05 (L8), July 21 (L8), August 14 (L7), September 07 (L8), October 25 (L8)

2014 2015 2016 2017

2014 2015 2016 2017

5

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

values of this sampled pixel during the simulation period; the thick black is the mean of these variables. The mean or cumulative value, standard deviation (SD, among hourly values), and skew of these variables were calculated from all hourly values for a simulation run of a given year (Table 2).

2.5. Statistical methods Reported crop, annual water application, and application area were utilized for conducting statistical analysis. We didn’t assess the accuracy of these reported crop and application area in this study, however, the Kansas Department of Agriculture, Division of Water Resources (DWR) and Kansas Geological Survey (KGS) is responsible of developing WIMAS database. When no crop was specified for a given year, the WRMU (water right management unit) was not included in subsequent analysis. The frequency of a given crop type, was calculated as the number of WRMU reported with the given crop type, divided by the total number of WRMU with complete information, for the given year. Significant differences in crop distributions among WRMU, relative to the five-year (2013–2017) mean distribution, were tested using the Chisquare statistic (SAS 9.4 Proc Frequency). Systematic effects of crop, year and interactions were evaluated by analysis of variance (SAS v 9.4. SAS Institute, Cary, NC.). Confidence bands (95%) were calculated, for each year, with respect to annual mean values of applied irrigation (Irr App), simulated irrigation (Irr Sim), gridded precipitation (P) and simulated ET for the WRMU reported as cropped to corn. Mean values of these parameters, reported for WRMU planted to other crops, were considered significantly different from that of corn when the mean values occurred outside the confidence bands constructed for corn. Significance levels for all statistical parameters were tested at the 5% probability level. We evaluated simulated Irr (Irr Sim) compared with reported applied Irr (Irr App) as well as simulated ET and applied Irr plus P assuming simplified seasonal water balance as adopted by Malek et al. (1992).

3.1.1. Input variables Fig. 4a to 4f show the seven-day moving average of weather variables and linearly interpolated daily vegetation indices (NDVI and LAI). Results indicate that the range of the maximum values of these moving averages was generally consistent among the years (Ta: 30 to 32 °C, Rs↓: 321 to 332 W m−2, uz: 6.1 to 7.2 m s-1, qa = 0.01 to 0.015 kg kg-1, NDVI: 0.77 to 0.84, and LAI: 3.5 to 5.1 m2 m−2). The NDVI and LAI gradually increased from negligible at the start of simulation to maximum indicating canopy formation and senescence. The maximum NDVI was 0.85 (Fig. 4e) and maximum LAI was 5 m2 m−2 (Fig. 4f) at maturity. Dhungel et al. (2019b) indicated that estimated vegetation indices from remote sensing data and the adopted equation in BAITSSS may be biased when compared to ground based measured data. Farmreported data indicated that the field containing the sampled pixel was cropped to corn (2013, 2016, and 2017) and soybean (2014 and 2015). The seasonal changes in LAI for pixel, when cropped to corn, were consistent with LAI measured for corn at Bushland, TX in 2016 (Dhungel et al., 2019a). Gao et al. (2017) discussed the characteristics of NDVI of corn and soybean from Landsat data for the year 2011 in Dallas County, Iowa, where the maximum NDVI of both crops was 0.85. In this study, the analysis of crop ET is based on the vegetation indices derived from Landsat imagery. Any differences between the actual and simulated representation of crop canopy are expected to propagate to the final calculation of ET (Dhungel et al., 2019b).

3. Results and discussion 3.1. Representative daily flux

3.1.2. Major output variables This study focuses attention on water balance components. Therefore, energy balance flux components, simulated surface temperatures, and resistances, which are analyzed in Dhungel et al. (2019a) are not presented. Seasonal changes in cumulative daily values of T, Ess, ET, Irr, P, and mean daily values of soil moisture at the root zone (θroot;

Temporal information for single representative pixel in SD-6 (100° 38′ 22″ W, 39° 21′ 38″ N) is shown in Fig. 3 (red circle). Our analysis demonstrates the impact of input variables on the dynamics of final ET. The shaded areas in Figs. 4,5, and 6 show the maximum and minimum

Fig. 4. 7-day moving average plots of a) air temperature (Ta), b) incoming solar irradiance (Rs↓), c) wind speed (uz), d) specific humidity (qa) from NLDAS, e) daily mean normalized difference vegetation index (NDVI), and f) leaf area index (LAI) from Landsat at SD-6 (100° 38′ 22″ W, 39° 21′ 38″ N) between May 10 and September 15, Kansas, USA. Shade represents 5-year maximum and minimum and the black line represents mean value. 6

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Fig. 5. Simulated daily cumulative plots of corn a) transpiration (T), b) evaporation (Ess), c) mean soil moisture at root zone (θroot), d) mean soil moisture at surface (θsur), e) evapotranspiration (ET), f) gridded precipitation (P), and simulated irrigation (Irr; bar plot), daily cumulative plots of soybean g) through l), respectively of sampled pixel at SD-6 (100° 38′ 22″ W, 39° 21′ 38″ N) between May 10 and September 15 in SD-6, Kansas, USA. Shade represents 5-year maximum and minimum and the black line represents mean value.

P and irrigation events. The daily maximum ET of 12.4 mm occurred on DOY 200 in the year 2016 (Fig. 5e). The mean maximum ET generally ranged between 8 and 10 mm (Fig. 5e and k) for both crops. Under the irrigation decision rule, the first in-season simulated irrigation event occurred around DOY 170 (mid-June) in the year 2015 (Fig. 5l) (on average, start irrigating around June 15th as per the field). The maximum daily precipitation was 55 mm in the year 2014. Table 2 shows the annual mean and cumulative values of these variables. The standard deviation (SD) and skew provide a measure of the yearly variability of these variables. The SD and skew were in general similar indicating the consistency of these variables among the years (Table 2). The skew of qa, Ta, LAI, NDVI, and θsur showed negative signs indicating infrequent periods with smaller values, typically at the start of grown season compared to the maturity (Table 2, Fig. 4). A detailed discussion of BAITSSS was presented previously for a fully irrigated corn field at a lysimeter site near Bushland, TX (Evett et al., 2016) for the year 2016 (Dhungel et al., 2019b, 2019a). This study adopts a similar set-up and assumptions to the Bushland study

100–1000 mm) and surface (θsur; 100 mm) layers for the sampled pixel are shown in Fig. 5. 3.1.2.1. Corn and soybean. As mentioned earlier, the field containing the sampled pixel was cropped to either corn or soybean during the 5year study period. We didn’t conduct separate analyses for each individual crop as there were minor variations in vegetation indices among years. However, we separated figures based on crop. Fig. 5a–f showed the results for corn and Fig. 5g–l for soybean. The partitioned ET showed a large variation in Ess due to the differences in quantity and frequency of P during partially canopy closure (Fig. 5b, f, and Fig. 5h, l). The simulated θroot remained smaller than θfc at the start of the simulation (Fig. 5c, 5i). Later in the simulation, θroot decreased to values near the threshold moisture content (i.e. 0.17 m3 m−3) that would prompt a simulated irrigation event. The amount and frequency of P and simulated Irr (40 mm per application, Fig. 5f and l) were not sufficient to increase θroot to near θfc values. The θsur remained large, under conditions of full canopy cover (Fig. 5d and j) because of multiple 7

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Table 2 Summary statistics of the sampled pixel. Year

Rs↓ W m−2

P mm

Mean* 2013 275.31 319.9 2014 270.81 481.3 2015 277.02 259.3 2016 279.12 306.5 2017 276.74 376.2 Standard deviation (SD) 2013 312.51 0.71 2014 310.85 1.18 2015 314.78 0.60 2016 317.09 0.70 2017 313.69 0.92 Skew 2013 0.68 11.33 2014 0.72 16.34 2015 0.67 12.06 2016 0.67 13.28 2017 0.67 14.20

qa kg kg−1

uz m s−1

Ta o C

LAI m2 m−2

NDVI

θroot m3 m−3

θsur m3 m−3

ET mm

T mm

Ess mm

Irr Sim mm

0.009972 0.009378 0.010658 0.011246 0.010202

4.95 4.80 4.71 5.01 4.49

25.82 24.71 24.90 24.91 24.13

2.47 2.34 2.66 1.96 2.40

0.60 0.55 0.62 0.53 0.58

0.22 0.22 0.21 0.22 0.22

0.16 0.17 0.18 0.15 0.19

785 750 857 746 772

599 518 648 531 558

186 232 210 215 214

440 280 560 360 400

0.00286 0.00231 0.00283 0.00280 0.00252

2.20 2.21 2.08 2.14 1.99

6.48 7.30 7.06 6.74 6.22

1.60 2.05 1.67 1.39 1.75

0.21 0.24 0.21 0.21 0.25

0.03 0.03 0.03 0.03 0.03

0.09 0.09 0.07 0.09 0.08

0.29 0.28 0.31 0.29 0.29

0.26 0.25 0.28 0.24 0.26

0.09 0.12 0.09 0.09 0.11

2.38 1.90 2.68 2.15 2.27

−0.15 −0.45 −0.24 −0.40 −0.19

0.59 0.60 0.55 0.57 0.65

−0.05 −0.41 −0.37 −0.32 −0.22

−0.36 0.19 −0.16 −0.16 −0.16

−0.58 −0.09 −0.76 −0.42 −0.54

0.53 0.21 0.95 0.56 0.50

−0.43 −0.28 −0.37 −0.21 −0.80

1.04 1.07 0.86 1.19 1.05

1.30 1.66 1.16 1.41 1.46

2.92 2.38 2.15 1.76 2.56

16.70 20.97 14.78 18.48 17.52

* cumulative value for P, ET, T, Ess, and Irr replacing mean, rest as indicated in Table.

illustrate spatial differences in the expected seasonal water use of wellwatered crops at the landscape scale. Simulation ET values ranged from 220 to 980 mm, despite the assumption of full-irrigations (i.e., MAD fraction of 0.5). With similar environmental conditions, variations in ET, within a given year, were primarily controlled by variations in vegetation indices. Weather conditions likely contributed to variability in simulated ET among years. Operationally in the BAITSSS algorithm, increased NDVI is linked to increasing LAI (non-linear) and canopy fraction (fc, linear). The Jarvis function for canopy resistance (rsc) results in a smaller rsc and increased T with increasing LAI. The fc parameter indicates the relative contribution of Ess and T to ET; with small fc increasing the contribution of the Ess components. A maximum LAI value of 6 m2 m−2 corresponded to a maximum NDVI value of 0.90 in this study (not shown). The minimum values of ET appear to represent non-cropped areas, where small NDVI should correspond with small fc, small LAI, large rsc and large contribution of Ess to ET. Under these conditions, ET would primarily be influenced by P, as irrigation would not be triggered by depletion of water retained in the root zone soil layer. Other factors affecting spatial variation in ET could include undetected cloud cover and inconsistency in interpolated Landsat 7 stripes. Fig. 8 shows a magnified section of the sampled WRMU, where the distribution of irrigation water is not known. We suggested two (of many) possible spatial distribution patterns as interpretations. Figs. 8 are consistent with six center-pivot sprinkler systems, representing WRMU. Cumulative ET for the lower left and lower center WRMU (Fig. 8a) exhibit limited within-field variation, consistent with uniform water distribution. In contrast, ET for the remaining units appears to display a bi-model distribution, split approximately halfway through the WRMU. Klocke et al. (2006) described the split-pivot strategy for optimizing the use of a limited water supply, e.g., with a portion of the field receiving full irrigation and the remainder receiving limited or no irrigation. Simulated mean ET values in these WRMU tended to range between 600 mm–800 mm. To understand these variabilities, we sampled vegetation indices LAI and NDVI for the year 2013. Results indicated that some crops are late planted (upper left two center pivots, Fig. 8a), which may be one of the factors affecting final ET within WRMU. Section 3.3 presents the overall statistics within the WRMU based on the limited field observation data.

using gridded NLDAS and Landsat based vegetation indices. We hypothesized the behavior and capability of the model should be comparable to that at Bushland within a similar environment. Further, it is expected the variations in vegetation indices should help account for differences between this study that has several different crops and the Bushland study with only corn. Bushland results (Dhungel et al., 2019b, 2019a) with corn indicated that BAITSSS was able to estimate seasonal cumulative ET relatively accurately with 4% positive bias, and daily and hourly RMSE of 0.85 mm and 0.10 mm (r2 = 0.9 for both daily and hourly). The comparison of BAITSSS with the lysimeter with sorghum also indicated high accuracy with daily RMSE = 0.80, r2 = 0.87 at Bushland, TX (working manuscript). However, when gridded data (NLDAS weather data and Landsat based vegetation indices were used, the accuracy was reduced, i.e., bias of seasonal cumulative ET increased up to 13% positive and RMSE increased in both daily and hourly scale with 1.64 mm d−1 and 0.14 mm h−1 (r2 = 0.74 daily and 0.81 hourly), respectively, for corn. The study concluded that increase in the positive bias in ET modeling from gridded data was due to larger uz, Rs↓, warmer Ta, and smaller actual vapor pressure (ea) (Dhungel et al., 2019b). Furthermore, smaller LAI from remote sensing contributed to a negative bias in ET modeling and vice versa. Fig. 6a and 6c show the simulated seasonal cumulative ET, simulated Irr, and P of the corn and Fig. 6d and 6f of soybean. The largest ET was observed in 2015 (857 mm, Fig. 6d) and the smallest occurred in 2016 (746 mm; Fig. 6a, Table 2). The largest P was 481 mm (2014, Fig. 6e) coincided with the smallest Irr of 280 mm (2014, Fig. 6f). The large vegetation indices during the early and mid-season in 2015 likely contributed to the larger seasonal cumulative ET of that year. The P in 2015 was least among years, 259 mm (Fig. 6e), corresponding with largest simulated Irr for that year (560 mm; Fig. 6f). In practice, irrigation applications typically ended prior to crop maturity (around the first week of September typically for corn), as observed in Bushland, TX (Dhungel et al., 2019b). However, the irrigation sub-model in BAITSSS didn’t implement these local practices and irrigation was applied throughout the simulation period possibly leading to overestimations relative to actual applications.

3.2. Landscape cumulative ET analysis Cumulative ET, as simulated over the agricultural landscape, is compared with farm-reported Irr and crops for the 2013–2017 growing seasons. Seasonal cumulative ET simulated values are shown for TWP 9S-41 W (Figs. 7a–e) and SD-6 (Fig. 7f–j). These ‘maps’ of cumulative ET

3.3. Within water right evaluation In this section, within WRMU cumulative water (ET, P, and Irr) at 8

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Fig. 6. Simulated cumulative plots of corn a) simulated daily evapotranspiration (ET), b) NLDAS precipitation (P), and c) simulated irrigation (Irr), cumulative plots of soybean d) through f), respectively of sampled pixel at SD-6 (100° 38′ 22″ W, 39° 21′ 38″ N) between May 10 and September 15 in SD-6, Kansas, USA. Shade represents 5-year maximum and minimum and the black line represents mean value.

Fig. 7. Simulated cumulative evapotranspiration (ET) at 30 m spatial resolution from automated BAITSSS 10 May and 15 September for a) to e) TWP 9S-41 W and f) to j) SD-6, Kansas, USA (black circles, water rights shapes). 9

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Fig. 8. Visual discrimination of full and split-pivot circles of simulated cumulative evapotranspiration (ET) from automated BAITSSS and vegetation indices from Landsat at 30 m spatial resolution between May 10 and September 15 for SD-6, Kansas, USA (outline circles at 6a, water right shapes).

For corn, greatest irrigation occurred in 2013 and 2014 and the least in 2017. Reported irrigation volume, applied to corn exceeded that reported for other crops with exceptions of equivalent amounts for soybean in 2015, 2016, and 2017, corn + soybean in 2014, corn + grain sorghum in 2015 and corn + wheat in 2014 and 2015. During the 2013–2017 growing seasons, reported irrigation volume applied to corn in the SD-6 LEMA (210–305 mm) was consistently less than simulated Irr values of 350–529 mm, (Table 3). This difference could indicate a positive bias in simulated irrigation requirements. Applied irrigation for well-watered corn at the Bushland, TX lysimeter site with drip was 491 mm; and the measured P was 225 mm for the total in-season water supply of 716 mm (Dhungel et al., 2019a). These values are consistent with the 671–775 mm of in-season water supply simulated during the 2013–2017 growing seasons for SD-6 LEMA conditions. An alternative explanation for the discrepancy between simulated and reported Irr for the SD-6 LEMA is that many farmers are practicing deficit irrigation. Deficit irrigation management is when the supply of water is less than the crop ET requirements (Fereres and Soriano, 2006). Under this management regime, available soil water is expected to be depleted to levels that may result in crop stress, possibly leading to yield reductions

SD-6 and TWP 9S- 41 W are presented. A detailed analysis was conducted of SD-6 because of larger sample size and the overall results were representative of TWP 9S- 41 W. 3.3.1. SD-6 LEMA Reported crop distributions indicated that 75% of the WRMU was planted to a single crop (corn, 58%; soybean, 9%; grain sorghum, 3%; wheat, 3%; sunflower, 2%) when averaged over the five-year period. The remaining 25% of WRMU area was planted to two crops (corn + soybean, 15%; corn + grain sorghum, 5%; corn + wheat, 4%). Chi-square analysis indicated that in 2017 the distribution of crops among WRMU in 2017 was different from the five-year average with fewer fields containing multiple crops; 82% of WRMU being planted to a single crop (corn, 66%; soybean 16%; grain sorghum 1%) and only 18% planted to two crops (corn + soybean, 12%; corn + wheat, 4%; corn + grain sorghum, 3%). In 2017, the final year of the five-year LEMA, farmers knew their remaining water allocations based on reported use in the prior four years. This may have affected the shift in crop allocations. Analysis of variance indicated that reported applied irrigation amounts differed among years and crops with some interacting effects. 10

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

crops that are grown in this region can or may improve the confidence in these simulation results. Fig. 9a shows the simulated ET was equivalent to or exceeded ‘inseason’ water supply (the reported Irr application plus P) for all reported corn in SD-6. If ET is representative of well-watered condition, then the difference between ET and reported Irr and P could indicate soil water depletion and/or supply-limited ET, as expected under deficit irrigation management. With the exception of 2015, simulated Irr was consistent in regard to reported Irr, although consistently greater in quantity (102 mm, average of 2013, 2014, 2016 and 2017; Fig. 9a, Table 3). This discrepancy could be accounted for by net soil water depletion in the order of 100 mm, a realistic quantity for these soils (Lamm et al., 2017). A negative bias in gridded precipitation could also contribute to the difference between simulated ET and reported Irr and P. A recent report from Whittemore et al. (2018) indicated that the true water conservation for four years (2013–2016) of LEMA operation, adjusted for climate, was in the range of 26–28%; a proportion that exceeded the targeted 20%. Another report from American Association for the Advancement of Science citing GMD4 official indicates a 39% reduction in groundwater use over the course of the LEMA (“Kansas Farmers Minimize Water Use as the Southern Great Plains Become More Arid, 2019). This supports the evidence that in-season water supply was less than simulated ET for the fully irrigated assumption, indicating possible deficit irrigation. Furthermore, various other factors such as model capability and limitations, propagation of bias from input gridded data (Dhungel et al., 2019b), uncertainty in applied irrigation acreage, splitpivots, and other unknown field conditions may have influenced the final ET. Fig. 9c and d show the trend of cumulative mean ET and seasonal mean LAI and NDVI, as expected, both show the linear relationship with the increase with ET with an increase in vegetation indices.

Table 3 Mean annual cumulative water (ET, P, Irr) for sampled crops. Year

2013 corn Difference Interval** Soybean grain sorghum wheat Corn + soybean Corn + grain sorghum Corn + wheat 2014 corn Difference Interval soybean grain sorghum wheat Corn + soybean Corn + grain sorghum Corn + wheat corn 2015 corn Difference Interval soybean grain sorghum wheat Corn + soybean Corn + grain sorghum Corn + wheat corn 2016 corn Difference Interval soybean grain sorghum wheat Corn + soybean Corn + grain sorghum Corn + wheat 2017 corn Difference Interval soybean grain sorghum wheat Corn + soybean Corn + grain sorghum Corn + wheat

Irr App mm

Irr Sim mm

P mm

Irr App + P mm

ET mm

n*

305 17 239 208 82 261 232 215

395 9.1 309 335 281 342 385 356

282 5.1 276 295 301 284 288 287

587 16.9 515 503 383 545 520 502

716 9.7 620 646 608 658 705 679

66

292 17.7 264 217 118 132 279 198 283

350 30.8 278 317 217 271 301 343 262

418 10.2 402 392 377 383 412 409 405

711 20.7 666 609 495 515 690 606 689

790 9.1 696 738 666 683 741 778 723

68

236 17.6 241 159 93 136 256 218 216

529 35.6 424 452 244 324 464 465 409

246 3.2 245 239 248 240 253 246 248

483 17.6 487 399 342 376 509 464 465

803 10.8 714 731 554 611 759 754 711

55

275 14.3 275 149 125 253 180 192

353 28.3 334 320 321 338 380 348

318 2 325 313 323 314 314 317

592 13.9 600 462 449 567 494 509

721 8.2 714 701 717 719 766 735

77

210 13.4 195 20 76 164 144 118

393 25.8 338 400 304 343 389 340

368 1.5 369 374 364 369 371 370

577 13.7 564 393 440 533 515 488

760 7.3 711 774 675 720 761 720

85

9 7 4 24 8 5

10 6 5 3 18 7 4

11 2 6 5 19 6 8

4 4 5 16 7 6

3.3.2. Township 9S-41W Fig. 9b shows a similar plot of ET vs. irrigation application plus P for TWP 9S-41 W. Results showed a similar trend with the majority of ET above 1:1 with some exceptions. The mean cumulative ET was between 500 mm–800 mm for evaluated years. 3.4. Water management in localized conditions

20 1 4 16 3 4

Earlier sections discussed of full irrigation treatment and corresponding ET simulation. This section briefly discusses the framework of full irrigation treatment and possible options of deficit irrigation in BAITSSS. Fig. 10 shows F4 function (Eq. 5) associated with θroot in the Jarvis equation for the sampled pixel discussed in Section 3.1.1 (100° 38′ 22″ W, 39° 21′ 38″ N) for year 2013. With relatively low moisture related stress, F4 was relatively large (> 0.85, Fig. 10) throughout the simulation without reducing transpiration when energy was available. As a reminder, Jarvis functions vary from 0 to 1 with infinite to minimum assigned resistance, respectively. In the instance of deficit irrigation, the soil moisture falls below threshold and eventually generating moisture related stress. The F4 becomes smaller which will increase rsc and ultimately reduce ET. Physically, this would delay irrigation timing and frequency, however, extent, timing, and duration of these types of stress may be difficult to measure and validate. Also, how these kinds of stress would affect the final ET and crop production is another key question and suitable for future research.

* number of parcels of WRMU. ** The Difference Interval is the product of standard error and the ‘Z’ statistic and used to calculate upper and lower bounds of a 95% confidence interval for the expected mean parameter for a corn crop.

(Blum, 1996; Gibson and Paulsen, 1999). Rudnick et al. (2019) summarized and discussed the deficit irrigation management of maize in the US High Plains aquifer region. It is possible that application of deficit irrigation management complicates the comparison of reported Irr App with simulated Irr Sim. The ET simulated from BAITSSS assumes well-watered conditions (i.e. 0.5 MAD). The reduced ET may be the results of the GMD4 policy of water restriction, where the first five-year period was recently completed (2013–2017). The seasonal ET for fully irrigated corn (sub-surface drip distribution system) at the Bushland, TX lysimeter site (2016) was 697 mm (Dhungel et al., 2019a). It is likely that this ET would increase with sprinkler irrigation due to greater evaporation from wet soil surfaces. For comparison, the simulated ET for irrigated corn in SH6-LEMA ranged from 716 to 803 mm, with values being consistent with the field measurements reported from Bushland, TX. Payero et al. (2008) reported similar seasonal ET (∼ 655 mm) for corn in west-central Nebraska based on 2005–2006 data. Further comparisons with a range of

3.5. Potential economic and policy implications of the BAITSSS results Majority of remote sensing based instantaneous ET models lack irrigation scheduling, soil moisture, and precipitation components in surface energy balance. Hourly scale soil water balance with irrigation scheduling make BAITSSS a comprehensive and robust algorithm for examining water management issues in the agricultural landscape. As such, we content that this simulation approach constitutes a next11

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Fig. 9. Scatterplot between the mean simulated ET in full irrigation treatment and reported irrigation (Irr) application plus precipitation of water rights between May 10 and September 15, a) corn at SD-6, b) corn at Township 9S-41 W, c) trend of ET to mean seasonal corn LAI and d) corn mean NDVI at SD-6.

soil moisture conditions. For either full or “split” center pivot irrigation systems, these “pictures” or “graphic images” of estimated crop water usage could provide guidance for efficiently irrigating particular parts of fields that are most “stressed’ in terms of soil moisture, as opposed to other parts of irrigated fields that may have adequate soil moisture supplies. Information from BAITSSS could be especially valuable in making decisions to end-of-season irrigation management, related to on crop water needs. Essentially, in any irrigated fields that must manage irrigation water supplies that are limited– either because of declining water availability or water management conservation regulations – this tool could provide irrigated crop producers with valuable irrigation water management decision making information if it were available periodically in a timely manner at critically important stages of crop development. The BAITSSS tool could provide guidance for farmers with adjacent irrigation systems who may have the ability to divide water supplies among these fields based on differing soil moisture needs. Also, a multiple-year record of past water use efficiency and effectiveness on a particular field may help irrigators make economically profitable decisions about “full” versus “partial” center pivot circle coverage of particular crops.

Fig. 10. Plot of relationship between daily mean weighting functions representing plant response (F4) to simulated soil moisture at root zone (θroot) of sampled pixel at SD-6 (100° 38′ 22″ W, 39° 21′ 38″ N) between May 10 and September 15, 2013 in SD-6, Kansas, USA.

generation set of tools with application to irrigation management. It has global application, subject to supporting databases. If the information from the BAITSSS model were available for use on a broad scale, it could have important economic and policy implications for irrigated crop producers, regional water resource managers, and natural resource policy makers.

3.5.2. Regional water resource managers and water management policy makers Regional water resource managers and water management policy makers could also benefit from the information provided by BAITSSS as the program’s output provides information on water use efficiency and irrigation management practices. On the one hand, BAITSSS information could be used constructively to help design and encourage efficient irrigation water management practices and procedures that are economically effective in promoting profitable crop production practices in both the short and long term. However, a concern to farmers maybe the possible use of BAITSSS as a regulatory tool by these same public entities to verify compliance to existing water policies. Farmers in NW Kansas should not have such concerns because they are already

3.5.1. Irrigated crop producers For irrigated crop producers in areas where availability to irrigation water is regulated, the potential to provide field level crop production information from BAITSSS could provide valuable information on ingrowing season crop water use and possibly water use efficiency. If this information were to be made available for individual irrigated fields periodically throughout the growing season (i.e., weekly, bi-weekly, or monthly), it could help farmers make more effective irrigation management decisions – particularly under “tight”, “scarce” or limited crop 12

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

Availability and Watershed Management, and in part by the Ogallala Aquifer Program, a consortium of the USDA Agricultural Research Service, Kansas State University, Texas AgriLife Research, Texas AgriLife Extension Service, Texas Tech University, and West Texas A&M University. Numerous technicians and student workers contributed to the lysimeter and evapotranspiration programs.

required to report their groundwater use. The use of BAITSSS as an alternative to reporting groundwater use may actual save farmers time from recording and reporting groundwater use. To avoid potential disagreements over the use of BAITSSS data among individual irrigated crop producers, water resource managers, and policy makers, these groups will need to work together to develop policies for its permissible use. It is possible that in-season basis BAITSSS results could be provided on a fee basis to individual irrigated crop producers by either private enterprise or public entities. End-ofyear summaries of crop water use information could also be made available to crop producers and regulatory authorities for management, monitoring, and policy purposes. Finally, at issue regarding field-level crop water usage information with BAITSSS is how accurately it describes the effectiveness of irrigated crop water management practices. If acceptably accurate, BAITSSS crop water use information can provide both farmers and regional water managers with valuable irrigation water management information. In summary, BAITSSS may improve farmers’ ability to efficiently and profitably manage their irrigated acres. And, BAITSSS may also help water managers have a better understanding of effective irrigation water management plans, policies, and procedures. The ultimate goal in development of BAITSSS is to have a user interface that gives the user the ability to select the 30-meter pixels that represents their land displaying an accurate estimate of ET (and any other outputs from BAITSSS model) from the most recent hour and day as well as cumulative ET (near real-time) so that all the stakeholders can plan accordingly. As a caution regarding the future availability of BAITSSS, individual crop producers may question whether regulatory authorities would be motivated to eventually use information from BAITSSS for a closer, more informed regulatory oversight and control of their field level irrigation management practices.

References Aladjem, D., Sunding, D., 2015. Marketing the sustainable groundwater management act: applying economics to solve California’s groundwater problems. Nat Resour. Environ. 30, 28. Allen, R.G., Morse, A., Tasumi, M., 2003. Application of SEBAL for western US water rights regulation and planning. Proc. ICID Int. Workshop on Remote Sensing. Allen, R.G., Tasumi, M., Morse, A., Trezza, R., 2005. A landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning. Irrig. Drain. Syst. Eng. 19, 251–268. Allen, R.G., Tasumi, M., Trezza, R., 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—model. J. Irrig. Drain. Eng. 133, 380–394. Allen, R.G., Trezza, R., Tasumi, M., Kjaersgaard, J., 2012. Mapping Evapotranspiration at High Resolution Using Internalized Calibration: Application Manual for Landsat Satellite Imagery. University of Idaho. Anderson, M.C., Allen, R.G., Morse, A., Kustas, W.P., 2012. Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources. Remote Sens. Environ. 122, 50–65. Araya, A., Gowda, P.H., Golden, B., Foster, A.J., Aguilar, J., Currie, R., Ciampitti, I.A., Prasad, P.V.V., 2019. Economic value and water productivity of major irrigated crops in the Ogallala aquifer region. Agric. Water Manag. 214, 55–63. Basso, B., Kendall, A.D., Hyndman, D.W., 2013. The future of agriculture over the Ogallala Aquifer: solutions to grow crops more efficiently with limited water. Earths Future 1, 39–41. Bastiaanssen, W.G., Menenti, M., Feddes, R.A., Holtslag, A.A.M., 1998. A remote sensing surface energy balance algorithm for land (SEBAL). 1. Formulation. J. Hydrol. 212, 198–212. Blum, A., 1996. Crop responses to drought and the interpretation of adaptation. Drought Tolerance in Higher Plants: Genetical, Physiological and Molecular Biological Analysis. Springer, pp. 57–70. Butler Jr, J.J., Whittemore, D.O., Wilson, B.B., Bohling, G.C., 2018. Sustainability of aquifers supporting irrigated agriculture: a case study of the High Plains aquifer in Kansas. Water Int. 43, 815–828. Calera, A., Campos, I., Osann, A., D’Urso, G., Menenti, M., 2017. Remote sensing for crop water management: from ET modelling to services for the end users. Sensors 17, 1104. Cantor, A., Owen, D., Harter, T., Nylen, N.G., Kiparsky, M., 2018. Navigating Groundwater-Surface Water Interactions Under the Sustainable Groundwater Management Act. Chen, J., Zhu, X., Vogelmann, J.E., Gao, F., Jin, S., 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sens. Environ. 115, 1053–1064. Choudhury, B.J., Monteith, J.L., 1988. A four-layer model for the heat budget of homogeneous land surfaces. Q. J. R. Meteorol. Soc. 114, 373–398. Clark, G.A., Rogers, D., Briggeman, S., 2000. KanSched: an ET-based irrigation scheduling tool for Kansas summer annual crops. Kans. State Univ. Res. Ext. Deines, J.M., Kendall, A.D., Butler, J.J., Hyndman, D.W., 2019. Quantifying irrigation adaptation strategies in response to stakeholder-driven groundwater management in the US High Plains Aquifer. Environ. Res. Lett. Dennehy, K.F., 2000. High Plains Regional Ground-water Study. US Geological Survey. Dhungel, R., Aiken, R., Colaizzi, P., Lin, X., O’Brien, D., Baumhardt, L., Brauer, D., Marek, G., 2019a. Evaluation of uncalibrated energy balance model (BAITSSS) for estimating evapotranspiration in a semiarid, advective climate. Hydrol. Process. Dhungel, R., Aiken, R., Colaizzi, P.D., Lin, X., Baumhardt, R.L., Evett, S.R., Brauer, D.K., Marek, G.W., O’Brien, D., 2019b. Increased Bias in evapotranspiration modeling due to weather and vegetation indices data sources. Agron. J. Dhungel, R., Allen, R.G., Trezza, R., Robison, C.W., 2016. Evapotranspiration between satellite overpasses: methodology and case study in agricultural dominant semi-arid areas. Meteorol. Appl. 23, 714–730. Du, J.P., Sun, R., 2012. Estimation of evapotranspiration for ungauged areas using MODIS measurements and GLDAS data. Procedia Environ. Sci. 13, 1718–1727. Eheart, J.W., Tornil, D.W., 1999. Low-flow frequency exacerbation by irrigation withdrawals in the agricultural midwest under various climate change scenarios. Water Resour. Res. 35, 2237–2246. Er-Raki, S., Chehbouni, A., Duchemin, B., 2010. Combining satellite remote sensing data with the FAO-56 dual approach for water use mapping in irrigated wheat fields of a semi-arid region. Remote Sens. 2, 375–387. Famiglietti, J.S., 2014. The global groundwater crisis. Nat. Clim. Change 4, 945. Fereres, E., Soriano, M.A., 2006. Deficit irrigation for reducing agricultural water use. J. Exp. Bot. 58, 147–159. Gao, F., Anderson, M.C., Zhang, X., Yang, Z., Alfieri, J.G., Kustas, W.P., Mueller, R., Johnson, D.M., Prueger, J.H., 2017. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sens. Environ. 188, 9–25. Gibson, L.R., Paulsen, G.M., 1999. Yield components of wheat grown under high temperature stress during reproductive growth. Crop Sci. 39, 1841–1846.

4. Conclusion Our effort to understand the implications of the simulated landscape ET in an agriculturally-dominated groundwater region of Kansas produced opportunities as well as challenges. A large database of water diversion and metered consumption data provided an excellent opportunity evaluate model simulations to real life groundwater uses. The hourly landscape ET simulation in a time series at 30 m spatial resolution from BAITSSS was computationally challenging and data-intensive. Results indicated that the upper limit of seasonal ET at WRMUs for multiple years approximated the applied irrigation plus precipitation; thus, indicating the overall competence of BAITSSS. The lack of systematic documentation of ground truth data for various localized conditions affected the final interpretation of the results. It is not feasible to have ground truth data in each field, so our critical understanding and interpretation of these results are inevitable. The overestimation of simulated irrigation can be reduced by ceasing irrigation simulation during the senescence period. Overall, results indicated that the restriction policy may have played positive impact to minimize crop water use. The policies for implementing water reduction in an agricultural landscape is generally hindered by the lack of efficient and reliable tools in landscape scale. But we show that remote-sensingbased landscape ET models are generally used to successfully provide these tools. We believe a more accurate and detailed model like BAITSSS would open opportunities and dialogue among the farmers, producers, regional water resource managers, and water management policy-makers to solve one of the critical water issues of the western United States and around the world. Acknowledgments Lysimeter and evapotranspiration research at USDA, Bushland, Texas were supported by USDA-ARS National Program 211, Water 13

Agricultural Water Management 227 (2020) 105854

R. Dhungel, et al.

power plant. Irrig. Sci. 13, 73–80. Melton, F.S., Johnson, L.F., Lund, C.P., Pierce, L.L., Michaelis, A.R., Hiatt, S.H., Guzman, A., Adhikari, D.D., Purdy, A.J., Rosevelt, C., 2012. Satellite irrigation management support with the terrestrial observation and prediction system: a framework for integration of satellite and surface observations to support improvements in agricultural water resource management. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5, 1709–1721. Niles, M., Wagner, C., 2017. Farmers share their perspectives on California water management and the Sustainable Groundwater Management Act. Calif. Agric. 72, 38–43. Order of decision returning the local enhanced management plan with proposed modifications, 2017. Order of Decision Returning the Local Enhanced Management Plan With Proposed Modifications (No. 002-DWR-LEMA-2017). Order of designation approving the Sheridan 6 local enhanced management area within groundwater management district no. 4, 2013. Order of Designation Approving the Sheridan 6 Local Enhanced Management Area Within Groundwater Management District no. 4 (No. 12 WATER 8366). Payero, J.O., Tarkalson, D.D., Irmak, S., Davison, D., Petersen, J.L., 2008. Effect of irrigation amounts applied with subsurface drip irrigation on corn evapotranspiration, yield, water use efficiency, and dry matter production in a semiarid climate. Agric. Water Manag. 95, 895–908. Peck, J.C., 2015. Legal challenges in government imposition of water conservation: the Kansas example. Agron. J. 107, 1561–1566. Pereira, L.S., Teodoro, P.R., Rodrigues, P.N., Teixeira, J.L., 2003. Irrigation scheduling simulation: the model ISAREG. Tools for Drought Mitigation in Mediterranean Regions. Springer, pp. 161–180. Rudnick, D.R., Irmak, S., West, C., Chávez, J.L., Kisekka, I., Marek, T.H., Schneekloth, J.P., Mitchell McCallister, D., Sharma, V., Djaman, K., 2019. Deficit irrigation management of maize in the High Plains Aquifer Region: a review. JAWRA J. Am. Water Resour. Assoc. 55, 38–55. Schaible, G., Aillery, M., 2012. Water Conservation in Irrigated Agriculture: Trends and Challenges in the Face of Emerging Demands. USDA-ERS Econ. Inf. Bull. Senay, G.B., Friedrichs, M., Singh, R.K., Velpuri, N.M., 2016. Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin. Remote Sens. Environ. 185, 171–185. Sun, S.F., 1982. Moisture and Heat Transport in a Soil Layer Forced by Atmospheric Conditions (Thesis). [Storrs] : [Univ. Of Connecticut]. Tang, R., Li, Z.-L., Tang, B., 2010. An application of the Ts–VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: implementation and validation. Remote Sens. Environ. 114, 540–551. Whittemore, D.O., Butler Jr., J.J., Wilson, B.B., 2018. Status of the High Plains Aquifer in Kansas. Kansas Geological Survey. Whittemore, D.O., Butler Jr, J.J., Wilson, B.B., 2016. Assessing the major drivers of waterlevel declines: new insights into the future of heavily stressed aquifers. Hydrol. Sci. J. Des Sci. Hydrol. 61, 134–145.

http://hercules.kgs.ku.edu/geohydro/wimas/index.cfm, n.d. https://docs.conda.io/en/latest/, n.d. https://hydro1.gesdisc.eosdis.nasa.gov/data/NLDAS/NLDAS_FORA0125_H.002/1979/ 006/, n.d. https://www.nrcs.usda.gov/wps/portal/nrcs/main/soils/survey/geo/, n.d. http://www.gdal.org [WWW Document], n.d. URL https://www.facebook.com/ charushree?fref=nf&__tn__=%2Cdm-R-R&eid= ARAxc3MyHUWRKtqlmDVP4RMx6jJV9ktM9zpLeUBqNGFLlbyowEjWB PRieelWzlfgqWLpxeiYyTyuNwkJ (Accessed 5.21.19). Irmak, A., Allen, R.G., Kjaersgaard, J., Huntington, J., Kamble, B., Trezza, R., Ratcliffe, I., 2012. Operational remote sensing of ET and challenges, in: evapotranspiration-remote sensing and modeling. IntechOpen. Jarvis, P.G., 1976. The interpretation of the variations in leaf water potential and stomatal conductance found in canopies in the field. Philos. Trans. R. Soc. Lond., B, Biol. Sci. 273, 593–610. Kansas Farmers Minimize Water Use as the Southern Great Plains Become More Arid, 2019. Kansas Farmers Minimize Water Use as the Southern Great Plains Become More Arid. n.d.. Prof. Ethics Rep. Kiparsky, M., 2016. Unanswered questions for implementation of the sustainable groundwater management act. Calif. Agric. 70, 165–168. Kiparsky, M., Owen, D., Green Nylen, N., Doremus, H., Christian-Smith, J., Cosens, B., Fisher, A., Milman, A., 2016. Designing Effective Groundwater Sustainability Agencies: Criteria for Evaluation of Local Governance Options. Kirchhoff, C.J., Dilling, L., 2016. The role of US states in facilitating effective water governance under stress and change. Water Resour. Res. 52, 2951–2964. Kisekka, I., Aguilar, J., 2016. Deficit irrigation as a strategy to cope with declining groundwater supplies: experiences from Kansas. Emerging Issues in Groundwater Resources. Springer, pp. 51–66. Klocke, N.L., Currie, R.S., Tomsicek, D.J., Koehn, J., 2011. Corn yield response to deficit irrigation. Trans. ASABE 54, 931–940. Klocke, N.L., Stone, L.R., Clark, G.A., Dumler, T.J., Briggeman, S., 2006. Water allocation model for limited irrigation. Appl. Eng. Agric. 22, 381–389. Kramber, W.J., Allen, R.G., Trezza, R., 2011. Filling in the Gaps in Landsat 7 Data. Kumar, A., Chen, F., Niyogi, D., Alfieri, J.G., Ek, M., Mitchell, K., 2011. Evaluation of a photosynthesis-based canopy resistance formulation in the Noah land-surface model. Bound.-Layer Meteorol. 138, 263–284. Lamm, F.R., Rogers, D.H., Schlegel, A.J., Lin, X., Aiken, R.M., Klocke, N.L., Stone, L.R., Shaw, L.K., 2017. Trends in plant available soil water on producer fields of western Kansas. Appl. Eng. Agric. 33, 859–868. Leahy, T.C., 2015. Desperate times call for sensible measures: the making of the California Sustainable Groundwater Management Act. Gold. Gate U Envtl LJ 9, 5. Lilienfeld, A., Asmild, M., 2007. Estimation of excess water use in irrigated agriculture: a Data Envelopment Analysis approach. Agric. Water Manag. 94, 73–82. https://doi. org/10.1016/j.agwat.2007.08.005. Malek, E., Bingham, G.E., McCurdy, G.D., Hanks, R.J., 1992. Determination of evapotranspiration from an alfalfa crop irrigated with saline waste water from an electrical

14