Field-scale crop water consumption estimates reveal potential water savings in California agriculture

Domain: earth_science
Published: 2024-03-25
Editorial outcome: minor_revision

Abstract
Efficiently managing agricultural irrigation is vital for food security today and into the future under climate change. Yet, evaluating agriculture’s hydrological impacts and strategies to reduce them remains challenging due to a lack of field-scale data on crop water consumption. Here, we develop a method to fill this gap using remote sensing and machine learning, and leverage it to assess water saving strategies in California’s Central Valley. We find that switching to lower water intensity crops can reduce consumption by up to 93%, but this requires adopting uncommon crop types. Northern counties have substantially lower irrigation efficiencies than southern counties, suggesting another potential source of water savings. Other practices that do not alter land cover can save up to 11% of water consumption. These results reveal diverse approaches for achieving sustainable water use, emphasizing the potential of sub-field scale crop water consumption maps to guide water management in California and beyond.

Introduction
Climate change, drought, and the overexploitation of water resources have led to declines in freshwater storage in many vital agricultural regions1, raising concerns surrounding the future of food and water security2,3. Irrigation constitutes the largest use of freshwater globally4,5, and many regions will need to decrease their agricultural water use while maintaining high levels of production to ensure a sustainable food and water supply as the population grows6. Proposed options for reducing water use include leaving lands fallow, switching to less water-intensive crops, adopting water-saving farming practices such as deficit irrigation, and improving irrigation efficiency7,8. However, any approach to reducing agricultural water use depends on the challenging task of characterizing the amount of water crops consume9.

While it is possible to monitor volumes of water withdrawn for irrigation, this is a poor proxy for the amount of water crops consume through evapotranspiration (ET)10. Only some irrigation water results in ET: the rest remains in the system as runoff or recharge, though this proportion can vary widely depending on topography, climate, soil type, and farming practices11. Agricultural ET, or the increase in ET that irrigated agriculture brings, is therefore a critical measure in that it represents the amount of water that is actually “consumed” by agriculture. This water leaves the watershed entirely as it is evaporated from the soil and transpired by crops4. Since agricultural ET represents agricultural water consumption, it is key for gauging the potential of fallowing, crop switching, or other farming practices to save water12. When compared to total irrigation amounts, agricultural ET can also be used to highlight the fraction of irrigation withdrawals that do not effectively result in consumptive use13. While this water stays in the system and therefore may continue to provide beneficial uses14, it also does not achieve its original purpose of contributing to crop growth. Therefore, agricultural ET can help calculate irrigation efficiency and identify unnecessary irrigation water withdrawals from surface or groundwater reservoirs13.

Two methodological challenges prevent agricultural ET from being monitored at scale. First, simply measuring total ET at scale is a challenge. Second, even when measures of ET are available, it is difficult to separate agricultural ET from the total10,15. To measure total ET, eddy covariance flux towers are highly accurate for monitoring ET at a single location16. However, they are expensive and thus sparse, and they are designed to measure ET over uniform vegetation, which is not reflective of complex agricultural landscapes17. In the absence of ET measurements, theoretical water demand can be simulated based on climate and crop type18,19. Although such tools are helpful for water demand planning20, theoretical water demand represents the ET of a crop whose water demands are fully met. Therefore, these are likely to overestimate actual ET. Additionally, these simulated estimates exclusively reflect variations in water demand influenced by factors incorporated into the model, which are often limited to crop type and climate21. Therefore, analyses based on these models are constrained to examining these specific factors, neglecting other critical drivers of ET such as farming practices. Given the scarcity of in situ data and the inherent constraints of simulated estimates, accurately gauging total ET at scale remains a significant challenge.

Addressing the second challenge, even when accurate ET measurements are available, isolating agricultural ET from total ET is difficult. This step is key for water management, since the resulting metric represents the increase in ET attributable to agriculture. To do so, one must estimate the naturally-occurring ET that would occur in the absence of irrigated agriculture (for example, if the land were left fallow). Most often, this naturally-occurring ET is assumed to be equal to precipitation19,22. However, there may be temporal lags between precipitation and resulting ET, some precipitation may not result in ET at all, and ET may also be supplemented by other sources of water, such as near surface groundwater. Together, these challenges leave us with limited measurements of ET and no empirical estimates of agricultural ET at large scales, inhibiting our ability to form evidence-based water management policies that accurately reflect crop water use and potential water savings in agriculture.

Recent advances in the remote sensing of ET unlock new avenues for research into agricultural water consumption. Numerous algorithms for estimating ET using land surface temperature and other remote sensing inputs have been validated specifically for use in agricultural settings23,24. These advancements have allowed researchers to empirically study total ET in agricultural settings25,26. However, despite high-resolution maps of ET in agricultural landscapes25,27, the challenge of isolating agricultural ET from total ET has been limited to studies in extremely arid regions where irrigation is the only source of water available to plants28.

Here, we develop a framework for measuring agricultural ET at sub-field scales. We use remote sensing to determine total ET and combine it with machine learning to estimate naturally-occurring ET. First, we retrieve satellite-based remotely sensed total ET estimates from the 30m, monthly OpenET ensemble data24 available in the western United States starting in 2016. Second, since we can use these same OpenET estimates to directly observe naturally-occurring ET over fallow lands, we train a gradient boosting algorithm to predict ET over fallow lands. We use information on topography, soil quality, climate, and spatial and temporal coordinates as predictors. We then use the model to retrieve naturally-occurring ET over all active agricultural fields, which we subtract from the remotely-sensed ET to calculate agricultural ET.

We apply this methodology to calculate agricultural ET across California’s Central Valley, one of the world’s most water-stressed and agriculturally productive regions29. We use these maps of agricultural ET to calculate the water consumption of different crop types, as well as to calculate variability in agricultural ET within crop types. These insights allow us to evaluate different strategies that have been proposed to save water under the Sustainable Groundwater Management Act (SGMA)7, which mandates that all water basins in California reduce groundwater pumping to sustainable levels by 204030. Specifically, we compare the ability of fallowing, crop switching, and other farming practices to save water by reducing agricultural ET. Additionally, we calculate irrigation efficiency to assess the potential to reduce irrigation withdrawals without decreasing agricultural ET. In the wake of groundwater pumping cutbacks of 20–50% under SGMA7, this work will guide water managers in enacting water savings and help predict which land use changes are likely to ensue.

Results
Estimating agricultural ET
We calculate agricultural ET by retrieving the total ET observed over agricultural areas and subtracting naturally-occurring ET (Fig. 1). While total ET estimates are retrieved directly from OpenET, we simulate naturally-occurring ET by training a gradient boosting regressor to predict the ET observed by OpenET over fallow lands. Our model simulating naturally-occurring ET achieves an R2 of .87 and a Mean Absolute Error (MAE) of 35.5 mm per year (Supplementary Fig. 1). We use a test set made up of 2 km2 held-out areas (about four times the area of a large agricultural field in the Central Valley). We do not find that error is structured across either space (Supplementary Figs. 2 and 3) or time (Supplementary Fig. 4).

Variation in agricultural ET within and across crop types
We leverage the significant variability in agricultural ET observed across the Central Valley (Fig. 1) to analyze the factors driving these variations. Crop type, which is commonly cited as an important variable explaining differences in agricultural ET19, explains 34% of the variation in estimated agricultural ET (Eq. (4), Supplementary Fig. 5). In Fig. 2, we show the water intensity of different crop groups (Supplementary Note 7). Deciduous fruits and nuts are some of the highest consumers at 625 mm per year (582–668 95% CI) (Fig. 2), particularly almonds at 715 mm per year (651–778 95% CI) (Supplementary Fig. 5). Conversely, grain and hay crops consume only 141 mm per year (111–171 95% CI). These broad variations in the water consumption of different crop types align with previous work estimating crop water demands (Supplementary Note 1, Supplementary Fig. 6) and suggest that substantial water savings may be possible with crop switching. However, the majority of the variation remains unexplained by crop type (Fig. 2, Supplementary Fig. 5). This within-crop variability is not uniform for different crops: crops such as deciduous fruits and nuts tend to have large variances, whereas rice has a very small variance. For example, pistachios have the most variability with an interquartile range (IQR) of 664 mm per year over the whole state. Conversely, rice has an IQR of only 122 mm per year (Supplementary Fig. 5).

Part of the large variability in agricultural ET for deciduous fruits and nuts can be explained by orchard age: young orchards consume significantly less water than more mature ones (Supplementary Note 2, Supplementary Fig. 7). Climate, topography, and soil quality explain an additional 6% of within-crop variation. However, substantial within-crop variation remains, indicating that some of these differences may be due to variations in farming practices. This would suggest that reducing water consumption without switching crops may be feasible, which we explore in the next section.

The water-saving potential of different management strategies
The variability of agricultural ET both within and between crops allows us to evaluate the water saving-potential of different management strategies. Here, we compare the effect of three scenarios on reducing agricultural ET in groundwater sub-basins across the Central Valley:
1. Crop switching: Substitute high-ET crops for the median water-consuming crop (Eq. (6)).
2. Farming practices: Keep the same spatial allocation of crops, but reduce agricultural ET of high consumers to the median, crop-specific, consumption level (Eq. (7)).
3. Fallowing: Fallow the 5% of lands with the highest estimated agricultural ET (Eq. (8)).

Because it would not be realistic to prescribe farmers in different contexts to consume similar amounts of water, we calculate crop-specific agricultural ET and within-crop variation at the level of groundwater sub-basins. We additionally control for differences in climate, topography, and soil type before conducting the scenarios. To account for the effects of orchard age, we remove orchards that have been bearing fruit for 5 years or less or that are in their last year of production (Supplementary Fig. 7).

We find that each of these scenarios return similar reductions in overall water consumption of around 10% (Fig. 3). Since crops with high agricultural ET are heavily favored in most sub-basins and therefore the median crop usually has a high agricultural ET, crop switching only results in a 9.9% reduction in agricultural ET. In order to achieve greater water savings, crops would need to be switched to less popular crops with lesser water demands: switching all crops to the lowest consuming crop in a sub-basin results in a 93.8% reduction in agricultural ET. While strategies that do not require a change in land cover type are less commonly cited as a water management approach, we find that reducing high consumers to their crops’s median consumption level yields similar water savings of up to 11.3%. Fallowing does, however, remain the most effective way to reduce agricultural ET. We find that leaving a mere 5% of land fallow results in a 9.3% consumption reduction, comparable to the savings afforded by crop switching and within-crop scenarios which by definition affect up to 50% of lands.

Irrigation efficiency
While fallowing, crop switching, and other farming practices highlight opportunities to decrease agricultural ET, we also find opportunities to diminish runoff and deep percolation during conveyance, on-farm management, or application (Fig. 4). On average, we find that irrigation in the Central Valley is 61.8% (54.0–69.7%) efficient, similar in magnitude to what theoretical estimates predict (Supplementary Note 3, Supplementary Fig. 8). The large disparity in efficiency between northern and southern counties is, however, more pronounced than theoretical estimates (Supplementary Fig. 8). As a result, there may be limited potential to decrease water withdrawn for irrigation in the south which achieves efficiencies as high as 80%. The particularly low efficiencies in the northern counties, however, suggest a large potential to decrease irrigation water withdrawals without affecting agricultural ET.

Methods
Agricultural ET
We define agricultural ET as the difference between total ET over an agricultural parcel and the ET that would have been, had that parcel been fallow land instead (Eq. (1)). This definition recognizes that not all ET over agricultural lands, during the growing season or otherwise, can necessarily be attributed to agriculture. Such a definition is particularly useful from a management perspective, since it denotes the decrease in ET, or water savings, that one might expect if agriculture were to cease.

ET_ag = ET_tot - ET_nat (1)

where ET_ag is agricultural ET, ET_tot is the total ET over an agricultural parcel, and ET_nat is the counterfactual ET that would occur naturally, were the same land fallow.

We note that another, theoretically distinct counterfactual could be constructed to represent ET_nat: ET if the land were undisturbed, natural land rather than fallow. We elect to simulate ET over fallow lands since it allows us to predict the potential water savings from fallowing, which has a management relevant interpretation.

Total ET estimates. We construct ET_tot from OpenET ensemble data, available at a monthly timestep from 2016-2021. This data is calculated as the average output of six different ET models estimated using Landsat data24. The data have been corrected for biases in ET resulting from data only being available during cloudless overpasses and have been extensively evaluated over a broad variety of land covers48. While they are published at a 30m scale, we resample to 70m to improve computational efficiency since this is well below the average size of an agricultural field in the Central Valley.

Calculation of a naturally-occurring ET counterfactual using machine learning. We estimate naturally-occurring ET for all agricultural pixels using a gradient boosting regressor trained on data from fallow lands. We choose a gradient boosting regressor due to its high flexibility in learning non-linear relationships and proven performance on tabular datasets relative to other regressors50. We retrieve the locations of fallow fields in the Central Valley from the Department of Water Resources (DWR) statewide crop mapping dataset over available years: 2016, 2018, and 2019. Pixels that are within the top 5% ET during July-September are removed due to some implausibly high numbers during these months that suggest mislabeling of an active agricultural pixel as fallow. While we believe that this data cleaning step is important to ensure an unbiased training set, omitting this step does not significantly change our main findings (Supplementary Note 5, Supplementary Figs. 10–12). We additionally find that our findings do not change when training our model on data that are marked fallow by both the DWR dataset and the Cropland Data Layer (CDL) (Supplementary Note 6, Supplementary Figs. 13–15).

We predict naturally-occurring ET based on latitude and longitude, the month and year, as well as a broad set of additional variables describing topography (elevation, aspect, slope, topographic wetness index), soil quality (California Storie Index), and climate (Potential ET). The latitude, longitude, and month and year indicators are included to capture the spatial and temporal patterns in ET underlying the densely distributed fallow fields in our dataset. However, the inclusion of an indicator variable for each year in our model limits our analysis to 2016, 2018, and 2019, as these are the only years for which we have available land cover data. Nevertheless, given that ‘year’ contributes a mere 3% to our model’s predictive capability (see Supplementary Fig. 17), it may be considered non-essential for future research in this area.

Including additional predictors in our model presents two benefits. First, they can improve the model’s predictive power. For example, they can help correct for systematic differences between the fallow lands used to train our model and the active agricultural lands we apply the model to. Such differences could arise from farmers selecting lands to be fallowed due to their inherently lower productivity. To account for this, we calculate an “adjusted” agricultural ET for each pixel by removing the difference between the mean agricultural ET for that year and the overall sample mean agricultural ET from the original agricultural ET calculation, as follows:

Adjusted ET_p,y = ET_p,y - (ET_y - ET) (2)

where Adjusted ET_p,y is the agricultural ET for pixel p in year y with the year-specific variation removed, ET_p,y is the original agricultural ET, ET_y is the mean agricultural ET in year y across all pixels and ET is the mean agricultural ET across all pixels and years.

Regressions. To calculate point estimates and confidence intervals for the overall mean agricultural ET (Eq. (3)), the mean agricultural ET by crop group or crop type (Supplementary Note 7), or the mean agricultural ET by county (used to calculate irrigation efficiency) (Eq. (4)), we conduct a series of regressions with the following format:

Adjusted ET_p,y = alpha + epsilon_p,y (3)

Adjusted ET_p,y = beta_p,y * Group_p,y + epsilon_p,y (4)

where alpha is the intercept, Group_p,y is a vector of dummy variables indicating which crop (for crop comparisons shown in Figs. 2 and S5) or county (for irrigation efficiency calculations shown in Fig. 4; county does not vary by year) observation p,y falls into, beta_p,y is a vector of coefficients for each crop or county, and epsilon_p,y is an error term. Standard errors are calculated using 75 km clusters, as determined by plotting a variogram of the spatial autocorrelation in agricultural ET (Supplementary Fig. 16).

The aggregate crop group (Fig. 2) and detailed crop type (Supplementary Fig. 5) are each assigned using the same DWR land cover data used to determine fallow status.

In addition to using regression to calculate point estimates and confidence intervals, regression allows us to calculate the proportion of variation that is explained by a set of variables. This is because the R2 corresponds to the fraction of variation explained by the regression. We use this to assess the % variation explained by crop type, using Eq. (4), as well as that explained by climate, topography, and soil quality:

Adjusted ET_p,y = alpha + beta_p,y * X_p,y + epsilon_p,y (5)

where alpha is the intercept, X_p,y is a vector of variables including PET, elevation, aspect, slope, TWI, and soil quality, beta_p,y is a vector of coefficients for each variable in X_p,y, and epsilon_p,y is an error term.

Management scenarios. We conduct three scenarios in which we manipulate agricultural ET to mimic land management changes and report the decrease in overall agricultural ET within groundwater sub-basins across the Central Valley. Eq. (6) is a crop switching scenario (cs) where pixels with agricultural ET values above those of the median crop in a given sub-basin are replaced with that median crop. Eq. (7) is a farming practice scenario (fp) where pixels with agricultural ET values above crop and sub-basin-specific mean values are replaced with that crop and sub-basin mean. Finally, Eq. (8) is a fallowing scenario (fal) which replaces all pixels with agricultural ET values above the sub-basin-specific 95th percentile with a value of zero agricultural ET.

Savings_cs = 1 - (sum(min(ET_i, Median(ET_c,b))) / sum(ET_i)) (6)

Savings_fp = 1 - (sum(min(ET_i, ET_c,b)) / sum(ET_i)) (7)

Savings_fal = 1 - (sum(ET_i, if ET_i <= ET_.95,b; 0, otherwise) / sum(ET_i)) (8)

where ET_i is the adjusted agricultural ET for observation i, ET_c,b is the average agricultural ET for crop type c in groundwater sub-basin b, Median(ET_c,b) is the median ET_c,b in sub-basin b, and ET_.95,b is the 95th percentile of agricultural ET in sub-basin b.

To ensure that the water savings identified in our scenarios result from factors that can be influenced by management interventions, we account for orchard age, climate, and other physical characteristics of the land. To account for orchard age, we remove young orchards that are have been bearing fruit for 5 or less years, or old orchards that are going to be removed within the next year (Supplementary Note 2). Because we are only able to label orchard age in this way for all orchards in year 2019 (the DWR crop type data we use are only available starting 2014), we exclusively use 2019 for this part of the analysis. Since we are only using one year, we do not adjust the water use according to the year like we do for the other analyses. To account for differences in climate and other characteristics inherent to the land, we control for potential ET, soil quality, topographic wetness index, elevation, aspect and slope using linear regression (Eq. (5)).

Irrigation efficiency. We define irrigation efficiency as the proportion of irrigation water that results in agricultural ET, and is thus consumed by agriculture (Eq. (9)). To determine amounts of water withdrawn for irrigation, we retrieve county level irrigation water use data from the USGS National Water Information System. We note that these irrigation amounts are counted at the point of use, rather than the water’s point of origin. These data are gathered every five years with the most recently available data from 2015. Because there is no match for the years of available irrigation data and our agricultural ET estimates from 2016, 2018, and 2019, we use the two most recent years, 2010 and 2015, which represent a non-drought year and a drought year, respectively.

IRRIGATION EFFICIENCY = ET_ag / Irrigation (9)

where ET_ag is agricultural ET (Eq. (1)) and Irrigation is the water withdrawn for irrigation. ET_ag and Irrigation must be in matching units, either volumetric or depth. We calculate both in mm per year.

We assume all active agricultural lands in the Central Valley are irrigated and calculate the average agricultural ET in mm per year over active agricultural lands in each county. To also retrieve average irrigation amount across irrigated lands in a county in mm per year, we divide the volume of irrigation water by the average area of irrigated land in each county. Because some counties are not fully encompassed within the Central Valley, we assume that irrigation in a given county is evenly distributed over irrigated lands within and outside of the Central Valley.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study reveals how much of the evapotranspiration (ET) in agricultural fields is due to the specific\ncrop being grown there, as opposed to the total ET which includes natural ET that would occur without\ncrop growth (fallow field) in addition to the agricultural ET. This is made possible by using an ML model\nto predict fallow/natural ET and subtracting the natural ET from the observed total ET provided by\nOpenET (derived from satellite observations). The agricultural ET maps are then used to evaluate the\nreduction in ET (reduction in actual water use) that could be obtained from switching crop types,\nchanging farming practices, or fallowing fields. The study finds that all three scenarios have similar\ntotal reductions, but the fallowing scenario required land use changes in only 5% of fields while the\nother two scenarios impacted 50% of fields. These results can be used by decision-\nmakers/policymakers to determine the most effective (in terms of cost, environmental, and social\nfactors) strategies for reducing water use.\nOverall I found the manuscript to be well written, easy to understand, and to have addressed most of\nthe potential limitations of the analysis. There are only a few points that I had questions about:\n1. One of the water-saving scenarios discussed is described as changing farming practices, which\ninvolves keeping the same spatial allocation of crops but changing the highest consumers to the\nmedian consumption level. While it cannot be stated for certain that the variations in agricultural ET\nwithin crops are due to farming practices, the authors showed that only a small percentage of the\nvariations could be explained by other factors and thus is most likely due to farming practices.\nHowever, there was no discussion of what those farming practices actually are, and I did not see any\nanalysis to show that any of the variation was due to farming practices. Is this due to a lack of data\nabout farming practices? It’s unclear without this discussion what would actually be involved in\nchanging farming practices (particularly from a policy/economic perspective – would this be a major\nchange or small change for a farmer?).\n2. Are there errors associated with the OpenET observations that should be propagated when used in\nEqns 1 and 4?\n3. Lines 313-314 state that the analysis could only be conducted for years 2016, 2018, and 2019\nbecause the year was used as a covariate in the ML model to predict natural ET. How sensitive are the\nmodel predictions to this variable? It seems like this could be a limiting factor for applying the same\nmethodology to other locations/years since data will not always be available for every year, so it would\nbe helpful to know how important that variable is to make accurate predictions.\nMinor comments\n• Line 208 has a typo: “irritation” instead of “irrigation”\n• The split percentage for the fallow ET training dataset is described, but I did not see the total\nnumber of samples. Can this be added?", "reviewer_2": "Review: Field-scale crop water consumption estimates reveal potential water saving in California\nagriculture\nThis paper led by Boser et al. attempts to separate naturally occurring ET (ETnat) and agriculture\nET(ETag) in the Central Valley using remotely sensed ET and machine learning model. The estimates\nof ETag are then being used to understand patterns and derive scenarios for water savings, with an\nemphasis on land management (fallowing and alternative crops) and irrigation efficiency scenarios.\nThe idea of separating the naturally occurring ET is novel, but the methodological approach does not\nseem robust. The estimates of irrigation efficiency seems flawed, and it fails to acknowledge/discuss\nthe irrigation efficiency paradox (i.e., higher efficiency != more water saving, see:\nhttps://www.science.org/doi/10.1126/science.aat9314). The pathway of applying water-saving land\nmanagement scenarios is interesting and can be applied to other areas with minor manipulation.\nHowever, the paper failed to address the opportunity cost, water saving is only one of the many goals\nthat farmers are trying to achieve. Below, I provide my detailed comments:\n1) Separating the naturally occurring ET is the main crux of the paper. However, I am unclear how this\nwas achieved. The paper talks about ML model, predictors used, and splitting the data between\ntraining, validation, and testing. What was the source of natural ET for building the model? I am\nguessing, based off of the description of fallow land identification, it was derived from OpenET. If this\nis true then, I am not confident that OpenET can accurately represent natural ET on fallow land. In my\nunderstanding OpenET was not validated for this environment and an over-prediction is very likely and\nquite apparent in Fig. 2. Natural ET for Pasture and Rice is closely following Agriculture ET and in\nmany counties (San Joaquin and many neighboring counties) natural ET is more than agriculture ET\n(Fig. 1). 46% ET being contributed from naturally occurring ET on fallow land with no residual\ncrop and in a Mediterranean climate warrant more ground truthing. What is the uncertainty associated with\nusing PET data at such a course 0.1 degree resolution?\n2) Concluding that crop switching and/or land fallowing as a scenario for saving water is not novel,\nPPIC and others have estimated potential water savings. In fact, PPIC extended their analysis to\nmarket and other considerations for the growers (https://www.ppic.org/publication/exploring-the-\npotential-for-water-limited-agriculture-in-the-san-joaquin-valley/ ,\nhttps://www.sciencedirect.com/science/article/pii/S0048969722070632). Authors should consider\nmore comprehensive analysis of cost and benefits of different alternatives.\n3) The calculation of irrigation efficiency seems flawed. First, I do not know the details of the USGS\ndelivery data and whether it accounts for water moving in and out of counties. Looking at the map\n(Fig. 4), it is hard to comprehend that Mariposa and Madera counties have higher irrigation than\nMerced and Fresno. San Francisco and Marine counties also jump out. The north south valley split in\nthe irrigation efficiency (Fig. 4, E8) suggests me that USGS data is likely incomplete and this pattern\nmay very well driven by the local and state/federal water deliveries. Or it could be an artifact of your\nanalysis time mismatch. Also, the comparison between theoretical and empirical irrigation efficiency is\nunfair since theoretical efficiencies do not account for conveyance losses.\n4) Discussion section is very superficial and can be improved by referencing prior work on irrigation\nefficiency paradox, complexities with land fallowing and crop switching, market pressure, water right,\nland retirement for other beneficial use like solar, wildlife habitat, and even FloodMAR.\n5) Lastly, I am intrigued by your use of the word empirical vs. modeled. Example: Agriculture ET\nderived from CalSIMETAW is being referred as modeled but same data from OpenET is empirical. Even\nfor the sake of clarity, it would be better to refer the data by source.\nSpecific Comments:\na) More information should be provided on how various variables are influencing ETnat prediction\nusing ML model. It was briefly mentioned in discussion section (line 240-242) but is not well-defined.\nb) The spatial scale of all analyses differs from each other. ETnat was carried out at 70m resolution,\nvariation in ET at Valley, water saving potential at groundwater sub-basin and irrigation efficiency at\ncounty level. More information on how spatial aggregation was done for the ET components from 70m\nto county/sub-basins scales and reasons for choosing these scales will be helpful.\nc) Add details on crop groupings shown in Fig. 2 in the supplementary material.\nd) Fig. A4 can be represented better with contrasting colors, hard to see the overlap. Also, plots with\ncombination of bar and box plots can be improved with different color choices and adding gridlines.\ne) The title seems misleading as the field-scale estimates are based of remotely sensed and other data\nof different scales, aggregated for the suggested water saving strategies at county/groundwater sub-\nbasin scales. You may want to consider revising it.\nf) Some sentences are lengthy and can be further simplified in the Introduction section. For example:\nlines 44-47, 68-71, 51-53.\ng) Given how the ML model was developed, should you be still calling the ET over fallow land as\nnatural? Don’t you think fallow ET would be more appropriate?\nLine-specific comments:\nL46: evapotranspiration is “both” evaporation and transpiration that often occur simultaneously.\nL111: “R2”\nL124: More information on how 34% was calculated will be helpful for readers.\nL125: “water intensive crops”\nL128: reference to “(Figure A1)” seems wrong.\nL207-208: You can write that “…. 30% deficit irrigation for some crops (wheat, maize, cotton etc.)” as\nthe referred studies are only for limited crops.\nL211-214: The point made here makes sense, but not sure how relevant is the precipitation factor for\nCentral valley agriculture as the peak demand and rainfall occurs are different times of the year.\nAppendix C: Provide citation for CalSIMETAW", "reviewer_3": "Review of manuscript titled: “Field-scale crop water consumption estimates reveal potential water\nsavings in California agriculture”\nSummary: Authors present a study where through satellite remote sensing and machine learning they\ncan differentiate between naturally occurring evapotranspiration (ET) and agricultural ET (the increase\nin ET induced by irrigation) to better quantify agricultural water use in the state of California; and by\ndoing so offer potential solutions to decrease agricultural water use. This study is timely and relevant\nand takes advantage of the emerging technologies and approaches now available to earth system\nscientists. The scope of the manuscript is ambitious, but the authors have done a good job of\nexplaining the steps and approaches/assumptions taken. I do believe the approach taken is novel (the\nsubtraction of the naturally occurring ET) but am not entirely convinced of its validity. Other studies\nhave quantified crop water use over the state using ET (Wong et al., 2021; Schauer et al., 2019;\namong others), but are not discussed in the manuscript. This may simply be due to the writing\nconstraints of Nature Communications, but I still believe it is important to bring up.\nOverall, I think this is a wonderful and important study. But I do have some general concerns that I\nthink should be addressed prior to publication.\nConcern #1. This is a novel approach. Quantifying agricultural ET by determining the ET that would be\nnaturally occurring in that location should the crop (and applied irrigation) not be present and\nsubtracting it from the actual ET observed via OpenET. That said, I’m hesitant to agree that the\ndefinition of agricultural ET is this difference. My hesitation stems from the results stating that, on\naverage/in general, 46% of total ET is defined as naturally occurring and 53% of total ET is defined as\nagricultural ET. Intuitively this seems very high for ‘naturally occurring’. If I were in an almond orchard\nin September, I would not expect that 46% of the total ET coming off the field would be naturally\noccurring and only 53% would be because of the almond tree and the applied irrigation. I know you\nhave broken these percentages down into crop categories, but they still seem high. I’m also aware\nthat there are likely some time dependency factors that play a role. That said, I’m curious if you have\nany citations confirming this is a viable assumption or if you have ground data that can be used to\nvalidate your assumption. Also, because the idea behind the study is to quantify agricultural ET, should\nthere be more of a month-by-month analysis or growing season analysis? I think it is important prior\nto scaling an approach like this to the entire state and making claims about water use that a proof of\nconcept be completed, either on the ground or found in the literature.\nConcern #2. I apologize if I missed this in the manuscript, or if it is common knowledge, but could you\nplease provide a thorough description of “diversion” that is used throughout the manuscript in\nreference to water diversions and irrigation. My interpretation is that diversion is water found/stored in\none location (i.e., county) and “diverted” to another location (i.e., another county). This interpretation\nmakes sense when looking at Fig. 4 (left column) where we find higher water diversions for irrigation\nin the north (the Sacramento River Valley and north). Water in the northern portion of the state is\nfamously (or infamously, depending on where in the state you are) moved further south for\nagricultural irrigation. In my mind this makes the southern counties much less efficient (in terms of\nirrigation efficiency) compared to the north. However, the opposite is presented, with the southern\ncounties reporting higher irrigation efficiencies. Could you please offer more context to this Figure\nand the overall implications made from the associated analysis?\nConcern #3. Different parts of the manuscript discuss the within crop variability in ET as well as\nstatements alluding to how agricultural water applications can be significantly decreased (from 95\npercentile to 25 percentile) without changing the landcover. First, a likely reason for ET variability\nwithin the same cropping system (particularly tree nuts) is because of the age of the orchard. These\nsystems are perennials and exist for 15-25 years. It makes sense that an annual would present lower\nvariation – its effectively the same crop every year. A pistachio orchard changes drastically from 2nd\nleaf to 20th leaf, with a considerable amount of increase to biomass and by proxy ET. In order to make\nthe claim that water savings can occur without changing the crop, there needs to be an accounting of\norchard age. Second, I do not think that scenario #2 in reducing agricultural ET is a viable scenario.\nSure, deficit irrigation is possible and is well studied in certain cropping systems. In some, such as\nwine grapes, its required to produce a high-quality grape. But to suggest cutting back irrigation based\non ‘high’ ET estimates to ‘median’ ET estimates without including age (again, perennials introduce\nsome difficulty here), production goals of the commodity, or management style seems cavalier.\nMore specifics below:\nLine 31: This first sentence is a bit confusing – are you saying the overexploitation of ‘drought’ and\n‘climate change’ have led to ….. it seems like there is simply some grammatical error tying the\nsentence together.\nLine 43: “because a large share of irrigation water is returned to the system as runoff or recharge”. I\nunderstand the statement and it is true, but because the study takes place in California, it is important\nto note the high efficiency of the irrigation in the state (drip or micro-sprinklers installed). In these\nsystems, very little irrigated water is lost to runoff or deep percolation.\nLine 46: Consider rephrasing in that evapotranspiration is not evaporated by the soil but rather from\nthe soil to the atmosphere.\nLine 51: This last sentence is a bit confusing to me. How does decreasing water diversions not affect\nfood production? Because irrigation water does not contribute to crop growth? Maybe I’m missing\nsomething?\nLine 112: Here you give error metrics in cm/year, but you specify they will be mm/year on line 285. I\nwould suggest reporting values in mm/year as I’ve never seen ET reported in cm/year. It might also\nbe worth mentioning that all growers use acre-in or acre-feet as their unit of choice. I would suggest\nconverting your overall units (mm/day) to acre-in/acre-feet in maybe the conclusion/discussion so a\nbroader readership can more quickly interpret your results.\nLine 113: Your test set is made up of 2 km2 regions – can you mention how many? Also, these regions\nrepresent fallowed land. Are all 2 km2 regions completely fallowed land? I guess I’m asking if all\nfallowed pixels chosen are arbitrary 2 km2 boundaries placed all over the central valley and not\npolygons outlining specific fallowed fields.\nLine 137: See concern #3.\nLine 149: See concern #3.\nLine 208: Irrelevant and misleading. This 30% figure is from citations looking at wheat, maize, and\ncotton; all of which are annuals and all of which have no notable economic viability in California\n(combined, their economic output is only 2% of almonds and grapes combined).\nLine 250: Sorry for likely not following, but isn’t this 7% much lower than values specified earlier?\nWhere is this coming from?"}, "author_response": "Response to reviewers: Field-scale crop water\nconsumption estimates reveal potential water savings in\nCalifornia agriculture\nNovember 25, 2023\nWe would like to thank all of the reviewers for their thoughtful comments which have\nguided us in the revision of this manuscript. This document provides a guide to the nature\nand location of the changes prompted by the comments. Below, we reproduce each reviewer\ncomment italicized in gray and provide our replies in black. Excerpts from the paper are\nindented with the line numbers noted, with changes highlighted in red. Figures that appear\nin the main text or SI retain their original names and numbering. Additional figures and\ntablesareprefixedwiththeletter“R”inordertodistinguishthemfromfiguresinthepaper.\nThis study reveals how much of the evapotranspiration (ET) in agricultural fields is due to\nthe specific crop being grown there, as opposed to the total ET which includes natural ET that\nwould occur without crop growth (fallow field) in addition to the agricultural ET. This is made\npossible by using an ML model to predict fallow/natural ET and subtracting the natural ET\nfrom the observed total ET provided by OpenET (derived from satellite observations). The\nagricultural ET maps are then used to evaluate the reduction in ET (reduction in actual\nwater use) that could be obtained from switching crop types, changing farming practices, or\nfallowing fields. The study finds that all three scenarios have similar total reductions, but\nthe fallowing scenario required land use changes in only 5% of fields while the other two\nscenarios impacted 50% of fields. These results can be used by decision-makers/policymakers\nto determine the most effective (in terms of cost, environmental, and social factors) strategies\nfor reducing water use.\nOverall I found the manuscript to be well written, easy to understand, and to have ad-\ndressed most of the potential limitations of the analysis. There are only a few points that I\nhad questions about:\n1.1 Discussion of farming practices\n1\n1. One of the water-saving scenarios discussed is described as changing farming practices,\nwhich involves keeping the same spatial allocation of crops but changing the highest consumers\nto the median consumption level. While it cannot be stated for certain that the variations\nin agricultural ET within crops are due to farming practices, the authors showed that only a\nsmall percentage of the variations could be explained by other factors and thus is most likely\ndue to farming practices. However, there was no discussion of what those farming practices\nactually are, and I did not see any analysis to show that any of the variation was due to\nfarming practices. Is this due to a lack of data about farming practices? It’s unclear without\nthis discussion what would actually be involved in changing farming practices (particularly\nfrom a policy/economic perspective – would this be a major change or small change for a\nfarmer?).\nThank you for your comment, which has prompted us to revisit our discussion of the\nfarming practices scenario. As you suspected, the reason we do not conduct further analyses\nto identify the water saving potential of specific farming practices is because there are no\nfield-level data available in California that would allow for such an analysis. However, there\nis published research investigating the water-saving potential of individual farming practices\nin particular locations. We have updated the paper to include a more detailed discussion of\nthis prior literature and its implications for our findings.\nWe have made three specific changes in response to this comment. First, we have ex-\npanded our discussion to include previous literature on water saving farming practices. We\ncite practices such as mulching, conservation tillage, deficit irrigation, and improved irri-\ngation scheduling and technologies. Second, we touch on the economic and policy con-\nsiderations involved in farm-level management changes by highlighting the relative cost-\neffectiveness of these practices compared to other water-saving strategies. Finally, we un-\nderscore the importance of assembling and sharing data on such farming practices for future\nresearch, as it would enable a more comprehensive assessment of the water-saving potential\nof various farming practices.\nThese changes are reflected in paragraph 3 of the discussion (line 243):\n[...] Even after adjusting for variability attributable to field characteristics, orchard age,\nand climate, we find that these variations could translate to substantial water savings\nwithout requiring a change in crop type. Unfortunately, a lack of high-resolution data\non field-scale farming practices and yields inhibits further analysis of specific practices\ndriving agricultural ET variation and their economic implications. However, prior lit-\nerature from experimental plots or particular locations suggests that mulching (Kader\net al., 2019), conservation tillage (Mitchell et al., 2012), deficit irrigation (English and\nRaja, 1996; Goldhamer, 1999; Rudnick et al., 2019), and improved irrigation scheduling\nand technologies (Jovanovic et al., 2020) all have potential to limit agricultural ET.\nThese practices may prove advantageous relative to costly strategies like crop-switching\nor fallowing, though more detailed cost-benefit analyses are necessary to determine the\nsuitabilityofvariousinterventionsinspecificcontexts(Mitchelletal.,2016). Suchwater\nsaving farming practices are not mentioned in the plans drafted by SGMA water man-\nagers (Bruno et al., 2022). This is possibly due to a lack of conclusive research on the\n2\npotential of such strategies to effectively decrease agricultural ET without significant\neffects on yield or operation cost. Spatial data on the use of these different practices\nwould allow researchers to take full advantage of our high-resolution agricultural ET\nestimates and study their water-saving and economic benefits.\n1.2 Error propagation\n2. Are there errors associated with the OpenET observations that should be propagated when\nused in Eqns 1 and 4?\nWeappreciateyourqueryregardingthepropagationoferrorsfromOpenETandtakethis\nopportunity to clarify this aspect of our methodology and enhance the rigor of our analysis.\nWe note that because eq. 1, ET = ET −ET , serves as a definition of what we consider\nag tot nat\natheoretical, “true”agriculturalET,wedonotintroduceerrortermsatthatstage. However,\nerrors certainly do affect our empirical estimates of agricultural ET, which could for example\npropagate into eq. 4, AdjustedET = β ×Group +ϵ . In our revision, we have added\np,y p,y p,y p,y\nboth a conceptual and empirical investigation of error propagation, which shows how errors\nboth from OpenET and from our machine learning model may influence our downstream\nestimates of agricultural ET. We conclude that empirically, these errors are insufficient to\ninfluence key findings of the paper.\nWe detail our new analysis on this topic in Supplementary Note 4 copied below, but sum-\nmarize it briefly here. Several error terms are introduced into the agricultural ET estimates:\ntwo from error in OpenET and one from error in our machine learning model. These errors\nhave the potential to introduce bias into our estimates of agricultural ET and to inflate esti-\nmated variance over space and/or time in agricultural ET. Using OpenET’s Intercomparison\nand Accuracy report (OpenET, 2021) and our own validation of the machine learning model\n(Supplementary Fig. 1, 2, 3, and 4), we fine that all of these errors are unbiased (mean zero).\nBecause they are unbiased, they are captured in the random error term in the regression\nequations 3 and 4. We consequently do not adjust these equations in the manuscript.\nHowever, error still artificially inflates variance in agricultural ET. This impacts our anal-\nysis of the water saving potential of farmer’s practices and fallowing, because variations in\nestimated agricultural ET across space is used to investigate possible water saving. We now\nshow that the variance of our estimated agricultural ET is equal to the true variance in\nagricultural ET plus the variance of the error we accrue. However, we estimate that vari-\nance of the error terms is only 11% of the total variance we find across our agricultural ET\nestimates. Therefore, the vast majority of the variation we retrieve is in fact signal rather\nthan noise. We include this decomposition of error in new Supplementary Note 4 (SI line\n76) and we additionally note this finding in the discussion in the main text (line 298).\nmachine learning model error on agricultural ET es-\ntimates\nWe define agricultural ET as the difference between total ET and naturally-occurring\nET, ET = ET − ET (Main text eq. 1), where naturally-occurring ET is the\nag tot nat\ncounterfactual ET that would occur naturally, were the same land fallow. However,\nwe calculate agricultural ET using estimates of total ET from OpenET and modeled\nnaturally-occurring ET trained on OpenET data (Supplementary eq. 1). Since the\nOpenET data and the model we use to estimate naturally-occurring ET have errors\nassociated with them, our estimates of agricultural ET can be described as the true\nagricultural ET plus several error terms (Supplementary eq. 4), which we derive as\nfollows:\n˜\nET∗ = ET ˆ −ET ˆ (1)\nag tot nat\nwhere ET∗ denotes our estimate of agricultural ET, ET ˆ is OpenET’s estimate of\nag tot\n˜\nˆ\nET and ET is our estiate of the naturally-occurring ET, predicted by our machine\ntot nat\nlearning (ML) model. The hat notation indicates estimates inclusive of OpenET error,\nwhile the tilde indicates estimates inclusive of ML model error.\nBecause our naturally-occurring ET ML model is trained on and therefore predicts\nOpenET values over fallow lands, we can rewrite the same expression as:\nET∗ = ET ˆ −(ET ˆ +ϵ ) (2)\nag tot nat ML\nˆ\nwhere ET is the (naturally-occurring) ET that OpenET would observe if the field\nnat\nˆ\nwere fallow, and ϵ is the error from the ML model in predicting ET .\nML nat\nWe can then again reorganize the same equation by separating the error from the\nOpenET observations. This reveals the relationship between the true ET and our\nag\nestimate, ET∗ :\nag\nET∗ = ET +ϵ −(ET +ϵ +ϵ ) (3)\nag tot OpenET,ag nat OpenET,fal ML\n= ET +ϵ −ϵ −ϵ (4)\nag OpenET,ag OpenET,fal ML\nwhereET , ET , andET arethetrue(unobservable)agriculturalET,totalET,and\nag tot nat\nnaturally-occurring ET, respectively, ϵ is the error associated with the OpenET\nOpenET,ag\nestimate over the agricultural field and ϵ is the error associated with what the\nOpenET,fal\nOpenET estimate would be if the land were fallow.\nIn our study, we seek to estimate mean ET , be it over the entire Central Valley, for\nag\na certain crop type, or for a given county, we seek to calculate the expectation of ET\nag\nover a sample of grid cells. Using Supplementary eq. 5, we can show that our estimate\nof E[ET∗ ] depends not only on E[ET ], but also the expectation of the error terms.\nag ag\nag ag OpenET,ag OpenET,fal ML\nWe show in Supplementary Figures 1-4 that ϵ is unbiased, that is, E[ϵ ] = 0.\nML ML\nOpenET has also been shown to produce unbiased estimates over agricultural lands, so\nE[ϵ ] = 0 (see Table 3 on page 6 of the OpenET Intercomparison and Accuracy\nOpenET,ag\nreport) (OpenET, 2021). We can also assume ϵ is unbiased because though\nOpenET,fal\nOpenET has not specifically been evaluated over fallow fields, it produces unbiased\nestimates over natural shrublands and grasslands with ET values of similar magnitude\nto fallow fields in the Central Valley (OpenET, 2021). As a result, in expectation our\nestimatesofagriculturalETareunbiased, andconsequentlyproduceunbiasedregression\ncoefficients.\nE[ET∗ ] = E[ET ] (6)\nag ag\nWhile our estimates of agricultural ET are unbiased in expectation, the error terms\ndo add variance that is not present in the true agricultural ET. Therefore, any analysis\nthat assesses variation across pixels will reflect inflated variance relative to true agri-\ncultural ET. For example, this has implications for the farming practices and fallowing\nscenarios we conduct (Fig. 3). However, here we estimate that error is responsible\nfor only 11% of the variance in our annual estimates of agricultural ET, suggesting its\ninfluence over key results is limited. Specifically, if we assume that all error terms are\nindependent, we have:\nvar(ET∗ ) = var(ET )+var(ϵ )+var(ϵ )+var(ϵ ) (7)\nag ag OpenET,ag OpenET,fal ML\nwhere var(·) denotes the variance.\nThen, because variance is equal to mean squared error (MSE) when bias is 0, we\nhave:\nvar(ET∗ ) = var(ET )+MSE(ϵ )+MSE(ϵ )+MSE(ϵ ) (8)\nag ag OpenET,ag OpenET,fal ML\nWecalculatetheMSEofourmachinelearningmodelusingourtestset, andOpenET\nprovides root MSE values over croplands and shrublands (which we use to approximate\nerror over fallow lands) (again, see Table 3 on page 6 of the OpenET Intercompari-\nson and Accuracy report) (OpenET, 2021). We find that for estimates of yearly ET∗ ,\nag\nMSE(ϵ )+MSE(ϵ )+MSE(ϵ ) represents only 11% of var(ET∗ ).\nOpenET,ag OpenET,fal ML ag\nThis indicates that the vast majority of the variance we uncover is representative of true\ndifferences in water consumption across fields, as opposed to variability due to model\nerrors.\ntial and its limitations. As such, we have added a passage in the limitations section of the\ndiscussion that highlights the main takeaways of this inquiry (line 298):\nThis study has some important limitations. Most notably, due to a lack of data on\nfarming practices, it is difficult to ascertain whether the variation in within-crop agri-\ncultural ET that we estimate is indeed due to farming practices. When conducting our\nmanagement scenarios, we account for the effects of climate, soil quality, topography,\nand orchard age. We additionally conduct the scenarios at the level of small groundwa-\nter sub-basins rather than across the entire valley to account for any additional regional\nenvironmental differences we are unable to otherwise account for. However, variance\nstemming from error in our agricultural ET estimates or from mislabeled crop types\ncould contribute to observed within-crop variation in agricultural ET. We minimize\nerror from mislabeled crop types by using the most accurate crop data available in Cal-\nifornia which boasts an accuracy of 97.6% (LandIQ, 2021). Additionally, the OpenET\nensemble model has been extensively validated (Melton et al., 2022; OpenET, 2021),\nand our machine learning model has an R2 of .87. We estimate that these sources of\nerror are responsible for only 11% of the variance in our yearly agricultural ET esti-\nmates (Supplementary Note 4). Nevertheless, the water-saving potentials we calculate\nfor both the fallowing and farmers practice scenarios should be interpreted as upper\nbound estimates.\n1.3 Using ‘year’ as a covariate\n3. Lines 313-314 state that the analysis could only be conducted for years 2016, 2018, and\n2019 because the year was used as a covariate in the ML model to predict natural ET. How\nsensitive are the model predictions to this variable? It seems like this could be a limiting\nfactor for applying the same methodology to other locations per years since data will not\nalways be available for every year, so it would be helpful to know how important that variable\nis to make accurate predictions.\nThank you for bringing to our attention the potential limitation imposed by using ‘year’\nas a covariate in our model. To address this, we have performed an analysis of feature im-\nportance within our model, which included assessing the impact of the ‘year’ variable on\nthe model’s predictions. Our findings suggest that the ‘year’ is not among one of the most\ncritical predictors in the context of the overall model performance, contributing only 3% of\nthe model’s predictive capability (Supplementary Fig. 17).\nboosting model on its predictions of naturally-occurring ET.\nIn light of this, we believe that our methodology holds the flexibility to be adjusted for\napplications where ‘year’ data may be incomplete or entirely unavailable. We have included\nthis insight in the methods section (line 379).\nHowever, the inclusion of an indicator variable for each year in our model limits our\nanalysis to 2016, 2018, and 2019, as these are the only years for which we have available\nland cover data. Nevertheless, given that ’year’ contributes a mere 3% to our model’s\npredictive capability (see Supplementary Fig. 17), it may be considered non-essential\nfor future research in this area.\nWe hope that these changes will provide a comprehensive view of the variable importance\nand offer additional insights into the robustness and adaptability of our method.\n1.4 Minor comments\nLine 208 has a typo: “irritation” instead of “irrigation”\nThank you for your attention to detail; we have fixed this misspelling.\nnumber of samples. Can this be added?\nThank you for pointing out this oversight. We have added the following statement in the\nmethods section (line 396).\nTo validate our naturally-occurring ET model, we split our dataset, reserving 60% for\ntraining, 10% for validation, and 30% for testing. In order to ensure that nearby and\ntherefore very similar pixels are not present across multiple splits, we group our splits\nby 2 km2 squares, four times the size of a large agricultural field in the Central Valley.\nThe entire dataset is made up of over 16 million pixels populating 8,180 distinct 2 km2\nregions. Nearly 10 million pixels and 4,908 2 km2 clusters are contained in the training\nsplit, and 2,454 clusters are reserved for testing.\n8\nReview: Field-scale crop water consumption estimates reveal potential water saving in Cali-\nfornia agriculture\nThis paper led by Boser et al. attempts to separate naturally-occurring ET (ETnat) and\nagriculture ET(ETag) in the Central Valley using remotely sensed ET and machine learning\nmodel. The estimates of ETag are then being used to understand patterns and derive sce-\nnarios for water savings, with an emphasis on land management (fallowing and alternative\ncrops) and irrigation efficiency scenarios. The idea of separating the naturally-occurring ET\nis novel, but the methodological approach does not seem robust. The estimates of irrigation\nefficiency seems flawed, and it fails to acknowledge/discuss the irrigation efficiency paradox\n(i.e., higher efficiency != more water saving, see: https://www.science.org/doi/10.112\n6/science.aat9314). The pathway of applying water-saving land management scenarios is\ninteresting and can be applied to other areas with minor manipulation. However, the paper\nfailed to address the opportunity cost, water saving is only one of the many goals that farmers\nare trying to achieve. Below, I provide my detailed comments:\nThank you for your valuable feedback, which has been instrumental in enhancing our\npaper. Regarding your overarching observations, we have made the following amendments,\nwhich we describe in greater detail below:\n1. We conducted a comprehensive analysis of potential errors in our methodology for\nestimating agricultural ET and confirmed the robustness of our approach. Details of\nthisanalysishavebeenaddedtothesupplementaryinformationandarenowreferenced\nin the main text for further clarity.\n2. Inresponsetoyourconcernsaboutourmethodologyforestimatingirrigationefficiency,\nwe have refined our approach for greater clarity. The manuscript additionally now\nincludes an expanded discussion on the irrigation efficiency paradox, offering a more\nnuanced understanding of this complex issue.\n3. We have enriched our study by incorporating a discussion of the opportunity costs\nassociated with various water-saving strategies, as explored in prior literature. This\naddition aims to provide a more comprehensive perspective on the economic implica-\ntions of these strategies.\n2.1 Errors associated with OpenET\n1) Separating the naturally-occurring ET is the main crux of the paper. However, I am un-\nclear how this was achieved. The paper talks about ML model, predictors used, and splitting\nthe data between training, validation, and testing. What was the source of natural ET for\nbuilding the model? I am guessing, based off of the description of fallow land identification,\nit was derived from OpenET. If this is true then, I am not confident that OpenET can accu-\nrately represent natural ET on fallow land. In my understanding OpenET was not validated\nfor this environment and an over-prediction is very likely and quite apparent in Fig. 2.\n(San Joaquin and many neighboring counties) natural ET is more than agriculture ET (Fig.\n1). 46% ET being contributed from naturally-occurring ET on fallow land with no residual\ncrop and in a Mediterranean climate warrant more ground truthing. What is the uncertainty\nassociated with using PET data at such a course 0.1 degree resolution?\nThank you for your thoughtful critique focusing on the critical aspect of separating\nnaturally-occurring ET in our paper. As we describe in detail below, your comments have\nprompted us to (1) refine our explanation of how we achieve this separation and (2) con-\nduct a comprehensive analysis of potential errors, particularly those associated with our use\nof OpenET data. This analysis has been instrumental in enhancing the robustness of our\nmethodology and ensuring the accuracy of our findings. Lastly, we address your concern\nsurrounding the use of coarse PET data.\n2.1.1 Clarifying our methods\nWe apologize for the lack of clarity in our methods. You are correct that we used OpenET\nestimates over fallow lands to train the model predicting naturally-occurring ET. In order to\nimprove clarity surrounding this point, we revise the introduction where we first introduce\nour approach (line 95):\nHere, we develop a novel framework for measuring agricultural ET at sub-field scales,\nusing remote sensing to determine total ET and machine learning to estimate naturally-\noccurring ET. First, we retrieve satellite-based remotely sensed total ET estimates from\nthe 30m, monthly OpenET ensemble data (Melton et al., 2022) available in the western\nUnited States starting in 2016. Second, since we can use these same OpenET estimates\nto directly observe naturally-occurring ET over fallow lands, we train a gradient boost-\ning algorithm to predict ET over fallow lands, using information on topography, soil\nquality, climate, and spatial and temporal coordinates. We then use the model to re-\ntrieve naturally-occurring ET over all active agricultural fields, which we subtract from\nthe remotely-sensed ET to calculate agricultural ET.\nAdditionally, we add a sentence at the beginning of the results section to further clarify\nthis point (line 120):\nWe calculate agricultural ET by retrieving the total ET observed over agricultural areas\nandsubtractingnaturally-occurringET(Fig. 1). WhiletotalETestimatesareretrieved\nfrom OpenET, we simulate naturally-occurring ET by training a gradient boosting re-\ngressor to predict the ET observed by OpenET over fallow lands.\n2.1.2 Errors associated with our estimates of naturally-occurring ET\nthe adequacy of our naturally-occurring ET estimates, we follow several steps including the\nconsideration of potential biases arising from OpenET data.\n1. Investigation of OpenET and ML model errors. We investigate potential errors\nassociatedwithOpenETandourmachinelearningmodel,andfindnoreasontosuspect\nbiased estimates arising from either. We undertake an analysis fully detailing the role\nof both in our estimates of agricultural ET (Supplementary Note 4).\n2. Additional analyses. Tofurtherensuretheadequacyofourestimatatesofnaturally-\noccurring ET, we additionally undertake an analysis of land cover data accuracy (Sup-\nplementary Note 6), conduct a comparison with other estimates, and consider the\nrobustness of our findings against hidden biases.\n3. Clarification of annual ET values. We recognize that our naturally-occurring ET\nvalues can seem high since we report annual values as opposed to ones confined to the\ngrowing season. To stave off confusion, we edit the manuscript to underscore this fact.\nInvestigation of OpenET and ML model errors. While it is true that OpenET’s\nvalidation has been focused on croplands and has not been validated over fallow lands specif-\nically, it has been validated over natural lands such as grasslands and shrublands. According\nto OpenET’s Intercomparison and Accuracy Assessment Report, croplands, grasslands, and\nshrublands, all which may help represent the kinds of errors we might expect over fallow\nlands, exhibit no statistically significant biases, and therefore do not suggest that OpenET\nispositivelybiasedoverfallowlands(TableR1)(OpenET,2021). Shrublandsmayespecially\nbe considered as a good proxy for the fallow lands of the Central Valley since the average\nET of the shrublands used in the OpenET validation, 31.07 mm/month, is similar to what\nOpenET predicts over fallow lands in the Central Valley (mean = 36.5 mm/month).\nTo improve transparency surrounding the effect of errors inherent to our method, we have\nadded a section in the supplementary information detailing the role of error in our analysis\n(Supplementary Note 4, SI line 76).\nSupplementary Note 4: The effect of OpenET and\nmachine learning model error on agricultural ET es-\ntimates\nWe define agricultural ET as the difference between total ET and naturally-occurring\nET, ET = ET − ET (Main text eq. 1), where naturally-occurring ET is the\nag tot nat\ncounterfactual ET that would occur naturally, were the same land fallow. However,\nwe calculate agricultural ET using estimates of total ET from OpenET and modeled\nnaturally-occurring ET trained on OpenET data (Supplementary eq. 1). Since the\nOpenET data and the model we use to estimate naturally-occurring ET have errors\nassociated with them, our estimates of agricultural ET can be described as the true\nSlope 0.95 0.86 - 1.04\nMBE (mm) -3.64 (-3.9%) -13.77 - 5.16 (-14.7% - 5.5%)\nCroplands\nMAE (mm) 155.66 (16.6%) 17.96 - 22.92 (19.2% - 24.5%)\n45 sites, N = 1682 months RMSE (mm) 195.17 23.43 - 25.89 (25% - 27.7%)\nMean station ET = 93.68 (mm) R-squared 0.95 0.89 - 0.93\nSlope 1.03 0.73 - 1.28\nMBE (mm) -1.23 (-2.9%) -11.88 - 9.73 (-27.9% - 22.9%)\nGrasslands\nMAE (mm) 19.45 (15.4%) 20.17 - 28.1 (47.4% - 66.0%)\n20 sites, N = 672 months RMSE (mm) 24.12 (56.7%) 24.62 - 35.96 (57.8% - 84.5%)\nMean station ET = 42.56 (mm) R-squared 0.75 0.54 - 0.8\nSlope 0.95 0.65 - 1.34\nMBE (mm) 2.89 (9.3%) -5.2 - 18.7 (-16.7% - 41.4%)\nShrublands\nMAE (mm) 15.68 (50.5%) 17.45 - 22.64 (56.2% - 72.9%)\n24 sites, N = 681 months RMSE (mm) 19.96 (64.2%) 20.9 - 29.18 (67.3% - 93.9%)\nMean station ET = 31.07 (mm) R-squared 0.66 0.3 - 0.69\nTable R 1. OpenET monthly performance over croplands, grasslands, and shrublands.\nMBE denotes mean bias error, and none of the land cover types show statistically significant MBE.\nReproduced from the OpenET Intercomparison and Accuracy Assessment Report, Table 3 (pg.6).\nagricultural ET plus several error terms (Supplementary eq. 4), which we derive as\nfollows:\n˜\nET∗ = ET ˆ −ET ˆ (1)\nag tot nat\nwhere ET∗ denotes our estimate of agricultural ET, ET ˆ is OpenET’s estimate of\nag tot\n˜\nˆ\nET and ET is our estiate of the naturally-occurring ET, predicted by our machine\ntot nat\nlearning (ML) model. The hat notation indicates estimates inclusive of OpenET error,\nwhile the tilde indicates estimates inclusive of ML model error.\nBecause our naturally-occurring ET ML model is trained on and therefore predicts\nOpenET values over fallow lands, we can rewrite the same expression as:\nET∗ = ET ˆ −(ET ˆ +ϵ ) (2)\nag tot nat ML\nˆ\nwhere ET is the (naturally-occurring) ET that OpenET would observe if the field\nnat\nˆ\nwere fallow, and ϵ is the error from the ML model in predicting ET .\nML nat\nWe can then again reorganize the same equation by separating the error from the\nOpenET observations. This reveals the relationship between the true ET and our\nag\nestimate, ET∗ :\nag\nET∗ = ET +ϵ −(ET +ϵ +ϵ ) (3)\nag tot OpenET,ag nat OpenET,fal ML\n= ET +ϵ −ϵ −ϵ (4)\nag OpenET,ag OpenET,fal ML\nwhereET , ET , andET arethetrue(unobservable)agriculturalET,totalET,and\nag tot nat\nnaturally-occurring ET, respectively, ϵ is the error associated with the OpenET\nOpenET,ag\nestimate over the agricultural field and ϵ is the error associated with what the\nOpenET,fal\nOpenET estimate would be if the land were fallow.\nIn our study, we seek to estimate mean ET , be it over the entire Central Valley, for\nag\na certain crop type, or for a given county, we seek to calculate the expectation of ET\nag\nover a sample of grid cells. Using Supplementary eq. 5, we can show that our estimate\nof E[ET∗ ] depends not only on E[ET ], but also the expectation of the error terms.\nag ag\nag ag OpenET,ag OpenET,fal ML\nWe show in Supplementary Figures 1-4 that ϵ is unbiased, that is, E[ϵ ] = 0.\nML ML\nOpenET has also been shown to produce unbiased estimates over agricultural lands, so\nE[ϵ ] = 0 (see Table 3 on page 6 of the OpenET Intercomparison and Accuracy\nOpenET,ag\nreport) (OpenET, 2021). We can also assume ϵ is unbiased because though\nOpenET,fal\nOpenET has not specifically been evaluated over fallow fields, it produces unbiased\nestimates over natural shrublands and grasslands with ET values of similar magnitude\nto fallow fields in the Central Valley (OpenET, 2021). As a result, in expectation our\nestimatesofagriculturalETareunbiased, andconsequentlyproduceunbiasedregression\ncoefficients.\nE[ET∗ ] = E[ET ] (6)\nag ag\nWhile our estimates of agricultural ET are unbiased in expectation, the error terms\ndo add variance that is not present in the true agricultural ET. Therefore, any analysis\nthat assesses variation across pixels will reflect inflated variance relative to true agri-\ncultural ET. For example, this has implications for the farming practices and fallowing\nscenarios we conduct (Fig. 3). However, here we estimate that error is responsible\nfor only 11% of the variance in our annual estimates of agricultural ET, suggesting its\ninfluence over key results is limited. Specifically, if we assume that all error terms are\nindependent, we have:\nvar(ET∗ ) = var(ET )+var(ϵ )+var(ϵ )+var(ϵ ) (7)\nag ag OpenET,ag OpenET,fal ML\nwhere var(·) denotes the variance.\nThen, because variance is equal to mean squared error (MSE) when bias is 0, we\nhave:\nvar(ET∗ ) = var(ET )+MSE(ϵ )+MSE(ϵ )+MSE(ϵ ) (8)\nag ag OpenET,ag OpenET,fal ML\nWecalculatetheMSEofourmachinelearningmodelusingourtestset, andOpenET\nprovides root MSE values over croplands and shrublands (which we use to approximate\nerror over fallow lands) (again, see Table 3 on page 6 of the OpenET Intercompari-\nson and Accuracy report) (OpenET, 2021). We find that for estimates of yearly ET∗ ,\nag\nMSE(ϵ )+MSE(ϵ )+MSE(ϵ ) represents only 11% of var(ET∗ ).\nOpenET,ag OpenET,fal ML ag\nThis indicates that the vast majority of the variance we uncover is representative of true\ndifferences in water consumption across fields, as opposed to variability due to model\nerrors.\nbias our estimates of agricultural ET would be if the land cover data we use to identify fallow\nlandswereimprecise. Ifthiswerethecase,wewouldlikelybetrainingournaturally-occurring\nET machine learning model on agricultural fields. This would lead to an overestimation of\nnaturally-occurring ET and therefore an underestimation of agricultural ET.\nTo rule out this potential issue, we test the effect of train a model exclusively on land that\nwas marked fallow not only by the DWR dataset we use for our main results, but also by\nthe Cropland Data Layer (CDL). We find that the results do not change significantly with\nthis more stringent criterion, and report these findings in our supplementary information\n(Supplementary Note 6, Supplementary Fig. 13, 14, and 15) (SI line 142).\nSupplementary Note 6: Results when using training\ndata also marked fallow by the Cropland Data Layer\n(CDL)\nWhen training the naturally-occurring ET model, we rely on the California Department\nof Water Resources (DWR) LandIQ crop type dataset due to its high accuracy. Here,\nwe evaluate the effect of instead training the data only on fallow fields that are marked\nas fallow by both the DWR dataset and the Cropland Data Layer (CDL). Overall, this\nchange does not affect our estimates of naturally-occurring ET: while we estimate 438\nmm per year (394.4-481.7 95% CI) in the main text, we estimate 423.9 mm per year\n(369-478.7 95% CI) here. All general trends and conclusions are robust to this change\n(Supplementary Fig. 14, Fig. 15, Fig. 16).\n14\n1200\n800\n400\nraey/mm\nAgricultural ET Naturally−occurring ET Total ET\nSupplementary Fig. 14. Variations in agricultural ET across and within crop groups\nusing training data also marked fallow by the Cropland Data Layer. Thisistheequivalent\nof Fig. 2 in the main text, but calculated using a naturally-occurring ET model trained on data\nalso marked fallow by the Cropland Data Layer.\na b c % Savings\n0 5 10 15 >20\n100 km\nSupplementary Fig. 15. The percent reduction in agricultural ET driven by various\nmanagement scenarios using training data also marked fallow by the Cropland Data\nLayer. This is the equivalent of Fig. 3 in the main text, but calculated using a naturally-occurring\nET model trained on data also marked fallow by the Cropland Data Layer.\n16\na b\nAgricultural ET (mm/year)\nIrIrrirgigaattioionn eefffficicieiencnyc (y% ()%)\n41\n0 1000 2000\n49 44\nIrrigation (Tg/year) 0 25 50 75 100\n50 38\n42\n1000 2000 38\n36 33\n53\n66\n69\n65\n80\n83 64\n80\n100 km\nSupplementary Fig. 16. Irrigation efficiency across the counties of the Central Valley\nusing training data also marked fallow by the Cropland Data Layer. Thisistheequivalent\nof Fig. 4 in the main text, but calculated using a naturally-occurring ET model trained on data\nalso marked fallow by the Cropland Data Layer.\ncover for naturally-occurring ET are within reason when compared to precipitation within\nthe Central Valley: while CalSIMETAW reports an average precipitation of 554 mm/year,\nour naturally-occurring ET estimates amount to 438 mm/year (394.4-481.7 95% CI) (Orang\net al., 2013). Additionally, we find that 438 mm/year falls within the range of values found\nin the literature studying natural systems in Mediterranean climates, which typically report\nvalues between 300 and 480 mm/year (Aires et al., 2008; Baldocchi et al., 2004; Duell, 1988).\nRobustness of findings against hidden biases. These additional analyses do not\nsuggest that OpenET or other potential sources of error would be inflating our estimates of\nnaturally-occurrign ET. However, out of an abundance of caution, we consider how our find\nings would be affected if our estimates of naturally-occurring ET were nevertheless inflated,\ncausing artificially low estimates of agricultural ET. Because our main findings focus more\non relative differences in agricultural ET, the patterns we uncover would remain unchanged.\nFor example, the ranking in water intensity of crops would not be affected by a uniform\nunderestimation of agricultural ET. Our scenarios all report water savings in percent, and\nwould also remain unchanged. Our estimates of irrigation efficiency would increase, but our\nmain finding surrounding the pronounced north to south gradient would not be affected.\nClarification of annual ET values. While we do not identify any potential sources\nof bias from our methods, we recognize that our estimates of naturally-occurring ET can\nseem high since they are aggregated over the entire year and include naturally-occurring\nET that occurs at times where the water is unavailable to crops. As you note, this is es\npecially problematic in Fig. 2, where the naturally-occurring ET makes up a large fraction\nof total ET across crop types. In order to stave off confusion, we edit the caption of this\nfigure to clarify that not all naturally-occurring ET necessarily translates to crop growth.\nWe additionally remove the statement on the proportion of annual total ET attributable to\nnaturally-occurring ET to avoid readers interpreting it as the fraction of water used by crops\nthat is naturally-occurring.\n18\n800\n400\nraey/mm\nAgricultural ET Naturally−occurring ET Total ET\nFig. 2. Variations in annual agricultural ET across and within crop groups. Mean\nagricultural ET by crop group (green fill and 95% CI) is the average difference between total ET\n(black outline) and naturally-occurring ET (brown fill). All measures are summed across the year,\nleading to naturally-occurring and total ET estimates that include water consumption occurring\noutside of the growing season. While we find significant differences in mean agricultural ET across\ncrop groups, the gray box plots also show a broad spread in agricultural ET within crop groups\n(box plots show 0.5, 0.25. 0.5, 0.75, and 0.95 quantiles).\n2.1.3 The effect of using a coarse PET product\nRegarding your query about the use of coarse PET data in predicting naturally-occurring\nET, we apologize for the lack of clarity surrounding the methodological decisions behind\nour model. We explain these choices below and additionally incorporate them into our\nmanuscript to improve clarity for our readers.\nOur model is composed of multiple predictors, including latitude and longitude, month\nand year, field characteristic information like topography and soil quality, and, as you note,\na coarse PET layer representing climate. Below, we describe the role of each of these layers\nand explain why we feel that the use of such a coarse PET product is justified in this setting.\n1. Latitude and longitude are important variables in our model, enabling it to discern\nfine spatial patterns from the densely distributed fallow fields in our dataset. These\ncoordinates inherently capture climate and land characteristics, as reflected in the\nET values over fallow lands. Thus, the model’s spatial resolution primarily hinges\non these coordinates, similar to how ‘month’ and ‘year’ indicators drive its temporal\nadaptability. The effectiveness of this approach is evident in Fig. 1, which displays\na detailed naturally-occurring ET map without any discontinuities caused by PET or\nother inputs.\n2. We incorporate land characteristics, such as soil quality and topography, to mitigate\n19\npotential biases from differences between fallow and agricultural fields. Farmers may\npreferentiallyfallowfieldswithlessfavorablesoilortopography. Byexplicitlyincluding\nthese variables, the model can adjust for such disparities, ensuring a more accurate and\nunbiased prediction.\n3. Finally, we include PET in our model to enhance its predictive accuracy. While higher\nresolution PET might slightly improve the model, our current R2 is 0.87. Considering\ntheminimalvariationleftunaccountedforandtherelativeerrorsinOpenETestimates,\nfurther refining PET data resolution is unlikely to significantly impact our agricultural\nET estimates.\nWe have amended our methods section to include a more detailed description of our use\nof different variables in predicting naturally-occurring ET (line 374):\nWepredictnaturally-occurringETbasedonlatitudeandlongitude, themonthandyear,\nas well as a broad set of additional variables describing topography (elevation, aspect,\nslope, topographic wetness index), soil quality (California Storie Index), and climate\n(Potential ET). The latitude, longitude, and month and year indicators are included\nto capture the spatial and temporal patterns in ET underlying the densely distributed\nfallow fields in our dataset. However, the inclusion of an indicator variable for each year\nin our model limits our analysis to 2016, 2018, and 2019, as these are the only years for\nwhich we have available land cover data. Nevertheless, given that ’year’ contributes a\nmere 3% to our model’s predictive capability (see Supplementary Fig. 17), it may be\nconsidered non-essential for future research in this area.\nIncluding additional predictors in our model presents two benefits. First, they can\nimprove the model’s predictive power. For example, we find that Potential ET con\ntributes greatly to the model’s final predictions (Supplementary Fig. 17). Other vari\nables are important to include because they can help correct for systematic differences\nbetween the fallow lands used to train our model and the active agricultural lands we\napply the model to. Such differences could arise from farmers selecting lands to be\nfallowed due to their inherently lower productivity, which would negatively bias our\nestimates of counterfactual naturally-occurring ET in locations actively being cropped\ntoday. To assemble these predictor variables, we retrieve topographic information from\nthe USGS National Elevations Database, soil quality information from the California\nStorie Index in the the USDA’s gSSURGO and STATSGO2 datasets, and Potential ET\nfrom the hPET global dataset (Singer et al., 2021).\n2.2 Costs and benefits of different management strategies\n2) Concluding that crop switching and/or land fallowing as a scenario for saving water is not\nnovel, PPIC and others have estimated potential water savings. In fact, PPIC extended their\nanalysis to market and other considerations for the growers (https://www.ppic.org/publi\ncation/exploring-the-potential-for-water-limited-agriculture-in-the-san-joaquin-v\nalley/ , https://www.sciencedirect.com/science/article/pii/S0048969722070632).\nnatives.\nThank you for your valuable feedback regarding our study’s context within the broader\nfield of research on crop switching and land fallowing for water savings. We highly appreciate\nyour insights and references, which have guided us in refining our manuscript. In particular,\nyour suggestions have led us to more explicitly acknowledge related studies and to expand\nour discussion to encompass a broader perspective on the costs and benefits of various man\nagement strategies. Below, we outline how we’ve integrated these considerations into our\nmanuscript, and also highlight the distinctive contributions of our work.\n2.2.1 Our Study’s Unique Contribution\nWhile previous research, including the valuable studies by PPIC and others that you men\ntioned, has indeed explored water savings through crop switching and fallowing, our work\noffers a novel approach. Unlike traditional models based on crop coefficients, our data-driven\nmethodology allows for a broader exploration of factors influencing water consumption.\nThis includes variables like orchard age and farming practices, which typically fall outside the\nscope of conventional models.\nOur primary contribution lies in providing a new, data-driven framework to robustly\nestimate agricultural water consumption. This approach can be used to estimate the water\nsavings potential of different crop switching, fallowing, and farmer’s practices management\nscenarios, as we demonstrate in our study. It can also be used to conduct future analyses\nidentifying opportunities for water saving across California and beyond. We’ve clarified this\nin the introduction of our study, setting up our work in direct relation to previous research\nwhile emphasizing its innovative aspects (line 58):\nTwo methodological challenges prevent agricultural ET from being monitored at scale.\nFirst, simply measuring total ET at scale is a challenge. Second, even when measures\nof ET are available, it is difficult to separate agricultural ET from it (Marston et al.,\n2022; National Academies of Sciences, 2019). To measure total ET, eddy covariance\nflux towers are highly accurate for monitoring ET at a single location (Pastorello et al.,\n2020). However, they are expensive and thus sparse, and they are designed to measure\nET over uniform vegetation, which is not reflective of complex agricultural landscapes\n(Baldocchi, 2003). In the absence of ET measurements, theoretical water demand can\nbe simulated based on climate and crop type (Johansson et al., 2016; Mancosu et al.,\n2016). Although such tools are helpful for water demand planning (Peterson et al.,\n2022), theoretical water demand represents the ET of a crop whose water demands are\nfully met. Therefore, these are likely to overestimate actual ET. Additionally, these\nsimulated estimates exclusively reflect variations in water demand influenced by factors\nincorporated into the model, which are often limited to crop type and climate (\nOrang et al., 2013). Therefore, analyses based on these models are constrained to examining\nthese specific factors, neglecting other critical drivers of ET such as farming practices.\nGiven the scarcity of in situ data and the inherent constraints of simulated estimates,\naccurately gauging total ET at scale remains a significant challenge.\n21\n2.2.2 Enhanced Discussion on Management Strategies\nResponding to your suggestion, we’ve incorporated a discussion on the costs and benefits of\ndifferent management practices, including insights from the studies you referenced. We’ve\noutlinedhowour findings alignwiththese studiesandalsowhere theyoffernew perspectives,\nparticularly in terms of financial viability and market adaptability of less water-intensive\ncrops in California (line 260):\nIn situ assessments of crop-specific water consumption and other variations in agricul\ntural ET enable us to model potential water savings across diverse management scenar\nios. In addition to previously mentioned scenarios based on farming practices, we also\ninvestigate more traditionally studied strategies, such as crop switching and fallowing.\nConsistent with prior studies relying on crop water demand simulations, our research\nsuggests that transitioning to crops with lower water requirements is an effective con\nservation strategy. However, accruing substantial water savings requires embracing less\npopular crops like grains and hay (Peterson et al., 2022). The feasibility of increasing\nproduction of these crops in the Central Valley is uncertain due to high labor and opera\ntional costs (Peterson et al., 2022). Furthermore, any form of crop switching entails ex\npenses related to the adoption of new knowledge, technologies, and market adjustments\n(Hornbeck, 2012; Kurukulasuriya and Mendelsohn, 2008). Consequently, the viability\nof a significant shift towards less water-demanding crops, and its alignment with market\nexpansion, remains uncertain (Peterson et al., 2022). Therefore, our findings support\nthe notion that extensive fallowing or land retirement may be essential to achieve sub\nstantial reduction targets in areas with severe overdrafts (Hanak et al., 2017; Kelsey\net al., 2018). Considering the risk of increased dust from unused land, repurposing\nsuch areas for habitat restoration, flood water capture for groundwater replenishment\n(Guivetchi et al., 2018), solar energy production, or sustainable industrial development\n(Fernandez-Bou et al., 2023) could mitigate the economic impacts of land retirement\nfor both farmers and local communities. Detailed agricultural ET maps like the ones\ngenerated for this study can help determine the scale of land repurposing needed and\nidentify priority areas for such initiatives under different constraints, including existing\nwater rights (Nelson and Burchfield, 2017).\nIn summary, your feedback has been instrumental in enhancing our manuscript. We be\nlieve these revisions provide a clearer understanding of our study’s distinct place in ongoing\nresearch efforts, as well as a more nuanced discussion of factors influencing water manage\nment decisions in California.\n2.3 Irrigation efficiency calculation\n3) The calculation of irrigation efficiency seems flawed. First, I do not know the details of\nthe USGS delivery data and whether it accounts for water moving in and out of counties.\nhave higher irrigation than Merced and Fresno. San Francisco and Marine counties also\njump out. The north south valley split in the irrigation efficiency (Fig. 4, E8) suggests me\nthat USGS data is likely incomplete and this pattern may very well driven by the local and\nstate/federal water deliveries. Or it could be an artifact of your analysis time mismatch.\nAlso, the comparison between theoretical and empirical irrigation efficiency is unfair since\ntheoretical efficiencies do not account for conveyance losses.\nThank you for your critical insights regarding the calculation of irrigation efficiency. We\napologize for any confusion caused by the initial presentation of our data and appreciate the\nopportunity to clarify the USGS data usage in our study.\n2.3.1 The nature of the USGS data.\nThe USGS data estimates irrigation water at the point of use in California, meaning water\ndiverted from a northern county to a southern one is counted in the county where it is used,\nmaking it a suitable source for estimating irrigation efficiency. Specifically, from personal\ncorrespondence with the USGS: “Regarding water diverted for agriculture in California, the\nwithdrawals for crop irrigation are estimated for the point of use. The irrigation withdrawal\npoints of diversion generally are located in the same county as where the water is applied\n(used).” We have modified the methods section and the caption to Fig. 4 to better reflect\nthis,andhavereplacedtheword“diversion”with“withdrawal”forimprovedclaritywherever\napplicable (e.g. lines 53, 207, 315). For instance, in the methods section, we have (line 479):\nTo determine amounts of water withdrawn for irrigation, we retrieve county level irriga\ntion water use data from the USGS National Water Information System. We note that\nthese irrigation amounts are counted at the point of use, rather than the water’s point\nof origin.\n2.3.2 A more intuitive map of irrigation.\nWhile the USGS data does account for intra-county diversions, as you note, many of the\nirrigation numbers displayed in Fig. 4 were counter-intuitive. This is because rather than\ndisplaying irrigation by volume, we were displaying irrigation in mm, which is a measure\nof volume divided by area (area of irrigated land, in our case.) To stave off confusion\nsurrounding this matter, we have altered this figure to display volumetric irrigation as opposed\nto irrigation depth.\nIrrigation efficiency (%)\nIrrigation (Tg/year)\nFig. 4. Irrigation efficiency across the counties of the Central Valley. (a) Irrigation\nefficiency is calculated by dividing agricultural ET (gridded data) by USGS county-level reports\nof irrigation amounts (blue polygons). For the calculation, agricultural ET is averaged to the\ncounty level to match the spatial scale of the irrigation data. Additionally, irrigation is displayed in\nvolumetric units (teragrams), but is divided by county-level cropland area to be in units consistent\nwith agricultural ET prior to the calculation. (b) The resulting county-level irrigation efficiency\nestimates vary widely across the Central Valley, with particularly low efficiencies in the northern\ncounties.\nThis clarification has also been reflected in the methods section (line 488):\nET and Irrigation must be in matching units, either volumetric or depth. We calcu\nlate both in mm per year.\nWe assume all active agricultural lands in the Central Valley are irrigated and cal\nculate the average agricultural ET in mm per year over active agricultural lands in each\ncounty. To also retrieve average irrigation amount across irrigated lands in a county in\nmm per year, we divide the volume of irrigation water by the average area of irrigated\nland in each county.\n2.3.3 Temporal mismatch effects.\nRegarding whether the north-south gradient in irrigation efficiency could be driven by the\ntemporal mismatch in our data, Supplementary Fig. 9 suggests that this is unlikely.\n24\n100\n80\n60\nS\nla\na\nn\nco\nra\nS\ne\na\nn\nn\nto Joaqu\nS\nin tanislaus Merced Madera Fresno Tulare Kings Kern\nCounty\n)%( \nycneiciffe\nnoitagirrI\nAgricultural ET year 2016 2018 2019\nIrrigation year 2010 2015\nAverage over all years\nSupplementary Fig. 9. Uncertainty in irrigation efficiency estimates. To calculate\nirrigation efficiency, we divide agricultural ET by irrigation amount, but the available data do\nnot overlap in years over which they are available. Counties are ordered by latitude, with more\nnorthern counties on the left. The average irrigation efficiency is shown as a red x, but we also\nassesstheeffectofusingdifferentyearsinthenumeratoranddenominatorbycombiningalldifferent\npermutations of agricultural ET (shape) and irrigation (color) data. We note that 2016 and 2015\nwere drought years, while 2018, 2019, and 2010 were wetter. We do find substantial spreads in\nirrigation efficiency driven by the year of irrigation data, especially in some southern counties.\nHowever, most of the spread is driven from combining wet and dry years together, suggesting that\nan average may successfully decrease much of the variability.\n2.3.4 Conveyance losses in theoretical efficiencies.\nFinally, we apologize for the confusion on the point about whether theoretical efficiencies\naccount for conveyance losses. We note in the supplementary information, where we carry\nout this comparison, that we do use a standard 85% conveyance loss rate to calculate theo\nretical efficiencies (SI line 64).\nWe set conveyance efficiency to .85, management efficiency to .95, and application effi\nciency to .60 for flood irrigation, .75 for sprinkler irrigation, and .95 for drip irrigation.\nWe believe these amendments enhance clarity in our manuscript with respect to the ir\nrigation efficiency calculations. Your expertise in the dynamics of water movement in the\nCentral Valley has been invaluable in refining our approach.\n25\n2.4 Improving the discussion section\n4) Discussion section is very superficial and can be improved by referencing prior work on\nirrigation efficiency paradox, complexities with land fallowing and crop switching, market\npressure, water rights, land retirement for other beneficial use like solar, wildlife habitat,\nand even FloodMAR.\nThank you for suggesting these pertinent topics, which have greatly strengthened our\ndiscussion. As mentioned above, we have amended our discussion section to reference prior\nwork concerning the complexities with land fallowing (line 272-281) and crop switching (lines\n265-272), market pressure (lines 270, 272), water rights (line 281), land retirement for other\nbeneficial use like solar (line 276), wildlife habitat (line 275), and FloodMAR (line276).\nAdditionally, we include a new paragraph detailing the nuances of the irrigation efficiency\nparadox (lines 282-297). The updated text now reads (line 260):\nIn situ assessments of crop-specific water consumption and other variations in agricul\ntural ET enable us to model potential water savings across diverse management scenar\nios. In addition to previously mentioned scenarios based on farming practices, we also\ninvestigate more traditionally studied strategies, such as crop switching and fallowing.\nConsistent with prior studies relying on crop water demand simulations, our research\nsuggests that transitioning to crops with lower water requirements is an effective con\nservation strategy. However, accruing substantial water savings requires embracing less\npopular crops like grains and hay (Peterson et al., 2022). The feasibility of increasing\nproduction of these crops in the Central Valley is questionable due to high labor and\noperational costs (Peterson et al., 2022). Furthermore, any form of crop switching entails\nexpenses related to the adoption of new knowledge, technologies, and market adjustments\n(Hornbeck, 2012; Kurukulasuriya and Mendelsohn, 2008). Consequently, the viability\nof a significant shift towards less water-demanding crops, and its alignment with market\nexpansion, remains uncertain (Peterson et al., 2022). Therefore, our findings support\nthe notion that extensive fallowing or land retirement may be essential to achieve sub\nstantial reduction targets in areas with severe overdrafts (Hanak et al., 2017; Kelsey\net al., 2018). Considering the risk of increased dust from unused land, repurposing\nsuch areas for habitat restoration, flood water capture for groundwater replenishment\n(Guivetchi et al., 2018), solar energy production, or sustainable indus\ntrial development (Fernandez-Bou et al., 2023) could mitigate the economic impacts of\nland retirement for both farmers and local communities. Detailed agricultural ET maps\nlike the ones generated for this study can help determine the scale of land repurposing\nneeded and identify priority areas for such initiatives under different constraints such\nas existing water rights (Nelson and Burchfield, 2017).\nFinally, we find a significant opportunity to improve irrigation efficiency, especially\nin the northern part of the Central Valley where we find lower irrigation efficiency than\npreviouslyexpected(SupplementaryNote2, SupplementaryFig. 7). Thissuggeststhat\nfarmers could reduce irrigation water without affecting agricultural ET, and therefore\n26\ncrop growth. While this aligns with management plans targeting irrigation efficiency\nimprovements (Bruno et al., 2022), improving irrigation efficiency would not necessarily\ntranslate to water savings at the watershed scale (Grafton et al., 2018). This is be\ncause non-consumed water does not necessarily leave the watershed, and may return to\na groundwater reservoir or surface water body and be reused at a later date. On the\nother hand, the evapotranspired water that makes up the ”efficient fraction of irrigation\nefficiency does leave the watershed entirely (Grafton et al., 2018). Therefore, increasing\nirrigation efficiency could paradoxically decrease water availability if it is not accom\npanied by a corresponding decrease in irrigation water withdrawals. Managers could\nleverage mapped agricultural ET estimates to ensure that water consumption does not\nincrease as irrigation efficiency improvements are rolled out.\n2.5 The use of ‘empirical’ vs. ‘modeled’\n5) Lastly, I am intrigued by your use of the word empirical vs. modeled. Example: Agricul\nture ET derived from CalSIMETAW is being referred as modeled but same data from OpenET\nis empirical. Even for the sake of clarity, it would be better to refer the data by source.\nThank you for pointing out our confusing use of the terms ‘empirical’ and ‘modeled’. In\norder to distinguish between the two kinds of estimates which, as you point out, are both\nmodeled, we ensure CalSIMETAW is referred to by name in both the discussion (lines\n221, 226) and supplementary information (SI line 19). We additionally replace the term “mod\neled”with“simulated”whengenerallyreferringtoestimatescalculatedusingcrop-coefficient\nbased models in order to distinguish from our in situ estimates. We have copied the revised\nfigure from the supplementary information below.\n1000\n500\n0\n0 500 1000 0 500 1000\nEmpirical Agricultural ET (mm/year)\n)raey/mm(\nTE\nlarutlucirgA\nWATEMISlaC\nCrop type\nAlfalfa & alfalfa mixtures\nAlmond & Pistacios\nBeans (dry)\nCitrus and subtropical\nCorn,Sorghum or Sudan\nCotton\nDeciduous fruits and nuts\nField crops\nGrain and hay crops\nMelons\nOnions & garlic\nPasture\nPotato or Sweet potato\nRice\nSafflower\nTomatoes (processing)\nTruck, nursery, and berry crops\nVineyards\nSupplementary Fig. 6. Simulated vs. empirical agricultural ET. Simulated agricultural\nET is calculated using theoretical crop water demand from the CalSIMETAW model and by sub\ntracting either (A) naturally-occurring ET estimated using machine learning or (B) precipitation.\nEach point represents the agricultural ET for a crop in a specific county in the California Central\nValley.\n2.6 Specific Comments\na) More information should be provided on how various variables are influencing ETnat pre\ndiction using ML model. It was briefly mentioned in discussion section (line 240-242) but is\nnot well-defined.\nThank you for your valuable suggestion regarding the need for more detailed information\non the influence of various variables in our machine learning model for predicting naturally\noccurring ET. Acknowledging the importance of this detail and also in response to your first\ncomment, we have revised the methods section to offer a clearer and more comprehensive\nexplanation of the role each variable plays in the model. We reproduce the corresponding\nexcerpt once more below (line 374):\nWepredictnaturally-occurringETbasedonlatitudeandlongitude, themonthandyear,\nas well as a broad set of additional variables describing topography (elevation, aspect,\nslope, topographic wetness index), soil quality (California Storie Index), and climate\n(Potential ET). The latitude, longitude, and month and year indicators are included\nto capture the spatial and temporal patterns in ET underlying the densely distributed\nfallow fields in our dataset. However, the inclusion of an indicator variable for each year\nin our model limits our analysis to 2016, 2018, and 2019, as these are the only years for\nwhich we have available land cover data. Nevertheless, given that ’year’ contributes a\nmere 3% to our model’s predictive capability (see Supplementary Fig. 17), it may be\nconsidered non-essential for future research in this area.\nIncluding additional predictors in our model presents two benefits. First, they can\n28\nimprove the model’s predictive power. For example, we find that Potential ET con\ntributes greatly to the model’s final predictions (Supplementary Fig. 17). Other vari\nables are important to include because they can help correct for systematic differences\nbetween the fallow lands used to train our model and the active agricultural lands we\napply the model to. Such differences could arise from farmers selecting lands to be\nfallowed due to their inherently lower productivity, which would negatively bias our\nestimates of counterfactual naturally-occurring ET in locations actively being cropped\ntoday. To assemble these predictor variables, we retrieve topographic information from\nthe USGS National Elevations Database, soil quality information from the California\nStorie Index in the the USDA’s gSSURGO and STATSGO2 datasets, and Potential ET\nfrom the hPET global dataset (Singer et al., 2021).\nFurthermore, we have included a supplementary figure (Supplementary Fig. 17) to visu\nally illustrate the significance of different variables in the model’s predictions. We hope these\nenhancements address your concerns and enrich your and future readers’ understanding of\nour methodology.\nSupplementary Fig. 17. The contribution of each variable included in the gradient\nboosting model on its predictions of naturally-occurring ET.\n29\nb) The spatial scale of all analyses differs from each other. ETnat was carried out at 70m\nresolution, variation in ET at Valley, water saving potential at groundwater sub-basin and\nirrigation efficiency at county level. More information on how spatial aggregation was done\nfor the ET components from 70m to county/sub-basins scales and reasons for choosing these\nscales will be helpful.\nWe apologize for the lack of clarity surrounding the scales of analysis. Below, we explain\nthe choices made surrounding the different scales of analysis and describe changes we have\nmade to the analysis to increase clarity for future readers.\nAs you mention, we first estimate naturally-occurring ET, and therefore agricultural ET,\nat sub-field scales to allow us to capture fine scale variation across the Central Valley. We\nthen exploit this sub-field scale dataset to investigate Valley-wide drivers in agricultural ET\nvariation, such as crop type.\nWe have included a new sentence in the results section explaining this (line 130):\nWe leverage the significant variability in agricultural ET observed across the Central\nValley (Fig. 1) to analyze the factors driving these variations.\nFollowing the calculation of these high level statistics, we conduct different simulations\nwherein we mimic various management interventions by manipulating sub-field scale agri\ncultural ET to investigate their overall water saving potential. These manipulations are still\ncarried out at the sub-field scale, and therefore necessitate the high-resolution agricultural\nET dataset (i.e. we judge whether to simulate a change in agricultural ET based on the\nproperties and agricultural ET of each separate pixel). However, when carrying out these\nsimulations, we calculate crop-specific agricultural ET and within-crop variation at the level\nof groundwater sub-basins in order to avoid comparing agricultural ET across different cli\nmatological contexts. In other words, we avoid prescribing that a farmer in a drier area\ngrow crops with the same agricultural ET as a farmer in a wetter area. The percent water\nsaved by such interventions could be displayed at any number of different spatial scales,\nbut we choose report savings at the groundwater sub-basin level since the interpretation of\nwater savings at the field scale for these scenarios would make less sense from a management\nperspective, which tends to operate at landscape scales. For example, mapping the water\nsaved by fallowing 5% of plots with the highest level of water consumption would render a\nmap of points that would be difficult to interpret, as opposed to the average water it may\nsave in a particular sub-basin with particular water saving goals.\nWe edit our results section to better reflect this (line 156):\nThe variability of agricultural ET both within and between crops allows us to evaluate\nthe water saving-potential of different management strategies. Here, we compare the\neffect of three scenarios on reducing agricultural ET in groundwater sub-basins across\nthe Central Valley:\n1. Crop switching: Substitute high-ET crops for the median water-consuming crop\n(eq. 6).\n30\n2. Farming practices: Keep the same spatial allocation of crops, but reduce agricul\ntural ET of high consumers to the median, crop-specific, consumption level (eq.\n7).\n3. Fallowing: Fallow the 5% of lands with the highest estimated agricultural ET (eq.\n8).\nBecauseitwouldnotmakesensetoprescribefarmersindifferentcontextstoconsume\nsimilar amounts of water, we calculate crop-specific agricultural ET and within-crop\nvariation at the level of groundwater sub-basins. We additionally control for differences\nin climate, topography, and soil type before conducting the scenarios. To account for\nthe effects of orchard age, we remove orchards that have been bearing fruit for 5 years\nor less or that are in their last year of production (Supplementary Fig. 7).\nFinally, we calculate irrigation efficiency at the county level simply because higher\nresolution data on irrigation amounts are not available. We simply aggregate our field-scale\nagricultural ET measurements by taking the mean over the entire county, and divide this\nby mean irrigation in a given county. We note that since both estimates are in mm per\nyear, they already inherently account for the amount of land irrigated in a given county. As\nmentioned in our response to your third comment, we have edited the caption to the figure\ndisplaying irrigation efficiency to better reflect this and have revisited the methods section\nfor improved clarity on this matter (Fig. 4):\nIrrigation efficiency (%)\nIrrigation (Tg/year)\nFig. 4. Irrigation efficiency across the counties of the Central Valley. (a) Irrigation\nefficiency is calculated by dividing agricultural ET (gridded data) by USGS county-level reports\nof irrigation amounts (blue polygons). For the calculation, agricultural ET is averaged to the\ncounty level to match the spatial scale of the irrigation data. Additionally, irrigation is displayed in\nvolumetric units (teragrams), but is divided by county-level cropland area to be in units consistent\nwith agricultural ET prior to the calculation. (b) The resulting county-level irrigation efficiency\nestimates vary widely across the Central Valley, with particularly low efficiencies in the northern\ncounties.\nWe have also clarified this in the methods section (line 487):\nET and Irrigation must be in matching units, either volumetric or depth. We calcu\nlate both in mm per year.\nWe assume all active agricultural lands in the Central Valley are irrigated and cal\nculate the average agricultural ET in mm per year over active agricultural lands in each\ncounty. To also retrieve average irrigation amount across irrigated lands in a county in\nmm per year, we divide the volume of irrigation water by the average area of irrigated\nland in each county.\nc) Add details on crop groupings shown in Fig. 2 in the supplementary material.\nWe have included the following passage in the supplementary material to provide a com\nprehensive explanation of the groupings in Fig. 2 (Supplementary Note 7, SI line 151).\nThe crop groups used to make Fig. 2 follow the groupings from the Department of\nWater Resources LandIQ dataset which we use to designate crop type in our analysis.\nMore information on these groups can be found in the metadata for these data, and are\nalso described here:\n1. Grain and hay crops include barley, wheat, oats, miscellaneous grain and hay, and\nmixed grain and hay.\n2. Rice includes rice and wild rice.\n3. Field crops include cotton, safflower, flax, hops, sugar beets, corn (field & sweet),\ngrain sorghum, sudan, castor beans, beans (dry), miscellaneous field, sunflowers,\nhybrid sorghum/sudan, millet, sugar cane, and corn.\n4. Pasture includes alfalfa & alfalfa mixtures, clover, mixed pasture, native pasture,\ninducedhighwatertablenativepasture,miscellaneousgrasses,turffarms,bermuda\ngrass, rye grass, and klein grass.\n5. Truck, nursery & berry crops include artichokes, asparagus, beans (green), cole\ncrops,carrots,celery,lettuce(alltypes),melons,squash,andcucumbers(alltypes),\nonions & garlic, peas, potatoes, sweet potatoes, spinach, tomatoes (processing),\nflowers, nursery&Christmastreefarms, mixed(fourormore), miscellaneoustruck,\nbush berries, strawberries, peppers (chili, bell, etc.), broccoli, cabbage, cauliflower,\nbrussels sprouts, tomatoes (market), greenhouse, blueberries, Asian leafy vegeta\nbles, lettuce or leafy greens, and potato or sweet potato.\n6. Deciduousfruitsandnutsincludeapples, apricots, cherries, peachesandnectarines,\npears, plums, prunes, figs, miscellaneous deciduous, mixed deciduous, almonds,\nwalnuts, pistachios, pomegranates, and plums, prunes or apricots.\n7. Citrus and subtropical include grapefruit, lemons, oranges, dates, avocados, olives,\nmiscellaneous subtropical fruit, kiwis, jojoba, eucalyptus, and mixed subtropical\nfruits.\n8. Vineyards include table grapes, wine grapes, and raisin grapes.\nd) Fig. A4 can be represented better with contrasting colors, hard to see the overlap. Also,\nplots with combination of bar and box plots can be improved with different color choices and\nadding gridlines.\nWe have implemented your suggestions surrounding the readability and accessibility of\nour figures in full.\nFirst, we have increased the brightness of the colors in the bar plot/box plot figures, and\nadded gridlines.\n33\n800\n400\nraey/mm\nAgricultural ET Naturally−occurring ET Total ET\nFig. 2. Variations in annual agricultural ET across and within crop groups. Mean\nagricultural ET by crop group (green fill and 95% CI) is the average difference between total ET\n(black outline) and naturally-occurring ET (brown fill). All measures are summed across the year,\nleading to naturally-occurring and total ET estimates that include water consumption occurring\noutside of the growing season (Supplementary Figure 6). While we find significant differences in\nmeanagriculturalETacrosscropgroups,thegrayboxplotsalsoshowabroadspreadinagricultural\nET within crop groups (box plots show 0.5, 0.25. 0.5, 0.75, and 0.95 quantiles).\nAdditionally, we have changed the colors in Figure A4 to enhance contrast. How\never, we note that Figure A4 shows lines that are closely overlapping, highlighting the\nclose matchup between our machine learning model predictions and OpenET estimates of\nnaturally-occurring ET over fallow lands but also making them inherently difficult to distin\nguish.\n34\n60\n50\n40\n30\n20\n10\n2.5 5.0 7.5 10.0 12.5\nMonth\n)htnom/mm(\nTE\nObserved ET Predicted ET\nSupplementary Fig. 4. Temporal error structure of ET predictions over fallow lands.\nThe average ET observed and predicted over fallow lands are similar throughout the year. In other\nwords, the model is not more likely to be positively or negatively biased based on the month it is\npredicting over.\ne) The title seems misleading as the field-scale estimates are based of remotely sensed and\nother data of different scales, aggregated for the suggested water saving strategies at coun\nty/groundwater sub-basin scales. You may want to consider revising it.\nThank you for your insights surrounding the accuracy and descriptiveness of our title.\nAfter much deliberation, we have elected to keep the same title as we feel that it captures\nthe two main contributions of our work: that we are able to estimate agricultural ET at the\nfield scale and that these fine resolution estimates allow for various analyses that may be\nused for water management.\nAs we mention above in addressing your point on the different scales of our analyses,\nour crop switching, fallowing, and farmer practices management scenarios do not aggregate\nthe field-scale data to the groundwater sub-basin scale. On the contrary, all of the fine\nspatial variation we uncover when calculating our high-resolution agricultural ET estimates\nis necessary and utilized when performing these analyses, as can be seen in equations 5-7.\nThe results are reported at a coarser spatial scale (the groundwater sub-basin) for ease of\ninterpretation.\nHowever, we do average our field-scale estimates of crop water consumption to the county\nlevel when calculating irrigation efficiency since higher resolution information on water ac\ncessed for irrigation is not available. This, however, does not change the potential of our\n35\nfield scale estimates to be used to do finer scale analyses on irrigation efficiency in the future\nas higher resolution water use data becomes available.\nWe therefore choose to retain our title, “Field-scale crop water consumption estimates\nreveal potential water savings in California agriculture,” but thank you for the opportunity\nto revisit it and consider its clarity and accuracy.\nf) Some sentences are lengthy and can be further simplified in the Introduction section. For\nexample: lines 44-47, 68-71, 51-53.\nWe have carefully read through and revised the lines you mentioned as well as several\nothers. Some examples are included below (lines 46, 52, 58, 64, 75, 79).\nAgricultural ET, or the increase in ET that irrigated agriculture brings, is therefore a\ncritical measure in that it represents the amount of water that is actually “consumed”\nby agriculture. This water leaves the watershed entirely as it is evaporated from the soil\nand transpired by crops (Hoekstra and Mekonnen, 2012).\nWhen compared to total irrigation amounts, agricultural ET can also be used to high\nlight the fraction of irrigation withdrawals that do not effectively result in consumptive\nuse (Puy et al., 2022). While this water stays in the system and therefore may continue\nto provide beneficial uses (Grafton et al., 2018), it also does not achieve its original\npurpose of contributing to crop growth. Therefore, agricultural ET can help calculate\nirrigation efficiency and identify unnecessary irrigation water withdrawals from surface\nor groundwater reservoirs (Puy et al., 2022).\nTwo methodological challenges prevent agricultural ET from being monitored at scale.\nFirst, simply measuring total ET at scale is a challenge. Second, even when measures\nof ET are available, it is difficult to separate agricultural ET from it (Marston et al.,\n2022; National Academies of Sciences, 2019).\nIn the absence of ET measurements, theoretical water demand can be simulated based\non climate and crop type (Johansson et al., 2016; Mancosu et al., 2016). Although such\ntools are helpful for water demand planning (Peterson et al., 2022), theoretical water\ndemand represents the ET of a crop whose water demands are fully met. Therefore,\nthese are likely to overestimate actual ET.\nAddressing the second challenge, even when accurate ET measurements are available,\nisolating agricultural ET from total ET is difficult. This step is key for water manage\nment, sincetheresultingmetricrepresentstheincreaseinETattributabletoagriculture.\nMost often, this naturally-occurring ET is assumed to be equal to precipitation (Man\ncosu et al., 2016; Snyder et al., 2012). However, there may be temporal lags between\nprecipitation and resulting ET, some precipitation may not result in ET at all, and ET\n36\nmay also be supplemented by other sources of water, such as near surface groundwater.\ng) Given how the ML model was developed, should you be still calling the ET over fallow\nland as natural? Don’t you think fallow ET would be more appropriate?\nThank you for your observation regarding the terminology we used to denominate ET\nover fallow land. Choosing between ‘fallow ET’ and ‘naturally-occurring ET’ was a nuanced\ndecision in our study, carefully considered to best reflect our research framework. We ulti\nmately opted for ‘naturally-occurring ET’ as it aligns more coherently with the overarching\nconceptual approach of our work, rather than emphasizing a specific methodological aspect.\nIn line with this overarching framework, we write in the methods section (line 346):\nWe note that another, theoretically distinct counterfactual could be constructed to rep\nresent ET : ET if the land were undisturbed, natural land rather than fallow. We\nnat\nelect to simulate ET over fallow lands since it allows us to predict the potential water\nsavings from fallowing, which has a management relevant interpretation.\n2.7 Line-specific comments\nL46: evapotranspiration is “both” evaporation and transpiration that often occur simultane\nously.\nWe have edited to more clearly portray evapotranspiration (line 46):\nAgricultural ET, or the increase in ET that irrigated agriculture brings, is therefore a\ncritical measure in that it represents the amount of water that is actually “consumed”\nby agriculture. This water leaves the watershed entirely as it is evaporated from the soil\nand transpired by crops (Hoekstra and Mekonnen, 2012).\nL111: “R2”\nThank you, this is fixed (line 124).\nL124: More information on how 34% was calculated will be helpful for readers.\nThank you for pointing out the need for clarity on how we calculated the 34% figure. We\nhave added detailed information about this calculation in the methods section to enhance\nthe manuscript’s transparency and aid reader understanding (line 443):\nIn addition to using regression to calculate point estimates and confidence intervals,\nregression allows us to calculate the proportion of variation that is explained by a set\nof variables. This is because the R2 corresponds to the fraction of variation explained\nby the regression. We use this to assess the % variation explained by crop type, using\n37\neq. 4 [...]\nL125: “water intensive crops”\nWe have amended the language of this line for improved clarity (line 134):\nIn Fig. 2, we show the water intensity of different crop groups (Supplementary Note 7).\nL128: reference to “(Figure A1)” seems wrong.\nThank you, this is fixed (line 137).\nL207-208: You can write that “.... 30% deficit irrigation for some crops (wheat, maize,\ncotton etc.)” as the referred studies are only for limited crops.\nWe have removed this statement from the paper entirely, and replaced it with a more\nnuanced discussion of different kinds of farmer practices (line 236):\nHigh-resolution, empirical estimates of agricultural ET additionally open up the pos\nsibility to investigate differences in agricultural ET past what is attributable to crop\ntype, which we find only accounts for 34% of the variation. These within-crop varia\ntions can be substantial for many crops: the difference in agricultural ET of a pistachio\nfield from the 75th to the 25th percentile is the same as the water that could be saved\nfrom fallowing an alfalfa field. Such broad variability is consistent with findings from\nstudies comparing total ET across crops during the growing season Schauer and Senay\n(2019); Wong et al. (2021). Even after adjusting for variability attributable to field\ncharacteristics, orchard age, and climate, we find that these variations could translate\nto substantial water savings without requiring a change in crop type. Unfortunately,\na lack of high-resolution data on field-scale farming practices and yields inhibits fur\nther analysis of specific practices driving agricultural ET variation and their economic\nimplications. However, prior literature from experimental plots or particular locations\nsuggests that mulching (Kader et al., 2019), conservation tillage (Mitchell et al., 2012),\ndeficit irrigation (English and Raja, 1996; Goldhamer, 1999; Rudnick et al., 2019), and\nimproved irrigation scheduling and technologies (Jovanovic et al., 2020) all have poten\ntial to limit agricultural ET. These practices may prove advantageous relative to costly\nstrategies like crop-switching or fallowing, though more detailed cost-benefit analyses\nare necessary to determine the suitability of various interventions in specific contexts\n(Mitchell et al., 2016). Such water saving farming practices are not mentioned in the\nplans drafted by SGMA water managers (Bruno et al., 2022). This is possibly due to\na lack of conclusive research on the potential of such strategies to effectively decrease\nagricultural ET without significant effects on yield or operation cost. Spatial data on\nthe use of these different practices would allow researchers to take full advantage of our\nhigh-resolution agricultural ET estimates and study their water-saving and economic\n38\nbenefits.\nL211-214: The point made here makes sense, but not sure how relevant is the precipitation\nfactor for Central valley agriculture as the peak demand and rainfall occurs are different\ntimes of the year.\nWe appreciate your observation regarding the relevance of the precipitation counterfac\ntual given the mismatch in seasonality with the crop growing season. It appears our initial\nexplanation did not clearly convey our methodology. We actually accounted for precipita\ntion at the monthly level and only included amounts not exceeding crop water demand. To\nclarify this, we’ve amended our explanation for why the increased bias occurs (line 226).\nWe find that the bias between our estimates and CalSIMETAW estimates increases\nwhen precipitation, rather than naturally-occurring ET, is used to represent baseline\namounts of available water. This can be explained by the seasonal mismatch between\nprecipitation and naturally-occurring ET: although annually there is more precipitation\nthan naturally-occurring ET, precipitation mainly occurs in the winter when it is un\navailable to most crops. Because using precipitation as a proxy for naturally-occurring\nET does not account for moisture that remains in the soil by the time the growing\nseason begins, this inflates simulated estimates of crop water demand.\nAppendix C: Provide citation for CalSIMETAW\nThank you, this is fixed (line 21).\n39\nReview of manuscript titled: “Field-scale crop water consumption estimates reveal potential\nwater savings in California agriculture”\nSummary: Authors present a study where through satellite remote sensing and machine\nlearning they can differentiate between naturally-occurring evapotranspiration (ET) and agri\ncultural ET (the increase in ET induced by irrigation) to better quantify agricultural water\nuse in the state of California; and by doing so offer potential solutions to decrease agri\ncultural water use. This study is timely and relevant and takes advantage of the emerg\ning technologies and approaches now available to earth system scientists. The scope of the\nmanuscript is ambitious, but the authors have done a good job of explaining the steps and\napproaches/assumptions taken. I do believe the approach taken is novel (the subtraction of\nthe naturally-occurring ET) but am not entirely convinced of its validity. Other studies have\nquantified crop water use over the state using ET (Wong et al., 2021; Schauer et al., 2019;\namong others), but are not discussed in the manuscript. This may simply be due to the\nwriting constraints of Nature Communications, but I still believe it is important to bring up.\nOverall, I think this is a wonderful and important study. But I do have some general\nconcerns that I think should be addressed prior to publication.\n3.1 Estimating agricultural ET\nConcern #1. This is a novel approach. Quantifying agricultural ET by determining the ET\nthat would be naturally-occurring in that location should the crop (and applied irrigation)\nnot be present and subtracting it from the actual ET observed via OpenET. That said, I’m\nhesitant to agree that the definition of agricultural ET is this difference. My hesitation stems\nfrom the results stating that, on average/in general, 46% of total ET is defined as naturally\noccurring and 53% of total ET is defined as agricultural ET. Intuitively this seems very high\nfor ‘naturally-occurring’. If I were in an almond orchard in September, I would not expect\nthat 46% of the total ET coming off the field would be naturally-occurring and only 53%\nwould be because of the almond tree and the applied irrigation. I know you have broken these\npercentages down into crop categories, but they still seem high. I’m also aware that there are\nlikely some time dependency factors that play a role. That said, I’m curious if you have any\ncitations confirming this is a viable assumption or if you have ground data that can be used to\nvalidate your assumption. Also, because the idea behind the study is to quantify agricultural\nET, should there be more of a month-by-month analysis or growing season analysis? I think\nit is important prior to scaling an approach like this to the entire state and making claims\nabout water use that a proof of concept be completed, either on the ground or found in the\nliterature.\nWe appreciate your comment which has prompted us to think more deeply about our\ndefinition of agricultural ET and to inquire into the accuracy of our estimates of naturally\noccurring ET. Below, we break our answer into several sections. First, we consider our\ndefinition of agricultural ET and its appropriateness in the context of our study. We com\npare it to the methodology employed by Wong et al. (2021) and Schauer and Senay (2019)\n40\nthat you mention above. We argue that our definition is relevant for water management\napplications. Second, through a month-by-month analysis, we demonstrate how this defini\ntion allows us to calculate annual measures of agricultural ET without relying on external\ninformation about the growing season. Third, in response to your comment concerning\nthe seemingly high estimates of naturally-occurring ET, we recognize that reporting annual\nvalues of naturally-occurring ET can be confusing since readers may be expecting values\nconfined to the growing season. We therefore adjust our language to improve clarity sur\nrounding this point. We additionally investigate possible sources of error in our estimates of\nnaturally-occurring ET to ensure their accuracy, but do not find evidence of bias.\n3.1.1 Our definition of agricultural ET\nWe define agricultural ET as the increase in ET that agriculture brings. This definition\nrecognizes that not all ET over agricultural lands, during the growing season or otherwise,\ncan necessarily be attributed to agriculture. This definition is particularly useful from a\nmanagement perspective, since it denotes the decrease in ET, or water savings, that one\nmight expect if agriculture were to cease. Defining agricultural ET in this way implies a\ncounterfactual ET that would be present regardless of agriculture, which we call naturally\noccurring ET.\nOur conceptualization of agricultural ET differs from the approach of attributing all\nET during the growing season over an agricultural field solely to agriculture (Schauer and\nSenay, 2019; Wong et al., 2021). We believe our definition offers practical advantages for\nmanagement,especiallyintermsofidentifyingpotentialwatersavingsifagricultureisceased.\nMoreover, it addresses the transitional phase between growing seasons, avoiding the ambigu\nity of classifying ET as agricultural or not in early growing seasons when crops are young and\nunlikely to be the sole drivers of ET. By considering the incremental impact of agriculture on\nET throughout the growing season, our definition remains applicable throughout the entire\nyear, a topic we will explore in the following section.\nWe edit our manuscript to further clarify our definition of agricultural ET. In the intro\nduction, we have (line 75):\n[...] even when accurate ET measurements are available, isolating agricultural ET from\ntotal ET is difficult. This step is key for water management, since the resulting metric\nrepresents the increase in ET attributable to agriculture. To do so, one must estimate\nthe naturally-occurring ET that would occur in the absence of irrigated agriculture (for\nexample, if the land were left fallow).\nAdditionally, in the methods section, we note (line 338):\nWe define agricultural ET as the difference between total ET over an agricultural parcel\nand the ET that would have been, had that parcel been fallow land instead (eq. 1).\nThis definition recognizes that not all ET over agricultural lands, during the growing\nseason or otherwise, can necessarily be attributed to agriculture. Such a definition is\n41\nparticularly useful from a management perspective, since it denotes the decrease in ET,\nor water savings, that one might expect if agriculture were to cease.\nET = ET −ET (1)\nag tot nat\nwhere ET is agricultural ET, ET is the total ET over an agricultural parcel, and\nag tot\nET is the counterfactual ET that would occur naturally, were the same land fallow.\nnat\nWe also edit the manuscript to more effectively put our study in the context of previous\nwork such as that of Wong et al. (2021) and Schauer and Senay (2019). In the introduction,\nwe write (line 87):\nRecent advances in the remote sensing of ET unlock new avenues for research into agri\ncultural water consumption. Numerous algorithms for estimating ET using land surface\ntemperature and other remote sensing inputs have been validated specifically for use in\nagricultural settings (Anderson et al., 2021; Melton et al., 2022). These advancements\nhave allowed researchers to empirically study total ET in agricultural settings (Schauer\nand Senay, 2019; Wong et al., 2021). However, despite high-resolution maps of ET\nin agricultural landscapes (Anderson et al., 2018; Wong et al., 2021), the challenge of\nisolating agricultural ET from total ET has been limited to studies in extremely arid\nregions where irrigation is the only source of water available to plants (Al-Gaadi et al.,\n2022).\nAdditionally, in the discussion, we write (line 236):\nHigh-resolution, empirical estimates of agricultural ET additionally open up the pos\nsibility to investigate differences in agricultural ET past what is attributable to crop\ntype, which we find only accounts for 34% of the variation. These within-crop variations\ncan be substantial for many crops: the difference in agricultural ET of a pistachio field\nfrom the 75th to the 25th percentile is the same as the water that could be saved from\nfallowing an alfalfa field. Such broad variability is consistent with findings from studies\ncomparing total ET across crops during the growing season (Schauer and Senay, 2019;\nWong et al., 2021).\n3.1.2 Annual estimates of agricultural ET\nIn addition to its management relevance, another benefit of our definition of agricultural ET\nis that summing over the entire year retrieves annual agricultural ET without requiring any\nassumptions about when the growing season starts or ends. Fig. R1 shows the seasonality of\nagricultural ET. This figure demonstrates how in the winter, outside of the growing season\nof the vast majority of crops in the Central Valley, naturally-occurring ET and total ET are\nnearly identical, leading to a near-zero agricultural ET. As a result, we use annual estimates\nof agricultural ET in our analyses.\n42\n150\n100\n50\n0\n1 2 3 4 5 6 7 8 9 10 11 12\nMonth\nhtnom/mm\nAgricultural ET Natural ET Total ET\nFig. R1. TimeseriesofagriculturalETintheCaliforniaCentralValley. TotalET(black)\ncan be partitioned into agricultural ET (green), and naturally-occurring ET (brown). Agricultural\nET peaks in the summer, which constitutes the growing season for most crops grown in the Central\nValley, but diminishes to nearly zero in the winter when few crops are grown.\n3.1.3 Improvingclarity surroundingtheannualpresentationofnaturally-occurring\nET\nWe recognize that our estimates of naturally-occurring ET can seem high because they are\naggregated over the entire year and include naturally-occurring ET that occurs at times\nwhere the water is unavailable to crops. In order to stave off confusion and because they\nhave little management relevance, we have removed statements comparing agricultural ET\nand naturally-occurring ET amounts. We additionally edit the caption of Figures 1 and\n2 to clarify that not all naturally-occurring ET necessarily translates to crop growth. For\nexample, we have copied Fig. 2 below.\n43\n800\n400\nraey/mm\nAgricultural ET Naturally−occurring ET Total ET\nFig. 2. Variations in annual agricultural ET across and within crop groups. Mean\nagricultural ET by crop group (green fill and 95% CI) is the average difference between total ET\n(black outline) and naturally-occurring ET (brown fill). All measures are summed across the year,\nleading to naturally-occurring and total ET estimates that include water consumption occurring\noutside of the growing season. While we find significant differences in mean agricultural ET across\ncrop groups, the gray box plots also show a broad spread in agricultural ET within crop groups\n(box plots show 0.5, 0.25. 0.5, 0.75, and 0.95 quantiles).\n3.1.4 Sources of uncertainty in estimating agricultural ET\nFurther responding to your concern that our estimates of naturally-occurring ET may be\ninflated, we investigate multiple potential sources of error. To ensure the accuracy of our\nfindings and improve the clarity of our work, we follow the following steps, which are de\nscribed in detail below:\n1. Investigation of OpenET and ML model errors. We investigate potential errors\nassociatedwithOpenETandourmachinelearningmodel,andfindnoreasontosuspect\nbiased estimates arising from either. We undertake an analysis fully detailing the role\nof both in our estimates of agricultural ET (Supplementary Note 4).\n2. Analysis of land cover data accuracy. We confirm that our estimates of naturally\noccurring ET are not positively biased due to inaccuracies in our land cover data,\ncausing us to train our model not only on fallow lands, but some active agricultural\nlands (Supplementary Note 6).\n3. Comparison with other estimates. We find our estimates of naturally-occurring\nET compare well with our estimates of precipitation and estimates of natural ET in\nMediterranean climates.\n44\n4. Robustness of findings against hidden biases. We consider how our analyses\nwould be affected if another source of bias not accounted for in the above analyses\nwere affecting our estimates of naturally-occurring ET. Because our main findings are\nrelative, they would remain unaffected.\nInvestigation of OpenET and ML model errors. OpenET estimates have been\nvalidated over a broad variety of land cover types. According to OpenET’s Intercomparison\nand Accuracy Assessment Report, croplands, grasslands, and shrublands, all which may\nhelp represent the kinds of errors we might expect over fallow lands, exhibit no statistically\nsignificant biases, and therefore do not suggest that OpenET is positively biased over fallow\nlands (Table R 1) (OpenET, 2021). Shrublands may especially be considered as a good\nproxy for the fallow lands of the Central Valley since the average ET of the shrublands used\nin the OpenET validation, 31.07 mm/month, is similar to what OpenET predicts over fallow\nlands in the Central Valley (mean = 36.5 mm/month).\nLand cover type Statistic Ensemble Range\nSlope 0.95 0.86 - 1.04\nMBE (mm) -3.64 (-3.9%) -13.77 - 5.16 (-14.7% - 5.5%)\nCroplands\nMAE (mm) 155.66 (16.6%) 17.96 - 22.92 (19.2% - 24.5%)\n45 sites, N = 1682 months RMSE (mm) 195.17 23.43 - 25.89 (25% - 27.7%)\nMean station ET = 93.68 (mm) R-squared 0.95 0.89 - 0.93\nSlope 1.03 0.73 - 1.28\nMBE (mm) -1.23 (-2.9%) -11.88 - 9.73 (-27.9% - 22.9%)\nGrasslands\nMAE (mm) 19.45 (15.4%) 20.17 - 28.1 (47.4% - 66.0%)\n20 sites, N = 672 months RMSE (mm) 24.12 (56.7%) 24.62 - 35.96 (57.8% - 84.5%)\nMean station ET = 42.56 (mm) R-squared 0.75 0.54 - 0.8\nSlope 0.95 0.65 - 1.34\nMBE (mm) 2.89 (9.3%) -5.2 - 18.7 (-16.7% - 41.4%)\nShrublands\nMAE (mm) 15.68 (50.5%) 17.45 - 22.64 (56.2% - 72.9%)\n24 sites, N = 681 months RMSE (mm) 19.96 (64.2%) 20.9 - 29.18 (67.3% - 93.9%)\nMean station ET = 31.07 (mm) R-squared 0.66 0.3 - 0.69\nTable R 1. OpenET monthly performance over croplands, grasslands, and shrublands.\nMBE denotes mean bias error, and none of the land cover types show statistically significant MBE.\nReproduced from the OpenET Intercomparison and Accuracy Assessment Report, Table 3 (pg.6).\nTo improve transparency surrounding the effect of errors inherent to our method, we have\nadded a section in the supplementary information detailing the role of error in our analysis\n(Supplementary Note 4, SI line 76).\nmachine learning model error on agricultural ET es-\ntimates\nWe define agricultural ET as the difference between total ET and naturally-occurring\nET, ET = ET − ET (Main text eq. 1), where naturally-occurring ET is the\nag tot nat\ncounterfactual ET that would occur naturally, were the same land fallow. However,\nwe calculate agricultural ET using estimates of total ET from OpenET and modeled\nnaturally-occurring ET trained on OpenET data (Supplementary eq. 1). Since the\nOpenET data and the model we use to estimate naturally-occurring ET have errors\nassociated with them, our estimates of agricultural ET can be described as the true\nagricultural ET plus several error terms (Supplementary eq. 4), which we derive as\nfollows:\n˜\nET∗ = ET ˆ −ET ˆ (1)\nag tot nat\nwhere ET∗ denotes our estimate of agricultural ET, ET ˆ is OpenET’s estimate of\nag tot\n˜\nˆ\nET and ET is our estiate of the naturally-occurring ET, predicted by our machine\ntot nat\nlearning (ML) model. The hat notation indicates estimates inclusive of OpenET error,\nwhile the tilde indicates estimates inclusive of ML model error.\nBecause our naturally-occurring ET ML model is trained on and therefore predicts\nOpenET values over fallow lands, we can rewrite the same expression as:\nET∗ = ET ˆ −(ET ˆ +ϵ ) (2)\nag tot nat ML\nˆ\nwhere ET is the (naturally-occurring) ET that OpenET would observe if the field\nnat\nˆ\nwere fallow, and ϵ is the error from the ML model in predicting ET .\nML nat\nWe can then again reorganize the same equation by separating the error from the\nOpenET observations. This reveals the relationship between the true ET and our\nag\nestimate, ET∗ :\nag\nET∗ = ET +ϵ −(ET +ϵ +ϵ ) (3)\nag tot OpenET,ag nat OpenET,fal ML\n= ET +ϵ −ϵ −ϵ (4)\nag OpenET,ag OpenET,fal ML\nwhereET , ET , andET arethetrue(unobservable)agriculturalET,totalET,and\nag tot nat\nnaturally-occurring ET, respectively, ϵ is the error associated with the OpenET\nOpenET,ag\nestimate over the agricultural field and ϵ is the error associated with what the\nOpenET,fal\nOpenET estimate would be if the land were fallow.\nIn our study, we seek to estimate mean ET , be it over the entire Central Valley, for\nag\na certain crop type, or for a given county, we seek to calculate the expectation of ET\nag\nover a sample of grid cells. Using Supplementary eq. 5, we can show that our estimate\nof E[ET∗ ] depends not only on E[ET ], but also the expectation of the error terms.\nag ag\nag ag OpenET,ag OpenET,fal ML\nWe show in Supplementary Figures 1-4 that ϵ is unbiased, that is, E[ϵ ] = 0.\nML ML\nOpenET has also been shown to produce unbiased estimates over agricultural lands, so\nE[ϵ ] = 0 (see Table 3 on page 6 of the OpenET Intercomparison and Accuracy\nOpenET,ag\nreport) (OpenET, 2021). We can also assume ϵ is unbiased because though\nOpenET,fal\nOpenET has not specifically been evaluated over fallow fields, it produces unbiased\nestimates over natural shrublands and grasslands with ET values of similar magnitude\nto fallow fields in the Central Valley (OpenET, 2021). As a result, in expectation our\nestimatesofagriculturalETareunbiased, andconsequentlyproduceunbiasedregression\ncoefficients.\nE[ET∗ ] = E[ET ] (6)\nag ag\nWhile our estimates of agricultural ET are unbiased in expectation, the error terms\ndo add variance that is not present in the true agricultural ET. Therefore, any analysis\nthat assesses variation across pixels will reflect inflated variance relative to true agri-\ncultural ET. For example, this has implications for the farming practices and fallowing\nscenarios we conduct (Fig. 3). However, here we estimate that error is responsible\nfor only 11% of the variance in our annual estimates of agricultural ET, suggesting its\ninfluence over key results is limited. Specifically, if we assume that all error terms are\nindependent, we have:\nvar(ET∗ ) = var(ET )+var(ϵ )+var(ϵ )+var(ϵ ) (7)\nag ag OpenET,ag OpenET,fal ML\nwhere var(·) denotes the variance.\nThen, because variance is equal to mean squared error (MSE) when bias is 0, we\nhave:\nvar(ET∗ ) = var(ET )+MSE(ϵ )+MSE(ϵ )+MSE(ϵ ) (8)\nag ag OpenET,ag OpenET,fal ML\nWecalculatetheMSEofourmachinelearningmodelusingourtestset, andOpenET\nprovides root MSE values over croplands and shrublands (which we use to approximate\nerror over fallow lands) (again, see Table 3 on page 6 of the OpenET Intercompari-\nson and Accuracy report) (OpenET, 2021). We find that for estimates of yearly ET∗ ,\nag\nMSE(ϵ )+MSE(ϵ )+MSE(ϵ ) represents only 11% of var(ET∗ ).\nOpenET,ag OpenET,fal ML ag\nThis indicates that the vast majority of the variance we uncover is representative of true\ndifferences in water consumption across fields, as opposed to variability due to model\nerrors.\nbias our estimates of agricultural ET would be if the land cover data we use to identify fallow\nlandswereimprecise. Ifthiswerethecase,wewouldlikelybetrainingournaturally-occurring\nET machine learning model on agricultural fields. This would lead to an overestimation of\nnaturally-occurring ET and therefore an underestimation of agricultural ET.\nTo rule out this potential issue, we test the effect of train a model exclusively on land that\nwas marked fallow not only by the DWR dataset we use for our main results, but also by\nthe Cropland Data Layer (CDL). We find that the results do not change significantly with\nthis more stringent criterion, and report these findings in our supplementary information\n(Supplementary Note 6, Supplementary Fig. 13, 14, and 15) (SI line 142).\nSupplementary Note 6: Results when using training\ndata also marked fallow by the Cropland Data Layer\n(CDL)\nWhen training the naturally-occurring ET model, we rely on the California Department\nof Water Resources (DWR) LandIQ crop type dataset due to its high accuracy. Here,\nwe evaluate the effect of instead training the data only on fallow fields that are marked\nas fallow by both the DWR dataset and the Cropland Data Layer (CDL). Overall, this\nchange does not affect our estimates of naturally-occurring ET: while we estimate 438\nmm per year (394.4-481.7 95% CI) in the main text, we estimate 423.9 mm per year\n(369-478.7 95% CI) here. All general trends and conclusions are robust to this change\n(Supplementary Fig. 14, Fig. 15, Fig. 16).\n48\n1200\n800\n400\nraey/mm\nAgricultural ET Naturally−occurring ET Total ET\nSupplementary Fig. 14. Variations in agricultural ET across and within crop groups\nusing training data also marked fallow by the Cropland Data Layer. Thisistheequivalent\nof Fig. 2 in the main text, but calculated using a naturally-occurring ET model trained on data\nalso marked fallow by the Cropland Data Layer.\na b c % Savings\n0 5 10 15 >20\n100 km\nSupplementary Fig. 15. The percent reduction in agricultural ET driven by various\nmanagement scenarios using training data also marked fallow by the Cropland Data\nLayer. This is the equivalent of Fig. 3 in the main text, but calculated using a naturally-occurring\nET model trained on data also marked fallow by the Cropland Data Layer.\n50\na b\nAgricultural ET (mm/year)\nIrIrrirgigaattioionn eefffficicieiencnyc (y% ()%)\n41\n0 1000 2000\n49 44\nIrrigation (Tg/year) 0 25 50 75 100\n50 38\n42\n1000 2000 38\n36 33\n53\n66\n69\n65\n80\n83 64\n80\n100 km\nSupplementary Fig. 16. Irrigation efficiency across the counties of the Central Valley\nusing training data also marked fallow by the Cropland Data Layer. Thisistheequivalent\nof Fig. 4 in the main text, but calculated using a naturally-occurring ET model trained on data\nalso marked fallow by the Cropland Data Layer.\ncover for naturally-occurring ET are within reason when compared to precipitation within\nthe Central Valley: while CalSIMETAW reports an average precipitation of 554 mm/year,\nour naturally-occurring ET estimates amount to 438 mm/year (394.4-481.7 95% CI) (Orang\net al., 2013). Additionally, we find that 438 mm/year falls within the range of values found\nin the literature studying natural systems in Mediterranean climates, which typically report\nvalues between 300 and 480 mm/year (Aires et al., 2008; Baldocchi et al., 2004; Duell, 1988).\nRobustness of findings against hidden biases. These additional analyses do not\nsuggest that OpenET or other potential sources of error would be inflating our estimates of\nnaturally-occurrign ET. However, out of an abundance of caution, we consider how our find\nings would be affected if our estimates of naturally-occurring ET were nevertheless inflated,\ncausing artificially low estimates of agricultural ET. Because our main findings focus more\non relative differences in agricultural ET, the patterns we uncover would remain unchanged.\nFor example, the ranking in water intensity of crops would not be affected by a uniform\nunderestimation of agricultural ET. Our scenarios all report water savings in percent, and\nwould also remain unchanged. Our estimates of irrigation efficiency would increase, but our\nmain finding surrounding the pronounced north to south gradient would not be affected.\n3.2 Irrigation diversions\nConcern #2. I apologize if I missed this in the manuscript, or if it is common knowledge,\nbut could you please provide a thorough description of “diversion” that is used throughout the\nmanuscript in reference to water diversions and irrigation. My interpretation is that diver\nsion is water found/stored in one location (i.e., county) and “diverted” to another location\n(i.e., another county). This interpretation makes sense when looking at Fig. 4 (left column)\nwhere we find higher water diversions for irrigation in the north (the Sacramento River\nValley and north). Water in the northern portion of the state is famously (or infamously,\ndepending on where in the state you are) moved further south for agricultural irrigation. In\nmy mind this makes the southern counties much less efficient (in terms of irrigation effi\nciency) compared to the north. However, the opposite is presented, with the southern counties\nreporting higher irrigation efficiencies. Could you please offer more context to this Figure\nand the overall implications made from the associated analysis?\nWe apologize for the lack of clarity surrounding the nature of the irrigation data we are\nusing and our use of the word ‘diversion.’ The USGS data in California we use estimates\nirrigation water at the point of use, so water diverted from a northern county for use in a\nsouthern county would be counted in the southern county. Specifically, from personal cor\nrespondence with the USGS: “Regarding water diverted for agriculture in California, the\nwithdrawals for crop irrigation are estimated for the point of use. The irrigation withdrawal\npoints of diversion generally are located in the same county as where the water is applied\n(used).” We have modified the methods section and the caption of Fig. 4 to better reflect\nthis,andhavereplacedtheword“diversion”with“withdrawal”forimprovedclaritywherever\napplicable (e.g. lines 53, 207, 315). For example, in the methods section, we have (line 479):\ntion water use data from the USGS National Water Information System. We note that\nthese irrigation amounts are counted at the point of use, rather than the water’s point\nof origin.\nHowever, as you note, many of the numbers displayed in Fig. 4 are counter-intuitive.\nThis is because rather than displaying irrigation by volume, we were displaying irrigation in\nmm, which is a measure of volume divided by area (area of irrigated land, in our case.) To\nstave off confusion surrounding this matter, we have altered this figure to display volumetric\nirrigation as opposed to irrigation depth.\nAgricultural ET (mm/year)\nIrrigation efficiency (%)\nIrrigation (Tg/year)\nFig. 4. Irrigation efficiency across the counties of the Central Valley. (a) Irrigation\nefficiency is calculated by dividing agricultural ET (gridded data) by USGS county-level reports\nof irrigation amounts (blue polygons). For the calculation, agricultural ET is averaged to the\ncounty level to match the spatial scale of the irrigation data. Additionally, irrigation is displayed in\nvolumetric units (teragrams), but is divided by county-level cropland area to be in units consistent\nwith agricultural ET prior to the calculation. (b) The resulting county-level irrigation efficiency\nestimates vary widely across the Central Valley, with particularly low efficiencies in the northern\ncounties.\n53\n3.3 Orchard age and limitations of the farming practices scenario\nConcern #3. Different parts of the manuscript discuss the within crop variability in ET\nas well as statements alluding to how agricultural water applications can be significantly de\ncreased (from 95 percentile to 25 percentile) without changing the landcover. First, a likely\nreason for ET variability within the same cropping system (particularly tree nuts) is because\nof the age of the orchard. These systems are perennials and exist for 15-25 years. It makes\nsense that an annual would present lower variation – it’s effectively the same crop every year.\nA pistachio orchard changes drastically from 2nd leaf to 20th leaf, with a considerable amount\nof increase to biomass and by proxy ET. In order to make the claim that water savings can\noccur without changing the crop, there needs to be an accounting of orchard age. Second, I\ndo not think that scenario #2 in reducing agricultural ET is a viable scenario. Sure, deficit\nirrigation is possible and is well studied in certain cropping systems. In some, such as\nwine grapes, its required to produce a high-quality grape. But to suggest cutting back irrigation based\non ‘high’ ET estimates to ‘median’ ET estimates without including age (again, peren\nnials introduce some difficulty here), production goals of the commodity, or management style\nseems cavalier.\nThis comment raises important points surrounding the depth and accuracy of our anal\nysis, particularly in relation to orchard age and the viability of proposed water-saving sce\nnarios. In response to your concerns, we have investigated the impact of orchard age on\nagricultural ET and adjusted our analyses to account for this factor. Additionally, we have\nmodified our language to reflect the inherent limitations of the farming practices scenario\nrelatedtoitspotentialcosts, benefits, anduncertainties. Wedetailthesetwochangesbelow.\n3.3.1 Orchard age\nWe would first like to thank you for raising this important point about the effect of orchard\nage on agricultural ET. After investigating this effect in our data and find a large and clear\neffect of orchard age on agricultural ET, and document this in a new section in our supple\nmentary information (Supplementary Note 2, line 42).\nSupplementary Note 2: The effect of orchard age on\nagricultural ET\nWater consumption is known to be affected by orchard age, especially for young or\nchards (Drechsler et al., 2022). One advantage of data-driven, field-scale estimates of\nagricultural ET is that they can capture such variation.\nSince information about orchard age is not currently available in California, we use\nthe LandIQ dataset’s transitions from ”young perennial” classifications to crop-specific\norchard categories as a proxy for orchard age. ”Young perennials” refer to non-fruit\nbearing young trees, so their change to fruit-bearing orchard classifications helps us\n54\nestimate the age of the orchard. LandIQ crop type data are available for the years\n2014, 2016, 2018, 2019, and 2020. Since we have agricultural ET data for the years\n2016, 2018, and 2019, we can date orchards that have been bearing fruit for up to five\nyears (i.e. these would be orchards for which we have agricultural ET data in 2019, and\nwere classified as young perennials for the last time in 2014.) Using this technique, we\nuncover a clear relationship between orchard age and agricultural ET (Supplementary\nFig. 7).\n60\n40\n20\n0\n−2 −1 1 2 3 4\nIn\n5 termediate −4 −3 −2 −1\nYoung orchard Old orchard\n)htnom/mm(\nTE\nlarutlucirgA\nYears to bearing fruit\nYears bearing fruit\nIntermediate age\nYears to removal\nSupplementary Fig. 7. Water consumption by life stage of orchards in California’s\nCentral Valley. Prior to bearing fruit, orchards have low agricultural water consumption, or\nevapotranspiration (ET). Water consumption then increases rapidly during the first 5 years of fruit\nproduction, after which it stabilizes.\nFirst, we include this finding in our initial discussion of within-crop variability in the results\nsection (line 148):\nPart of the large variability in agricultural ET for deciduous fruits and nuts can be\nexplained by orchard age: young orchards consume significantly less water than more\nmature ones (Supplementary Note 2, Supplementary Fig. 7). Climate, topography, and\nsoil quality explain an additional 6% of within-crop variation. However, substantial\nwithin-crop variation remains, indicating that some of these differences may be due to\nvariations in farming practices. This would suggest that reducing water consumption\nwithout switching crops may be feasible, which we explore in the next section.\nNext, we adjust our management scenario simulations to account for orchard age. We\ndescribe this change briefly in our results section (line 166):\nBecause it would not make sense to prescribe farmers in different contexts to consume\nsimilar amounts of water, we calculate crop-specific agricultural ET and within-crop\nvariation at the level of groundwater sub-basins. We additionally control for differences\nin climate, topography, and soil type before conducting the scenarios. To account for\nthe effects of orchard age, we remove orchards that have been bearing fruit for 5 years\nor less or that are in their last year of production (Supplementary Fig. 7).\nA more detailed explanation of the methodology used is described in the methods (line\n465):\nTo ensure that the water savings identified in our scenarios result from factors that can\nbe influenced by management interventions, we account for orchard age, climate, and\nother physical characteristics of the land. To account for orchard age, we remove young\norchards that are have been bearing fruit for 5 or less years, or old orchards that are\ngoing to be removed within the next year (Supplementary Note 2). Because we are only\nable to label orchard age in this way for all orchards in year 2019 (the DWR crop type\ndata we use are only available starting 2014), we exclusively use 2019 for this part of\nthe analysis. Since we are only using one year, we do not adjust the water use accord\ning to the year like we do for the other analyses. To account for differences in climate\nand other characteristics inherent to the land, we control for potential ET, soil quality,\ntopographic wetness index, elevation, aspect and slope using linear regression (eq. 5).\nWhile these modifications are essential for reinforcing the validity of our findings, it is\nnoteworthy that the results remain largely consistent, even with the analysis limited to the\nyear 2019.\n3.3.2 Limitations of the farming practices scenario\naccount for other equally important factors such as production goals and management style.\nAs a result, we have revisited our discussion of the farmer practices scenario to more clearly\nrecognize these limitations (line 245).\n[...] Unfortunately, a lack of high-resolution data on field-scale farming practices and\nyields inhibits further analysis of specific practices driving agricultural ET variation and\ntheireconomicimplications. However, priorliteraturefromexperimentalplotsorpartic\nular locations suggests that mulching (Kader et al., 2019), conservation tillage (Mitchell et al.,2012),\ndeficitirrigation (Englishand Raja, 1996;Goldhamer,1999; Rudnicket al., 2019), and\nimproved irrigation scheduling and technologies (Jovanovic et al., 2020) all have\npotentialtolimitagriculturalET.Thesepracticesmayproveadvantageousrelative\nto costly strategies like crop-switching or fallowing, though more detailed cost-benefit\nanalyses are necessary to determine the suitability of various interventions in specific\ncontexts (Mitchell et al., 2016). Such water saving farming practices are not mentioned\nin the plans drafted by SGMA water managers (Bruno et al., 2022). This is possibly due\nto a lack of conclusive research on the potential of such strategies to effectively decrease\nagricultural ET without significant effects on yield or operation cost. Spatial data on\nthe use of these different practices would allow researchers to take full advantage of our\nhigh-resolution agricultural ET estimates and study their water-saving and economic\nbenefits.\n3.4 More specifics\nLine 31: This first sentence is a bit confusing – are you saying the overexploitation of\n‘drought’ and ‘climate change’ have led to ..... it seems like there is simply some grammat\nical error tying the sentence together.\nThank you for this point; we have re-organized this sentence to increase clarity (line 32):\nClimatechange, drought,andtheoverexploitationofwaterresourceshaveledtodeclines\nin freshwater storage in many vital agricultural regions (Rodell et al., 2018), raising con\ncerns surrounding the future of food and water security (Elliott et al., 2014; Famiglietti,\n2014).\nLine 43: “because a large share of irrigation water is returned to the system as runoff or\nrecharge”. I understand the statement and it is true, but because the study takes place in\nCalifornia, it is important to note the high efficiency of the irrigation in the state (drip or\nmicro-sprinklers installed). In these systems, very little irrigated water is lost to runoff or\ndeep percolation.\nWe have edited the statement to include this information (line 42):\npoor proxy for the amount of water crops consume through evapotranspiration (ET)\n(Marston et al., 2022). Only some irrigation water results in ET: the rest remains in\nthe system as runoff or recharge, though this proportion can vary widely depending\non topography, climate, soil type, and farming practices (Ward and Pulido-Velazquez,\n2008).\nLine 46: Consider rephrasing in that evapotranspiration is not evaporated by the soil but\nrather from the soil to the atmosphere.\nThank you for this point; we have rephrased this passage as follows (line 46):\nAgricultural ET, or the increase in ET that irrigated agriculture brings, is therefore a\ncritical measure in that it represents the amount of water that is actually “consumed”\nby agriculture. This water leaves the watershed entirely as it is evaporated from the soil\nand transpired by crops (Hoekstra and Mekonnen, 2012).\nLine 51: This last sentence is a bit confusing to me. How does decreasing water diversions\nnot affect food production? Because irrigation water does not contribute to crop growth?\nMaybe I’m missing something?\nWe apologize for this confusing sentence. What we meant to highlight in this passage is\nthat of all the water used for irrigation, only that which ends up as agricultural ET has the\npotential to contribute to crop growth, and therefore food production. We have modified\nthis section to improve clarity (line 52):\nWhen compared to total irrigation amounts, agricultural ET can also be used to high\nlight the fraction of irrigation withdrawals that do not effectively result in consumptive\nuse (Puy et al., 2022). While this water stays in the system and therefore may continue\nto provide beneficial uses (Grafton et al., 2018), it also does not achieve its original\npurpose of contributing to crop growth. Therefore, agricultural ET can help calculate\nirrigation efficiency and identify unnecessary irrigation water withdrawals from surface\nor groundwater reservoirs (Puy et al., 2022).\nLine 112: Here you give error metrics in cm per year, but you specify they will be mm per\nyear on line 285. I would suggest reporting values in mm per year as I’ve never seen ET\nreported in cm per year. It might also be worth mentioning that all growers use acre-in or\nacre-feet as their unit of choice. I would suggest converting your overall units (mm/day)\nto acre-in/acre-feet in maybe the conclusion/discussion so a broader readership can more\nquickly interpret your results.\nWe apologize for the inconsistency in metrics used, and thank you for your suggestion to\nuse the more common mm per year metric. We have changed all calculations to be repre\nsented in mm per year (e.g. line 125, 136, 137). To maximize interpretability of our results\n58\nfor a broad readership, we do not use specific numbers in the discussion or conclusion and\ninstead focus on the trends and relative values we uncover in our analysis. Therefore, we do\nnot see an opportunity to use acre-in or acre-feet units in the discussion or conclusion, but\nwill be sure to do so if we revise these sections to include specific numbers in the future.\nLine 113: Your test set is made up of 2 km2 regions – can you mention how many?\nAlso, these regions represent fallowed land. Are all 2 km2 regions completely fallowed land?\nI guess I’m asking if all fallowed pixels chosen are arbitrary 2 km2 boundaries placed all\nover the central valley and not polygons outlining specific fallowed fields.\nWe apologize for the lack of clarity surrounding our hold-out sets. The 2 km2 areas are\nnot polygons outlining specific fallowed fields, but rather arbitrary boundaries. We have\nclarified our methodology and mention the number of regions in the methods section (line\n396):\nTo validate our naturally-occurring ET model, we split our dataset, reserving 60% for\ntraining, 10% for validation, and 30% for testing. In order to ensure that nearby and\ntherefore very similar pixels are not present across multiple splits, we group our splits\nby 2 km2 squares, four times the size of a large agricultural field in the Central Valley.\nThe entire dataset is made up of over 16 million pixels populating 8,180 distinct 2 km2\nregions. Nearly 10 million pixels and 4,908 2 km2 clusters are contained in the training\nsplit, and 2,454 clusters are reserved for testing.\nLine 137: See concern #3. Line 149: See concern #3.\nThese lines have been revised in accordance with our response to concern #3.\nLine 208: Irrelevant and misleading. This 30% figure is from citations looking at wheat,\nmaize, and cotton; all of which are annuals and all of which have no notable economic vi\nability in California (combined, their economic output is only 2% of almonds and grapes\ncombined).\nThank you for flagging this. Given that this statement is not relevant to the California\ncontext, we have removed this statement and replaced it with a more appropriate discussion\nabout different farmer practices as described in our response to your third comment (line\n236).\nLine 250: Sorry for likely not following, but isn’t this 7% much lower than values specified\nearlier? Where is this coming from?\nWe apologize for the confusion surrounding this statement, “Finally, the naturally\noccurring ET estimates have a MAE of 3.6 cm per year, representing only 7% of the average\nagricultural ET of an active agricultural field.” The 7% refers to the mean absolute error of\nour machine learning model that predicts naturally-occurring ET, not naturally-occurring\na more straightforward description of error (line 308):\nAdditionally, theOpenETensemblemodelhasbeenextensivelyvalidated(Meltonetal.,\n2022; OpenET, 2021), and our machine learning model has an R2 of .87. We estimate\nthat these sources of error are responsible for only 11% of the variance in our yearly\nagricultural ET estimates (Supplementary Note 4). Nevertheless, the water-saving po\ntentials we calculate for both the fallowing and farmers practice scenarios should be\ninterpreted as upper bound estimates.\nAires, L., Pio, C., and Pereira, J. (2008). 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Proceedings of the National Academy of Sciences, 105(47):18215–18220.\nPublisher: Proceedings of the National Academy of Sciences.\nWong, A. J., Jin, Y., Medell´ın-Azuara, J., Paw U, K. T., Kent, E. R., Clay, J. M., Gao, F.,\nFisher, J. B., Rivera, G., Lee, C. M., Hemes, K. S., Eichelmann, E., Baldocchi, D. D.,\nand Hook, S. J. (2021). Multiscale Assessment of Agricultural Consumptive Water Use\nin California’s Central Valley. Water Resources Research, 57(9):e2020WR028876. eprint:\nhttps://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR028876.\nReviewer #1 (Remarks to the Author):\nThe authors have addressed my questions/feedback satisfactorily. I do not have further requests for\nrevision.\nReviewer #3 (Remarks to the Author):\nMany thanks to the author(s) for their revisions. This was a very strong paper prior to revisions and I\nbelieve it is even stronger now. The revisions were probably the most thought out responses I have\never received. This is what the scientific (and review) process is supposed to look like and I want to\nthank the author(s) for their efforts and restoring my confidence in the review process. It was a joy to\nread the manuscript and I am looking forward to its publication and being able to cite the work."}]

Decision letter