Time Series Evapotranspiration and Applied Water Estimates from Remote Sensing

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1 Applied Water Estimates from Remote Sensing Prepared By March 213

2 Contents Kaweah Delta Water Conservation District Time Series Evapotranspiration and Applied Water Estimates from Remote Sensing Contents Prepared by Davids Engineering, Inc. March 213 Contents... i Tables... ii Figures... iii Acronyms and Abbreviations... v 1 Overview Development of Field Boundaries and Cropping Development of Field Boundaries Assignment of Cropping and Irrigation Method Assignment of Initial Crop Group and Irrigation Method Assignment of Final Crop Group and Irrigation Method Fields Not Evaluated Analysis Image Selection Filling Cloud Gaps with MODIS Extraction of Values by Field and Development of Time Series Results Development of Relationships to Estimate Basal Crop Coefficient from Model Development Estimation of Daily Reference Evapotranspiration Estimation of Daily Precipitation Estimation of Root Zone Water Balance Parameters Estimation of Annual Crop Consumptive Use Fraction and Other Irrigation Parameters Development of Database Model Calibration, Validation, and Application of Field-Scale Root Zone Water Balance Model Model Calibration Validation of Actual Crop ET Field and Section Scale Validation Validation by Crop-Method Group, Hydrologic Group, and Soil Type Validation by Crop-Method Group for SEBAL Periods Calibration and Validation of Crop Consumptive Use Fraction Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes Comparison to Water Resources Investigation Estimates Kaweah Delta Water Conservation District i March 213

3 Contents 7.1 Comparison of Monthly and Annual Crop Coefficients and Crop ET Alfalfa Citrus Deciduous Orchards Fallowed Fields Cotton Miscellaneous Field Crops Pasture Small Grains Sugarbeets Truck Crops Vineyards Summary of Comparisons by Crop Comparison of Remote Sensing and Root Zone Model Results to Water Resources Investigation for Comparison of Cropped Acres Comparison of Crop Evapotranspiration Comparison of Total Precipitation Comparison of Effective Precipitation Comparison of Gross Applied Irrigation Water Comparison of Deep Percolation of Precipitation Comparison of Deep Percolation of Applied Irrigation Water Comparison of Net Applied Irrigation Water Uncertainty Analysis of Surface Layer Fluxes Conclusions References... 9 Tables Table 1.1. Summary of Report Tables, Including Description of Area Represented and Role of Supporting Analysis Table 2.1. Crop Groups, Associated Crops, and Initial Cropped Acreage within and Immediately Surrounding KDWCD Table 2.2. Summary Statistics of Irrigation Method by Crop Group based on 23 Kings County and 1999 Tulare County DWR Crop Surveys, KDWCD and Immediately Surrounding Area Table 2.3. Summary Statistics of Irrigation Method by Crop Group based on 27 Tulare County DWR Crop Survey, KDWCD and Immediately Surrounding Area Table 3.1. Regression Parameters and R-Squared Values for Adjustment of MODIS to Landsat Table 3.2. Mean Actual Crop Coefficients by Range for KDWCD General Crop Types based on 27 SEBAL ET Analysis Table 4.1. Characteristics of Nearby CIMIS Agronomic Weather Stations Table 4.2. Summary of Weather Stations Evaluated for Development of Daily Precipitation Records Table 5.1. Analysis Periods and Image Selection for 27 SEBAL Analysis Table 5.2. Analysis Periods and Image Selection for 28 SEBAL Analysis Kaweah Delta Water Conservation District ii March 213

4 Contents Table 5.3. Analysis Periods and Image Selection for 29 SEBAL Analysis Table 5.4. Summary of Calibration Field Group, Validation Field Group, and District-Wide Cropping Based on 27 Crop-Method Groups Table 5.5. Comparison of Root Zone Model to SEBAL Area-Weighted Average Actual Evapotranspiration for KDWCD Verification Fields: 27, 28, and Table 5.6. Comparison of SEBAL and Root Zone Water Balance Model Evapotranspiration by Crop Method Group, 27 to Table 5.7. Comparison of SEBAL and Root Zone Water Balance Model Evapotranspiration by Hydrologic Unit, 27 to Table 5.8. Comparison of SEBAL and Root Zone Water Balance Model Evapotranspiration by Soil Type, 27 to Table 5.9. Summary of Modeled and Target CCUF by Crop-Method Group and Additional Applied Water Fluxes, 1999 to 21 Average Table 5.1. Summary of Modeled Precipitation Fluxes, 1999 to 21 Average Table 6.1. Average Annual Acreage by Crop-Method Group by Hydrologic Unit Table 7.1. WRI Crop Groups and Monthly Crop Coefficients (Fugro West, 23) Table 7.2. Summary of RS-RZ Fields and Acres by WRI Crop Group Table 7.3. WRI Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole Table 7.4. WRI Flux Depth Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole Table 7.5. RS-RZ Model Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole Table 7.6. RS-RZ Model Flux Depth Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole Table 7.7. Difference between RS-RZ Model and WRI Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole Table 7.8. Difference between RS-RZ Model and WRI Flux Depth Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole Table 7.9. Comparison of Crop ET Depths and Estimated Volumes for WRI and RS-RZ Model, Tulare County (Hydrologic Units 1 5), Table 7.1. Estimated Uncertainties in ET, ET aw, Applied Water, and DP aw by Approach Figures Figure 1.1. Detailed Summary of Process to Develop, Configure, and Apply Remote Sensing-Based Daily Root Zone Water Balance Model Figure 2.1. Field Boundaries and Hydrologic Units Figure 2.2. Source Year for Final Crop and Irrigation Method Assignment Figure 2.3. Estimated Acres by Crop Group, 1999 to Figure 2.4. Initial Crop Group Irrigation Method Categories Figure 2.5. Final Crop Group Irrigation Method Categories Figure 2.6. Changes in Crop-Irrigation Method Group Assignments (Initial to Final) Figure Time Series for KDWCD Figure 3.2. Illustration of Process for Fill Cloud Gaps in Landsat Imagery Using MODIS Figure 3.3. Regression Equations to Adjust from Eight MODIS Images to Landsat Values Kaweah Delta Water Conservation District iii March 213

5 Contents Figure 3.4. Combined Regression Equation to Calibrate from Eight MODIS Images to Landsat Values Figure 3.5. Sample Time Series for Selected 75-Acre Pasture Field (Field 552) Figure 3.6. Sample Time Series for Selected 166-Acre Field and Truck Crop Field (Field 6891). 24 Figure 3.7. Sample Time Series for Selected 24-Acre Deciduous Orchard Field (Field 3614) Figure 3.8. Sample Time Series for Selected 27-Acre Subtropical Orchard Field (Field 2594) Figure 3.9. Sample Time Series for Selected 57-Acre Vineyard Field (Field 4741) Figure 3.1. Comparison of and Kcs, April to August 27, Top 1 KDWCD Crops Combined. 29 Figure 4.1. Conceptualization of Fluxes of Water Into and Out of the Crop Root Zone Figure 4.2. Linear Regression to Estimate Daily Porterville ET o Based on Stratford CIMIS Station Figure 4.3. Annual Precipitation Totals for Selected Precipitation Stations Figure 4.4. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Surface Irrigated Deciduous Orchard Field (Field 3614), Figure 4.5. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Surface Irrigated Field and Truck Field (Field 3593), Figure 4.6. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Surface Irrigated Pasture Field (Field 3372), Figure 4.7. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Micro- Irrigated Subtropical Field (Field 446), Figure 4.8. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Micro- Irrigated Vineyard Field (Field 4741), Figure 5.1. Comparison of 27 Annual Crop ET from Root Zone Water Balance and SEBAL, KDWCD Verification Fields Figure 5.2. Comparison of 28 February - November Crop ET from Root Zone Water Balance and SEBAL, KDWCD Verification Fields Figure 5.3. Comparison of 29 March - September Crop ET from Root Zone Water Balance and SEBAL, KDWCD Verification Fields Figure 5.4 Comparison of 27 Annual Crop ET from Root Zone Water Balance and SEBAL, Section Scale Figure 5.5. Comparison of 28 Annual Crop ET from Root Zone Water Balance and SEBAL, Section Scale Figure 5.6. Comparison of 29 March - September Crop ET from Root Zone Water Balance and SEBAL, Section Scale Figure 5.7. Comparison of January December Period and Total SEBAL and Root Zone Model Results, Figure 5.8. Comparison of February November Period and Total SEBAL and Root Zone Model Results, Figure 5.9. Comparison of March October Period and Total SEBAL and Root Zone Model Results, Figure 6.1. Average Annual Crop-Method Group Acreage by Hydrologic Unit Figure 6.2. Annual Total Depth of ET aw, AW, DP aw, and DP pr by Crop-Method Group Figure 6.3. Annual Total Volume of ET aw, AW, DP aw, and DP pr by Crop-Method Group Figure 6.4. Annual Total Depth of ET aw, AW, DP aw, and DP pr by Hydrologic Unit Figure 6.5. Annual Total Volume of ET aw, AW, DP aw, and DP pr by Hydrologic Unit Kaweah Delta Water Conservation District iv March 213

6 Contents Figure 6.6. Average Monthly Depth of ET aw, AW, DP aw, and DP pr by Crop-Method Group Figure 6.7. Average Monthly Volume of ET aw, AW, DP aw, and DP pr by Crop-Method Group Figure 6.8. Average Monthly Depths of ET aw, AW, DP aw, and DP pr by Hydrologic Unit Figure 6.9. Average Monthly Volumes of ET aw, AW, DP aw, and DP pr by Hydrologic Unit Figure 7.1. WRI and RS-RZ Model Results for Alfalfa, Figure 7.2. WRI and RS-RZ Model Results for Citrus, Figure 7.3. WRI and RS-RZ Model Results for Deciduous Orchards, Figure 7.4. WRI and RS-RZ Model Results for Citrus, Figure 7.5. WRI and RS-RZ Model Results for Cotton, Figure 7.6. WRI and RS-RZ Model Results for Miscellaneous Field Crops, Figure 7.7. WRI and RS-RZ Model Results for Pasture, Figure 7.8. WRI and RS-RZ Model Results for Small Grains, Figure 7.9. WRI and RS-RZ Model Results for Truck Crops, Figure 7.1. WRI and RS-RZ Model Results for Vineyards, Figure Comparison of 1999 Annual Crop ET for WRI and RS-RZ Approaches by WRI Crop Group Figure Comparison of WRI and RS-RZ Model Acreage and Flux Volume Estimates for 1999 for KDWCD as a Whole Figure Comparison of WRI and RS-RZ Model Flux Depth Estimates for 1999 for KDWCD as a Whole Figure Comparison of DWR and FSA Field Delineations Acronyms and Abbreviations AW CCUF CIMIS CLU DP aw DP pr DWR E ET ET a ET aw ET c ET o FAO Applied irrigation water Crop consumptive use fraction California Irrigation Management Information System Common land unit Deep percolation of applied water Deep percolation of precipitation California Department of Water Resources Evaporation Evapotranspiration Actual evapotranspiration (used synonymously with ET c for purposes of this study) Evapotranspiration of applied water Crop evapotranspiration (used synonymously with ET a for purposes of this study) Reference evapotranspiration Food and Agriculture Organization FSA HU K c K cb K cs KDWCD K e MAD MODIS MS NCDC NRCS REW RS RZ Model of the United Nations Farm Services Administration Hydrologic unit Crop coefficient Basal crop coefficient Actual crop coefficient Kaweah Delta Water Conservation District Evaporation coefficient Management allowable depletion Moderate Resolution Imaging Spectroradiometer MicroSoft National Climatic Data Center Normalized difference vegetation index Natural Resources Conservation Service Readily evaporable water Remote sensing based root zone model Kaweah Delta Water Conservation District v March 213

7 Contents SEBAL SNA SQL T TEW Surface Energy Balance Algorithm for Land SEBAL North America, Inc. Standard query language Transpiration Total evaporable water USDA WRI United States Department of Agriculture Water Resources Investigation Kaweah Delta Water Conservation District vi March 213

8 Overview 1 Overview The purpose of this effort was to develop improved time series estimates of agricultural water demands for the Kaweah Delta Water Conservation District (KDWCD) service area from 1999 through 21. Demand was quantified at the field scale using a daily root zone water balance model, allowing aggregation to monthly time steps by hydrologic unit or other spatial scale to support ongoing groundwater modeling and conjunctive management of available surface water and groundwater supplies by KDWCD. Crop evapotranspiration (ET), the primary driver of agricultural water demand, was quantified using a combination of remote sensing and simulation of irrigation events using a daily root zone water balance model driven by remotely-sensed estimates of crop ET. Total applied irrigation water was then calculated based on estimates of the crop consumptive use fraction (CCUF, ratio of ET of applied water to total applied irrigation water) by crop type and irrigation method. Finally, downward fluxes to the groundwater system from deep percolation of precipitation and deep percolation of applied water were estimated. The process of developing, configuring, and applying the daily root zone water balance model included several steps. This report presents the results of this effort, and includes the following sections: 1. Overview 2. Development of Field Boundaries and Cropping 3. Analysis 4. Model Development 5. Calibration, Validation, and Application of Field-Scale Root Zone Water Balance Model 6. Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes 7. Comparison to Water Resources Investigation Estimates 8. Conclusions 9. References The report is structured to describe the modeling process essentially in the order it was completed. This process is summarized in detail in Figure 1.1. The modeling process described above included several supporting analyses that are described herein. In many cases, the acreage and individual fields evaluated as part of these supporting analyses differ from the full set of fields modeled within KDWCD s six hydrologic units (HUs) to determine overall surface fluxes for KDWCD s service area. Individual tables described in the report, along with a description of the area analyzed are summarized in Table 1.1. Additionally, a description of the role of the supporting analysis in developing, configuring, and applying the model is provided. The individual analyses and their results are described in greater detail in the corresponding report sections. Kaweah Delta Water Conservation District 1 March 213

9 Overview Develop Field Boundaries and Cropping Compile field delineations and assemble GIS coverage for study area Review available cropping and irrigation method data and develop general crop irrigation method groups Prepare time series cropping and irrigation method assignments by field over time Normalized Difference Vegetation Index () Analysis Identify and select satellite images Fill cloud gaps Extract by field and interpolate to daily values over time Develop relationship between basal crop coefficient and Analyze actual crop coefficients by crop for increasing Select relationship parameters Model Development Prepare reference ET and precipitation datasets Estimate root zone water balance parameters Estimate crop consumptive use fraction and other irrigation parameters Program and test database root zone water balance calculations Model Calibration, Validation, and Application Compile available SEBAL ET datasets Divide fields into calibration and validation datasets Apply model for calibration dataset and iteratively adjust input parameters Validate model through comparison of model results to SEBAL ET for validation dataset Evaluate reduction in random error from aggregation of results to section scale Compare model results by year 1 to SEBAL ET by crop method group, hydrologic unit, and soil type Compare model results to SEBAL ET over time within each year by crop method group Calibrate application efficiencies to provide targeted crop consumptive use fraction Summarization of Results Summarize irrigated area by hydrologic unit Summarize annual ET of applied water, applied water, and deep percolation fluxes by crop method group Summarize annual ET of applied water, applied water, and deep percolation fluxes by hydrologic unit Summarize average monthly ET of applied water, applied water, and deep percolation fluxes by crop method group Summarize average monthly ET of applied water, applied water, and deep percolation fluxes by hydrologic unit Comparison to Water Resources Investigation Compare monthly and annual crop coefficients and crop ET Compare modeled surface layer fluxes to Water Resources Investigation for 1999 Evaluate relative uncertainties in surface layer fluxes 1. SEBAL results corresponding to 27, 28, and 29 were used for model calibration and validation. As described herein, the SEBAL results for 27 represent the full calendar year, whereas the remaining two datasets represent a portion of the calendar year. Figure 1.1. Detailed Summary of Process to Develop, Configure, and Apply Remote Sensing-Based Daily Root Zone Water Balance Model. Kaweah Delta Water Conservation District 2 March 213

10 Overview Table 2.1. Crop Groups, Associated Crops, and Initial Cropped Acreage within and Immediately Surrounding KDWCD Area Represented HUs 1-6, Area Surrounding KDWCD Table 1.1. Summary of Report Tables, Including Description of Area Represented and Role of Supporting Analysis. Source of Cropping and Acreage Role of Supporting Analysis Total Acres Comments 1999 Tulare and 23 Kings DWR Crop Survey GIS files This analysis provided an initial view of the individual crops grown within and immediately surrounding the KDWCD service area and supported the development of general crop groups to be depicted in the model. 36,39 Ultimately, the field boundaries included in the DWR GIS files were not used. Additionally, for final analysis, results for the area immediately surrounding, but outside of KDWCD's service area are not reported Summary Statistics of Irrigation Method by Crop Group based on 23 Kings County and 1999 Tulare County Crop Surveys, KDWCD and Immediately Surrounding Area 2.3. Summary Statistics of Irrigation Method by Crop Group based on 27 Tulare County DWR Crop Survey, KDWCD and Immediately Surrounding Area HUs 1-6, Area Surrounding KDWCD HUs 1-5, Tulare County Area Surrounding KDWCD 1999 Tulare and 23 Kings DWR Crop Survey GIS files 27 Tulare DWR Crop Survey GIS File, excluding areas where 21 cropping information was used This analysis supported development of general cropirrigation method field groups within and immediately surrounding the KDWCD service area and initial assignment of fields to individual groups (i.e., assignment of the 1999 crop-irrigation method group to each field). This analysis supported final assignment of cropirrigation method groups to fields within and immediately surrounding the KDWCD service area (i.e., assignment of the 27 crop-irrigation method group to each field) and through comparison to Table 2.2 provides insight into changes in cropping and irrigation methods over time. 36,39 Ultimately, the field boundaries included in the DWR GIS files were not used. Additionally, for final analysis, results for the area immediately surrounding, but outside of KDWCD's service area are not reported. 138,288 Ultimately, the field boundaries included in the DWR GIS files were not used. Additionally, for final analysis, results for the area immediately surrounding, but outside of KDWCD's service area are not reported. Note that this table excludes HU 6, as well as fields for which 21 cropping information was available from sources other than DWR Mean Actual Crop Coefficients by Range for KDWCD General Crop Types based on 27 SEBAL ET Analysis HUs 1-5, Tulare County Area Surrounding KDWCD 27 Tulare DWR Crop Survey GIS file, buffered inward 45 meters, polygons representing top 1 KDWCD crops This analysis supported the selection of initial estimates of maximum basal crop coefficients (K cb,max ) for general crop groups for parameterization of the model. These estimates were later refined as part of model calibration. 98,84 The acreages reported represent buffered field boundaries from the DWR GIS file for Tulare County for 27. Only the top ten crops (representing more than 9% of the cropped area) were included in the analysis. The acreage for each field could be counted more than once as the analysis included ten image dates during the primary growing season from March to October Summary of Calibration Field Group, Validation Field Group, and District-Wide Cropping Based on 27 Crop-Method Groups Comparison of SEBAL and Root Zone Water Balance Model Results by Crop Irrigation Method Group, 27 to Comparison of SEBAL and Root Zone Water Balance Model Results by Hydrologic Unit, 27 to 29. HUs 1-6, Area Immediately Surrounding KDWCD HUs 1-6, Area Immediately Surrounding KDWCD FSA CLU field boundaries, 27 Tulare DWR Crop Survey GIS file, 21 Crop Data, 23 Kern County DWR Crop Survey GIS file FSA CLU field boundaries, 27 Tulare DWR Crop Survey GIS file, 21 Crop Data, 23 Kern County DWR Crop Survey GIS file HUs 1-6 FSA CLU field boundaries, 27 Tulare DWR Crop Survey GIS file, 21 Crop Data, 23 Kern County DWR Crop Survey GIS file This analysis demonstrates that the separate sets of calibration and validation fields represent approximately the same acreage and include fields for each crop-irrigation method group that are proportionate to the analysis area as a whole. This analysis demonstrates the agreement between SEBAL total crop ET and root zone model total crop ET by crop-irrigation method group for the validation fields from 27 to 29. This analysis demonstrates the agreement between SEBAL total crop ET and root zone model total crop ET by hydrologic unit for the validation fields from 27 to ,36 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. 141,793 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. Only the validation fields are included, which were not used as part of the model calibration process. 122,536 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. Only the validation fields are included, which were not used as part of the model calibration process. Also, fields immediately surrounding KDWCD are not included. Kaweah Delta Water Conservation District 3 March 213

11 Overview Table 5.8. Comparison of SEBAL and Root Zone Water Balance Model Results by Soil Type, 27 to Summary of Modeled and Target CCUF by Crop Method Group and Additional Applied Water Fluxes, 1999 to 21 Average. Area Represented HUs 1 6, Area Immediately Surrounding KDWCD HUs 1 6, Area Immediately Surrounding KDWCD Source of Cropping and Acreage Role of Supporting Analysis Total Acres Comments FSA CLU field boundaries, 27 This analysis demonstrates the agreement between 141,793 The field boundaries consist of FSA CLU field boundaries, which Tulare DWR Crop Survey GIS SEBAL total crop ET and root zone model total crop ET better represent the actual cropped area than the DWR GIS files. file, 21 Crop Data, 23 Kern by hydrologic unit for the validation fields from 27 Where CLU boundaries were not available, DWR field boundaries County DWR Crop Survey GIS to 29. were used to fill in the field boundary dataset. Only the validation file fields are included, which were not used as part of the model calibration process. FSA CLU field boundaries, 27 Tulare DWR Crop Survey GIS file, 21 Crop Data, 23 Kern County DWR Crop Survey GIS file This analysis demonstrates the agreement between the specified CCUF values by crop irrigation method group and the root zone model results over the full analysis period from 1999 to 21. Additionally, average applied water fluxes are summarized. 286,846 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. All fields are included Summary of Modeled Precipitation Fluxes, 1999 to 21 Average Average Annual Acreage by Crop Method Group by Hydrologic Unit 7.2. Summary of RS RZ Fields and Acres by WRI Crop Group. HUs 1 6, Area Immediately Surrounding KDWCD FSA CLU field boundaries, 27 Tulare DWR Crop Survey GIS file, 21 Crop Data, 23 Kern County DWR Crop Survey GIS file HUs 1 6 FSA CLU field boundaries, 1999 and 27 Tulare DWR Crop Survey GIS files, 21 Crop Data, 23 Kern County DWR Crop Survey GIS file HUs 1 5, Tulare County Area Immediately Surrounding KDWCD FSA CLU field boundaries, 1999 Tulare DWR Crop Survey GIS file As a companion to Table 5.9, this analysis summarizes average precipitation fluxes. Quantifies the average annual acreages depicted in Figure 6.1. This analysis was performed to provide a comparison of crop ET estimates from the root zone model to the previous Water Resources Investigation, completed in ,846 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. All fields are included. 25,39 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. Only fields within Tulare County and KDWCD are included, to represent the irrigated area. The slight difference from Table 7.5, described below, may be due to rounding error and is not considered significant. 24,541 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. Only fields within Tulare County are included, as the 1999 cropping data upon which the comparison is based were only available for that area WRI Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole RS RZ Model Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole Comparison of Crop ET Depths and Estimated Volumes for WRI and RS RZ Model, Tulare County (Hydrologic Units 1 5), HUs Water Resources Investigation (WRI) Report HUs 1 6 FSA CLU field boundaries, 27 Tulare DWR Crop Survey GIS file, 21 Crop Data, 23 Kern County DWR Crop Survey GIS file HUs 1 5 FSA CLU field boundaries, 1999 Tulare DWR Crop Survey GIS file This analysis summarizes the results of the 27 WRI Report for This analysis summarizes the results of the root zone model for This analysis compares the results of the 27 Water Resources Investigation Report with the root zone model for ,19 It is believed that the field boundaries used for this analysis are based on the 1996 Kings and 1999 Tulare DWR land use survey GIS files. 25,16 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. Only fields within Tulare County and KDWCD are included, to represent the irrigated area. 194,461 The field boundaries consist of FSA CLU field boundaries, which better represent the actual cropped area than the DWR GIS files. Where CLU boundaries were not available, DWR field boundaries were used to fill in the field boundary dataset. Only fields within Tulare County and KDWCD are included. Kaweah Delta Water Conservation District 4 March 213

12 Development of Field Boundaries and Cropping 2 Development of Field Boundaries and Cropping A spatial coverage of field boundaries was developed for the KDWCD service area, and individual field polygons were assigned cropping and irrigation method information. For each field polygon, daily water balance calculations were performed for the 1999 to 21 analysis period, and irrigation events were simulated to estimate the amount of water applied to meet crop irrigation demands. This section describes the development of the field polygon coverage and assignment of cropping and irrigation method attributes. 2.1 Development of Field Boundaries Field boundaries were delineated by combining publicly available polygon coverages in GIS format from the United States Department of Agriculture (USDA) and the California Department of Water Resources (DWR). Specifically, common land unit (CLU) coverages developed by the USDA Farm Services Administration (FSA) on a county by county basis were combined to develop the base field coverage for the study area. Gaps exist in the CLU field coverages for fields not participating in USDA farm programs. These gaps were filled by overlaying the FSA CLU data with field polygons from DWR land use surveys for Kings and Tulare counties. The area encompassed by the field boundary GIS coverage included the KDWCD service area and the area immediately surrounding, but outside of, KDWCD (see Figures 2.1 through 2.4, later in this Section). Fields outside of KDWCD were included to provide a more robust dataset for model calibration and validation. Ultimately, results specific to KDWCD as a whole or by hydrologic unit include only those fields with their centroid located within the KDWCD service area. Hydrologic Units were assigned to each field as shown in Figure Assignment of Cropping and Irrigation Method As described in Section 1, crop evapotranspiration (ET) was calculated based on a combination of remote sensing data and simulation of irrigation events in a daily root zone water balance model. Specifically, crop transpiration, or the T component of ET, was calculated based on remote sensing, and crop evaporation, or the E component of ET, was calculated based on simulated irrigation events and precipitation data. A result of the remote sensing approach is that crop transpiration was estimated with little influence from the assigned crop type for each field. Additionally, crop transpiration is the dominant component of ET, meaning that ET estimates are likewise largely independent of the assigned crop type. Crop evapotranspiration is driven to some extent by the characteristics of the irrigation method and its management, including the area wetted during each irrigation event and the frequency of irrigation. Surface irrigation methods (e.g., furrow or border strip irrigation) typically wet more of the soil surface than microirrigation methods (e.g., drip or microspray); however, surface irrigated fields are typically irrigated less frequently than their micro-irrigated counterparts. As a result, evaporation rates can be similar among surface and micro-irrigated fields, and estimates of evaporation are likewise somewhat independent of the assigned irrigation method. Kaweah Delta Water Conservation District 5 March 213

13 Development of Field Boundaries and Cropping Figure 2.1. Field Boundaries and Hydrologic Units. A key result of the relative insensitivity of the crop ET estimates to crop type or irrigation method (due to the remote sensing approach), is that detailed, accurate assignment of crop types and irrigation methods to each field is not critical to developing reliable estimates of crop ET at the field scale and, more importantly, at coarser scales due to the cancellation of errors in individual field estimates as they are aggregated. The effect of aggregation on uncertainties in ET estimates is evaluated in Section Assignment of Initial Crop Group and Irrigation Method Initial crop types and irrigation methods were assigned to each field based on DWR land use surveys. For Kings County, the land use survey from 23 was used. For Tulare County, the land use survey from 1999 was used. Fields were assigned to one of five general crop groups based on detailed crop descriptions from the DWR surveys. Crops were grouped based on similar cultural and irrigation practices that are expected to result in similar applied water depths relative to crop evapotranspiration amounts. The assignment of crops to crop groups is summarized in Table 2.1. Kaweah Delta Water Conservation District 6 March 213

14 Development of Field Boundaries and Cropping Table 2.1. Crop Groups, Associated Crops, and Initial Cropped Acreage within and Immediately Surrounding KDWCD. Crop Group Crop Acres Crop Group Crop Acres walnuts 27,137 corn 77,992 plums and prunes 9,392 cotton 57,223 peaches and nectarines 4,692 grain 17,321 pistachios 3,882 misc field 8,332 almonds 1,844 dry beans 6,345 deciduous misc deciduous 978 tomatoes 2,891 apples 49 grain corn 2,394 kiwis 156 nursery 2,9 cherries 147 sudan 1,48 field and truck pears 73 cole crops 966 apricots 73 sugar beets 94 SUBTOTAL 48,782 idle 598 alfalfa 48,955 sorghum 567 pasture pasture 3,344 onions and garlic 229 SUBTOTAL 52,299 misc truck 167 citrus 13,192 cucurbits 128 olives 2,759 safflower and sunflower 33 subtropical avocados 46 SUBTOTAL 179,57 eucalyptus 34 GRAND TOTAL 36,39 SUBTOTAL 16,31 vineyards vineyards 9,69 Irrigation methods commonly used for each crop group were identified from the DWR surveys based on review of the number of fields and acres within each crop group utilizing a given irrigation method. Summary statistics of irrigation methods based on the DWR 23 Kings County and 1999 Tulare County crop surveys by crop group are shown in Table 2.2. Kaweah Delta Water Conservation District 7 March 213

15 Development of Field Boundaries and Cropping Table 2.2. Summary Statistics of Irrigation Method by Crop Group based on 23 Kings County and 1999 Tulare County DWR Crop Surveys, KDWCD and Immediately Surrounding Area. Irrigation Number % of Irrigation Number % of Crop Group Method of Fields Acres Area Crop Group Method of Fields Acres Area micro 865 5,651 12% micro 2 4 % none 4 26 % none % deciduous sprinkler 5 5 % sprinkler % pasture surface 4,112 42,351 88% surface ,632 99% unknown unknown Subtotal 5,11 48,782 1% Subtotal 3,82 52,299 1% micro % micro ,3 94% none 154 1,35 1% none 1 4 % sprinkler % subtropical surface % field and truck surface 8,48 169,2 98% unknown unknown 711 7,76 Subtotal 1,36 16,31 1% Subtotal 9, ,57 1% micro 232 2,828 29% vineyards surface 48 6,861 71% Subtotal 64 9,69 1% Grand Total 19,58 36,39 1% Based on the summary statistics depicted in Table 2.2, the following general observations can be made: Deciduous orchard crops in KDWCD are typically irrigated by surface methods (e.g., border strip irrigation) but may also be irrigated by microirrigation (e.g., drip or microspray) Field and truck crops in KDWCD are typically irrigated by surface methods (e.g., furrow irrigation) Pasture in KDWCD is typically irrigated by surface methods (e.g., border strip irrigation) Subtropical orchards in KDWCD are typically irrigated by microirrigation (e.g., drip or microspray) Vineyards in KDWCD are irrigated by a combination of surface (approximately 7% in 1999) and microirrigation (approximately 3% in 1999) methods Based on these observations, all fields were assigned irrigation methods as follows: Deciduous fields were assigned as surface irrigated or micro-irrigated based on the DWR survey information Field and truck fields and pasture fields were assumed to be surface irrigated Subtropical fields were assumed to be micro-irrigated Vineyard fields were assigned as surface irrigated or micro-irrigated based on the DWR survey information The initial assignment of crop groups and irrigation methods resulted in the following seven crop group irrigation method combinations: 1. Deciduous orchards, micro-irrigated 2. Deciduous orchards, surface irrigated Kaweah Delta Water Conservation District 8 March 213

16 Development of Field Boundaries and Cropping 3. Field and truck crops, surface irrigated 4. Pasture, surface irrigated 5. Subtropical, micro-irrigated 6. Vineyards, micro-irrigated 7. Vineyards, surface-irrigated Due to a lack of available cropping and irrigation method information prior to 23 (Kings County) and 1999 (Tulare County), it was assumed that the general crop group and irrigation method did not change between the start of the analysis period (October 1998), and the availability of subsequent cropping and irrigation method data. As discussed previously, due to the reliance on remote sensing, crop ET estimates are relatively insensitive to the assignment of general crop groups and irrigation methods Assignment of Final Crop Group and Irrigation Method Cropping and irrigation method was updated to reflect changes in cropping and irrigation practices over time based on available DWR land use surveys and other cropping information developed by and/or furnished by KDWCD staff to support the analysis. Specifically, updated cropping and irrigation method data were available for Tulare County based on a DWR land use survey completed in 27, and cropping data for 21 were available from a combination of a KDWCD conducted crop survey during 21, as well as cropping data for the Tulare Irrigation District, Lakeside Irrigation Water District, and prominent growers within the KDWCD service area. Based on these data, updated crop groups were assigned to each polygon based on the 27 and 21 cropping data. It was assumed that the general crop type did not change between either 27 or 21, as appropriate, and the end of the analysis period (December 21). This assumption was necessary for those fields for which 21 cropping data were not available. For Tulare County, 27 was the most recent year for which detailed cropping data were available from DWR land use surveys. For Kings County, 23 was the most recent year for which detailed cropping data were available from DWR. The compilation of cropping data for 21 resulted in updated cropping information for 2,261 fields representing 98,32 acres, or about 34% of the study area. Tulare County DWR cropping data for 27 were utilized for 5,55 fields representing 136,684 acres, or about 47% of the study area 1. The source year for the final cropping and irrigation method assignment is shown for each field in Figure 2.2. In the future, cropping could be updated as additional information becomes available, via DWR land use surveys or other sources, though the lack of updated cropping information for 21 for approximately 66% of the study area is not expected to substantially influence or bias the results due to most cropping being updated based on the 27 cropping data for Tulare County; due to the analysis results being relatively insensitive to the assigned crop group-irrigation method, particularly when results for individual fields are aggregated to the hydrologic unit or other scale representing multiple fields; and due to general crop group not changing on many of the fields over time. 1 For the remainder of the Tulare County area, 21 cropping data were available from the sources described previously. Kaweah Delta Water Conservation District 9 March 213

17 Development of Field Boundaries and Cropping Figure 2.2. Source Year for Final Crop and Irrigation Method Assignment. For fields with the crop group changing between the initial crop group assignment and the updated crop group assignment, it is unknown when the general crop type changed for each field. As discussed previously, the crop type assigned to an individual field has a minor impact on the total crop ET estimated for each field over time due to the remote sensing approach. It is expected that crop types change gradually over time. As a result, crop types were updated over time for each field for which the crop group changed between the initial land use data acquisition and the updated land use acquisition. Fields know to change from a given initial crop type to a given final crop type were assigned a random ranking, and crop types were changed gradually over time between the initial and final land use date to simulate gradual changes in land use. Changes in crop acreages over time are shown graphically in Figure 2.3. Kaweah Delta Water Conservation District 1 March 213

18 Development of Field Boundaries and Cropping field and truck, surface pasture, surface deciduous, surface deciduous, micro subtropical, micro vineyard, surface vineyard, micro 3, 25, Acres 2, 15, 1, 5, Figure 2.3. Estimated Acres by Crop Group, 1999 to 21. Similar to the initial cropping and irrigation method information, summary statistics of irrigation method by crop group were developed based on the 27 DWR land use survey. These results are shown in Table 2.3. Table 2.3. Summary Statistics of Irrigation Method by Crop Group based on 27 Tulare County DWR Crop Survey, KDWCD and Immediately Surrounding Area. Crop Group deciduous field and truck Irrigation Number % of Irrigation Number % of Method of Fields Acres Area Crop Group Method of Fields Acres Area micro 424 8,45 23% none % none % sprinkler 13 9 % sprinkler 6 31 % pasture surface ,256 99% surface 1,246 26,737 77% unknown unknown 1 11 Subtotal 81 22,664 1% Subtotal 1,688 34,888 1% micro 84 14,883 9% micro 4 1,196 2% none 7 65 % none % subtropical surface 91 1,499 9% sprinkler % unknown 2 11 surface 1,815 5,452 97% Subtotal 94 16,458 1% unknown 388 7,647 micro 17 3,425 75% Subtotal 2,269 59,734 1% none % vineyards surface 45 1,84 24% Subtotal 157 4,545 1% Grand Total 5, ,288 1% Based on the summary statistics depicted in Table 2.3, the following general observations can be made: Kaweah Delta Water Conservation District 11 March 213

19 Development of Field Boundaries and Cropping Deciduous orchard crops in KDWCD are irrigated by a combination of surface (approximately 77% in 27) and microirrigation (approximately 23% in 27) methods Field and truck crops in KDWCD are typically irrigated by surface methods (e.g., furrow irrigation) Pasture in KDWCD is typically irrigated by surface methods (e.g., border strip irrigation) Subtropical orchards in KDWCD are typically irrigated by microirrigation (e.g., drip or microspray) Vineyards in KDWCD are irrigated by a combination of surface (approximately 75% in 27) and microirrigation (approximately 24% in 27) methods Based on these observations, fields for which the crop group was assigned based on 21 crop data, which did not include irrigation method, were assigned to irrigation method groups as follows: Deciduous fields that were deciduous in the initial cropping data were assigned the same irrigation method Fields converting to deciduous orchards between the initial and final survey were randomly assigned surface and microirrigation methods in proportions to observed results for the 27 DWR survey Field and truck fields and pasture fields were assumed to be surface irrigated Subtropical fields were assumed to be micro-irrigated Vineyard fields that were vineyards in the initial cropping data were assigned the same irrigation method Fields converting to vineyards between the initial and final survey were randomly assigned surface and microirrigation methods in proportion to observed results for the 27 DWR survey All changes in irrigation method between the initial and final land use surveys were assumed to occur at the same time as crop type changes. Initial crop and irrigation method assignments are shown in Figure 2.4. Final crop and irrigation method assignments are shown in Figure 2.5. Changes in crop and irrigation method assignments are shown in Figure Fields Not Evaluated Note that some fields were not included in the analysis for the following reasons: The fields were too small to allow for extraction of satellite data to estimate the amount of green vegetation present over time (described in Section 3) The fields were not cropped during the initial DWR crop surveys Fields excluded for the analysis represent approximately.9% of the total cropped area, or 2,29 total acres. In order to account for water use on these fields in the final results and to provide accurate estimates of crop ET, applied water, and deep percolation volumes, final volumes were scaled upwards by hydrologic unit to account for the cropped acreage not analyzed. Kaweah Delta Water Conservation District 12 March 213

20 Development of Field Boundaries and Cropping Figure 2.4. Initial Crop Group Irrigation Method Categories. Figure 2.5. Final Crop Group Irrigation Method Categories. Kaweah Delta Water Conservation District 13 March 213

21 Development of Field Boundaries and Cropping Figure 2.6. Changes in Crop-Irrigation Method Group Assignments (Initial to Final). Kaweah Delta Water Conservation District 14 March 213

22 Analysis 3 Analysis The amount of green vegetation present over time was estimated for each field polygon based on the Normalized Difference Vegetation Index (), which is calculated using a combination of red and near infrared reflectances, as measured using multispectral satellite sensors onboard the Landsat and MODIS satellites. can vary from -1 to 1 and is typically varies from approximately.15 to for bare soil to for green vegetation with full cover. Negative values typically represent water surfaces. 3.1 Image Selection Landsat images are preferred due to their greater spatial resolution (3 meter pixels, approx. acres in size) as compared to MODIS (25 meter pixels, approx. 15 acres in size). A total of 154 raw satellite images were selected and converted to spanning the period from September 1998 to January 211. Images for September through December 1998 were selected to condition soil moisture content within the daily root zone water balance model to provide a reasonable estimate of stored soil moisture on January 1, The final image selected was for December 6, 21. values were assumed to remain constant from December 6 through the end of the analysis period, December 31, 21. Of the images selected, 89 were from the Landsat 5 satellite, 52 were from the Landsat 7 satellite (first available in 21), and 13 were from MODIS. Following May 23, a malfunction in the Landsat 7 satellite resulted in data gaps in the Landsat 7 imagery; as a result, Landsat 5 was preferred after May 23, unless cloud free images were not available for the Landsat 5 satellite overpass dates. An example time series of imagery for 21 for the KDWCD service area is shown in Figure 3.1. In the figure, areas with little or no green vegetation present are shown in brown, and areas with green vegetation are shown in green. Kaweah Delta Water Conservation District 15 March 213

23 Time Series Evapotranspiration and Analysis Figure Time Series for KDWCD. Kaweah Delta Water Conservation District 16 March 213

24 Analysis 3.2 Filling Cloud Gaps with MODIS In general, Landsat images were selected to avoid clouds; however, in order to provide adequate temporal coverage, some images containing clouds were required. Landsat images are available every 8 days at best; due to sensor issues and other factors, cloud free Landsat images are not available for a month or more in some cases, particularly during the winter months. In contrast, MODIS imagery is available for 2 overpasses per day. Based on review and selection of available imagery, eight Landsat images with substantial cloud gaps (in addition to the cloud-free images) were selected to maximize the coverage of high resolution satellite imagery over the period of analysis for the KDWCD service area 2. For each of the 8 Landsat image dates covering the full analysis period containing clouds, MODIS imagery was selected as close in time as possible to allow for the cloud gaps in the Landsat imagery to be filled by the MODIS imagery. For each cloud-filled image, values for Landsat and MODIS were compared at the section scale (1 square mile), and a correction was developed via linear regression to adjust the MODIS values to match the Landsat values. The general form of the regression equation developed for each image was as follows (Equation 3.1): = + [3.1] where adj is the corrected MODIS used to fill cloud gaps in the Landsat imagery, MODIS is the raw MODIS value, and m and b are constants determined empirically for each pair of Landsat and MODIS images. Differences in for a given location at a given time between the two satellites may result from differences in sensor ranges, calibrations, and other calculations used to convert radiances observed at the satellites into surface reflectances. Additionally, some difference may occur due to changes in for individual fields that occurred between the Landsat image date and the selected MODIS image date. Cloud-free MODIS images were selected as close in time as possible to the Landsat images for which cloud gap filling was required. The effect of differences in time between the Landsat and MODIS image dates is expected to be minor. Once the MODIS imagery was calibrated to match Landsat, gaps in the Landsat data were filled pixel by pixel based on the MODIS values using Equation 3.1 with the slope and intercept of the regression equation (m and b, respectively) determined empirically for each image date. The process is illustrated for the Landsat image from January 22, 22 in Figure 3.2. In the upper left of the figure, the Landsat image and cloud gaps are shown. The MODIS image from January 24, 22 is shown in the lower left. The regression of Landsat as a function of MODIS is shown in the upper right. As indicated, Landsat tends to be less than MODIS at a given location across the full range of observed. Scatter plots of the eight regressions for gap filling of individual MODIS images are shown in Figure 3.3. The slope and intercept of each regression is summarized in Table 3.1, along with the R-squared value for the regression. 2 For some image dates, small areas of clouds affecting few fields existed. In these cases, the image date was estimated by interpolating values between the prior and following images. This was necessary for a small number of fields over a small number of image dates and is not expected to significantly impact the results. Kaweah Delta Water Conservation District 17 March 213

25 Analysis Landsat 1/22/2 Landsat / / y =.9389x 662 R² = MODIS MODIS 1/24/2 Combined Figure 3.2. Illustration of Process for Fill Cloud Gaps in Landsat Imagery Using MODIS. Kaweah Delta Water Conservation District 18 March 213

26 Landsat Landsat 2/2/ /5/ y = 9x 691 R² = MODIS y = 278x.125 R² = MODIS 1/22/22 Landsat /3/25 Landsat y =.9389x 662 R² = MODIS y = 63x 737 R² = MODIS Landsat Landsat 2/2/ /7/28 y =.9714x.1739 R² = MODIS y = 929x 727 R² = MODIS Landsat 2/2/ y = 683x.156 R² = MODIS 2/18/29 y =.9458x 98 R² =.93 Figure 3.3. Regression Equations to Adjust from Eight MODIS Images to Landsat Values. Landsat MODIS Kaweah Delta Water Conservation District 19 March 213

27 Analysis Table 3.1. Regression Parameters and R-Squared Values for Adjustment of MODIS to Landsat. Landsat Image Date Slope, m Intercept, b R Squared 2/2/ /22/ /2/ /5/ /3/ /7/ /2/ /18/ Combined For periods when no Landsat imagery was available for more than approximately 45 days, MODIS imagery was used as a substitute for Landsat imagery. MODIS was used as a substitute for Landsat for five of 154 image dates over the twelve year analysis period. In order to estimate Landsat for each of the five MODIS dates without any available Landsat imagery close in time, linear regressions for the eight cloud-filled Landsat images were reviewed. It was observed that the linear regressions to adjust MODIS to match the Landsat values are relatively consistent over time, with Landsat being consistently less than MODIS. As a result, data from the eight regressions were combined to provide a single adjustment equation that could be used to adjust the remaining five MODIS images to approximate values that would be expected from Landsat if imagery were available. The combined regression for all eight images is shown in Figure 3.4. The regression for the combined eight images resulted in a slope of 58, an intercept of -81, and an R-squared value of.74. Gap filling of clouds using MODIS and substitution of MODIS images for Landsat during prolonged periods of Landsat unavailability is expected to have a relatively minor affect on overall model results. A total of 133 of 141 Landsat images were cloud free (94 percent of Landsat images), and only five MODIS images were used as a substitute for Landsat (three percent of individual image dates). The benefit of gap filling and image substitution is to provide a complete, relatively continuous record of values describing the amount of green vegetation present at each field. Kaweah Delta Water Conservation District 2 March 213

28 Analysis Landsat y = 581x 813 R² = MODIS Figure 3.4. Combined Regression Equation to Calibrate from Eight MODIS Images to Landsat Values. 3.3 Extraction of Values by Field and Development of Time Series Results Following the preparation of imagery spanning the analysis period, mean was extracted from the imagery for each field for each image date. These values were then interpolated across the full analysis period from October 1, 1998 to December 31, 21 to provide a daily time series of mean values for each field. Interpolated values for selected fields are provided in Figures 3.5, 3.6, 3.7, 3.8, and 3.9 for the period 1999 through 21. These Figures provide an example of time varying for individual fields over time. The figures present on an annual basis, from January 1 to December 31 of each year. For each field, a location map is provided showing the location within KDWCD s hydrologic unit, along with a color satellite image of the field. The color imagery was acquired in 212 as part of the National Agricultural Imagery Program of the USDA. Figures 3.5 and 3.6 illustrate the ability of the remote sensing approach to account for both changes in cropping over time and to account for the presence of double- and triple-cropping. For example, Field 552 (shown in Figure 3.5) was identified based on available cropping data for Tulare County for 1999 as a pasture field. The profile for 1999 is consistent with typical pasture and shows the effect of grazing, cutting, crop stress, or other factors that influence the field-specific amount of green vegetation present. In 2 and 21 the field appears to have been converted to field crops, with double-cropping apparent in both years. Because the estimation of ET is strongly driven by transpiration and because surface irrigation practices are similar for pasture and field crops, the change from pasture to field crops does not greatly affect the ET and applied water estimates of the model. From 22 to 26, the field appears to convert back to pasture, followed by double-cropping in 27, 28, and 29. In 21, it is Kaweah Delta Water Conservation District 21 March 213

29 Analysis not immediately clear whether triple-cropping of field crops or field crops followed by replanting of pasture occurred, but the profile the primary driver of crop ET and applied water is apparent. Similarly, year to year variations in cropping are apparent for Field 6891 (Figure 3.6), classified as a field and truck crop field. Between 29 and 28, the field was single-cropped; however the timing and shape of the profile varies substantially from year to year, suggesting changes in the specific crop grown, cultural practices, or year-to-year variation in crop physiology as influenced by weather. In 29, the field was double-cropped, followed by a single crop in 21. The current methodology of relying on periodic cropping data from ground-based surveys, which is often unable to identify double- or triplecropping, also is unable to capture the actual variability in crop growth and ET at the field scale over time. The profile for a deciduous orchard (Field 3614) is shown in Figure 3.7. The profile is similar from year to year, as expected, however variation in the profile and thereby crop transpiration is apparent, with some years exhibiting longer, more sustained tree canopy and/or cover crops than others. Traditional crop coefficient approaches assume similar timing and magnitude in crop water use and are unable to capture the observed variability at the field scale. Figure 3.8 provides the profile for a citrus field (Field 2594). As expected, the canopy of the evergreen citrus orchard is quite steady over time and across years. The effects of pruning in November and December of many years are apparent and capture the variability in the amount of green vegetation present. The profile for a vineyard (Field 4741) is shown in Figure 3.9. Year-to-year variability, possibly due to differences in cultural and irrigation practices from year to year is apparent. Additionally, the presence of a cover crop or weeds during January and February of many years is apparent. Estimation of ET by winter vegetation through remote sensing provides improved estimates of the amount of winter precipitation depleted during the off-season and the corresponding need for irrigation through the application of a daily root zone water balance as described in Section 4 of this report. Kaweah Delta Water Conservation District 22 March 213

30 Analysis Image Date Interpolated Image Date Image Date 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 Figure 3.5. Sample Time Series for Selected 75-Acre Pasture Field (Field 552). Kaweah Delta Water Conservation District 23 March 213

31 Analysis Image Date Interpolated Image Date Image Date 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 Figure 3.6. Sample Time Series for Selected 166-Acre Field and Truck Crop Field (Field 6891). Kaweah Delta Water Conservation District 24 March 213

32 Analysis Image Date Interpolated Image Date Image Date 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 Figure 3.7. Sample Time Series for Selected 24-Acre Deciduous Orchard Field (Field 3614). Kaweah Delta Water Conservation District 25 March 213

33 Analysis Image Date Interpolated Image Date Image Date 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 Figure 3.8. Sample Time Series for Selected 27-Acre Subtropical Orchard Field (Field 2594). Kaweah Delta Water Conservation District 26 March 213

34 Analysis Image Date Interpolated Image Date Image Date 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/1 5/1 6/3 8/3 1/29 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/ Image Date Interpolated Image Date Interpolated Image Date Interpolated 1/1 3/1 5/1 6/3 8/3 1/29 12/29 1/1 3/2 5/2 7/1 8/31 1/3 12/3 1/1 3/2 5/2 7/1 8/31 1/3 12/3 Figure 3.9. Sample Time Series for Selected 57-Acre Vineyard Field (Field 4741). Kaweah Delta Water Conservation District 27 March 213

35 Analysis 3.4 Development of Relationships to Estimate Basal Crop Coefficient from Basal crop coefficients (K cb ) describe the ratio of crop transpiration to reference evapotranspiration (ET o ) as estimated from a ground-based agronomic weather station. By combining K cb, estimated from, with an evaporation coefficient (K e ), it is possible to calculate a combined crop coefficient (K c = K cb + K e ) over time 3. By multiplying K c by ET o, crop evapotranspiration (ET c ) can be calculated. For this analysis, ET o, K cb, K e, and ET c (synonymous to actual ET, ET a ) were estimated for each field on a daily time step from October 1, 1998 to December 31, 21. Mean daily values for each field were converted to basal crop coefficients based on cropping information from the 27 Tulare County crop survey conducted by DWR, combined with an analysis of actual evapotranspiration (ET a ) by crop conducted using the Surface Energy Balance Algorithm for Land (SEBAL ) for 27 (Bastiaanssen et al., 25; SNA, 29). Specifically, a relationship between actual basal crop coefficients estimated using SEBAL and field-scale mean values was developed and applied to calculate daily basal crop coefficients for each field over time 4. The calibration and validation of the relationship, as described in Section 5 was performed for the full KDWCD service area, including the portion of the service area in Kings County (for which detailed 27 cropping data were not available) to avoid any potential limitations to applying the model for the full service area. Regardless, potential biases for the Kings County portion of the service area would not be expected because cropping and irrigation practices in the Kings County portion of KDWCD s service area are similar to adjacent cropped areas in Tulare County. In order to determine the -K cb relationship for each crop type, a scatter plot was developed for the top ten crops in KDWCD comparing to K c as calculated based on the 27 SEBAL analysis (Figure 3.1) 5. Eight images from late April through Early August were selected to focus on the primary irrigation season when green vegetation is present. Note that because SEBAL determines actual ET, K c is denoted K cs, illustrating that the crop coefficient reflects the effects of water stress and other factors, if present, on crop ET. As illustrated in Figure 3.1, K cs varies substantially at lower values. Relatively large K cs values at low reflect the effect of evaporation on K cs (K e is large relative to K cb ), as the amount of vegetation cover increases, the soil surface is shaded, and less evaporation occurs. Based on the observed relationship between and K cs, an equation relating to K cb of the following form (Equation 3.2) after Er-Raki et al. (27) was parameterized: 3 The estimation of Ke is based on a daily 2-stage evaporation model presented in FAO Irrigation and Drainage Paper No. 56 (Allen et al. 1998), as described in Section 4. 4 This relationship is developed based on comparison of the combined crop coefficient to for individual fields, but represents only the transpiration component of ET. Thus, the relationship developed predicts the basal crop coefficient, K cb. 5 The top ten crops represent approximately 91% of the cropped area and were used to develop an initial estimate of the parameters for Equation 3.2, providing a representative first estimate of the parameters for KDWCD as a whole. To the extent that the relationship is biased due to including only the Tulare County area (which it likely is not, as discussed previously), the bias is removed through calibration of the K cb,max value for the full service area as described in Section 5. Kaweah Delta Water Conservation District 28 March 213

36 Analysis Kcb,max Fields with evaporation occurring in addition to transpiration min Kcb vs. (basal crop coefficient as function of ) max Figure 3.1. Comparison of and Kcs, April to August 27, Top 1 KDWCD Crops Combined max observed Kcb Kcb, max 1 [3.2] max min As indicated in Figure 3.1, min was selected to be.16, corresponding to the value at which no green vegetation is present, and transpiration is zero. Fields with bare soil evaporation may have an around.16 but K cs greater than zero because although K cb is zero, K e is greater than zero. Similarly, the region of Figure 3.1 encompassed by a dotted line shows fields with a combination of transpiration and evaporation. As and K cb increase, the relative amount of K cs representing evaporation decreases (due to reduction in the K e portion of K cs ), so that at the peak value, K cs is equivalent to K cb. As indicated in Figure 3.1, initial values of K cb,max and max were selected to be 1.15 and 5, respectively, corresponding to the maximum value of K cb expected at the maximum value of expected. max was selected to be slightly greater than the maximum values observed for the 27 analysis to allow for the possibility that could reach 5 for some fields at some time over Kaweah Delta Water Conservation District 29 March 213

37 Analysis the full twelve year analysis period. The resulting equation 3.2 denoting the relationship between and K cb is shown as a yellow line in Figure 3.1. As part of the calibration process, described later in this report, it was observed that there appear to be differences in the optimal value of K cb,max from Equation 3.2 across crop-method groups. These differences could result from differences in the spectral characteristics of the crops themselves as they relate to the actual basal crop coefficient or could be influenced by any biases in estimation of the evaporation component of total ET as estimated by the model for each irrigation method. In order to avoid biases in total ET estimates for each crop-method group, the parameters for Equation 3.2 were adjusted for each crop-method group as part of the calibration process. Evidence of differences in K cb for a given values across crops are shown in Table 3.2, which compares mean K cs values for varying ranges of by general crop type in KDWCD. This analysis represents a subset of fields within the Tulare County portion of KDWCD. Field boundaries from the DWR land use surveys were buffered inward by 45 meters to ensure that values were extracted from field centers and that the SEBAL results were not influenced by heat transfer processes occurring adjacent to the fields. Also, the results represent ten SEBAL image dates from March through October 27, and each field may be included multiple times in the summary statistics. Thus the n values in the table represent the number of field-image dates within each range by crop group. Note that for fields with ranging from to.7, K cs values for vineyards average 2, while K cs values for field and truck crops average.96. This may be due to increased levels of water stress imposed as part of irrigation management of vineyard crops as compared to field and truck crops that are not reflected in. Table 3.2. Mean Actual Crop Coefficients by Range for KDWCD General Crop Types based on 27 SEBAL ET Analysis. Mean SEBAL Kcs with Increasing Number Buffered Crop Group of Fields Acres n 1 Kcs n 1 Kcs n 1 Kcs Deciduous 1,144 19,33 2,827 2, Field and Truck 1,714 5,89 1,41 8 2, ,76 Pasture , ,85.95 Subtropical 292 2, Vineyard 134 2, TOTAL 3,95 98,84 5, , , ) The n values represent the number of field-image dates within each range. Kaweah Delta Water Conservation District 3 March 213

38 Model Development 4 Model Development This section describes the development of the daily root zone water balance model used to estimate the following surface layer fluxes of water into and out of the root zone, along with the amount of stored applied water and precipitation within the root zone over time: Precipitation (Pr), estimated from local weather stations; ET of applied water (ET aw ), portion of total crop ET (ET c ) derived from applied water; ET of precipitation (ET pr ), portion of total crop ET (ET c ) derived from precipitation; Runoff of precipitation (RO pr ); Tailwater (TW), assumed to be zero; Applied Irrigation Water (AW); Subsurface Inflow and Outflow, assumed to be zero; Deep percolation of applied water (DP aw ); and Deep percolation of precipitation (DP pr ). In general, the model is consistent with typical root zone water balance models developed for irrigation scheduling purposes, such as described in FAO Irrigation and Drainage Paper No. 56: Crop Evapotranspiration (Allen et al. 1998). The primary departure of the model described herein is that rather than relying on published crop coefficient values to estimate total crop ET, crop coefficients are derived based on actual observations of the amount of green vegetation present via satellite-based remote sensing of, as described in the previous section. A conceptual diagram of the various surface layer fluxes of water into and out of the crop root zone is provided in Figure 4.1. Precipitation ET aw ET pr RO pr Applied Water TW Subsurface In Subsurface Out DP aw DP pr Figure 4.1. Conceptualization of Fluxes of Water Into and Out of the Crop Root Zone. Kaweah Delta Water Conservation District 31 March 213

39 Model Development 4.1 Estimation of Daily Reference Evapotranspiration Daily reference evapotranspiration (ET o ) was estimated based on information from California Irrigation Management Information System (CIMIS) weather stations. ET o provides a means of estimating actual crop evapotranspiration over time for each field. Characteristics of nearby weather stations with data available during the period of analysis are summarized in Table 4.1. For each station, the CIMIS station ID, location, elevation, start date, end date, and distance and direction from the center of the KDWCD service area are provided, along with a brief assessment of site conditions for estimation of ET o. The term fetch in the site conditions assessment refers to the distance of grass surrounding each station, which is necessary to condition weather conditions at the sensors to allow for accurate estimation of ET o. Station ID Table 4.1. Characteristics of Nearby CIMIS Agronomic Weather Stations. Location Elevation (ft) Start Date End Date 15 Stratford 193 1/29/1982 Active 33 Visalia 35 1/5/1983 3/1/27 86 Lindcove 48 5/31/1989 Active 169 Porterville 4 8/2/2 Active 182 Delano 3 3/21/22 Active Distance and Direction 26 miles WSW 1 miles E 21 miles ENE 22 miles SE 31 miles SSE Site Conditions Relatively good fetch. Relatively poor fetch. Relatively poor fetch. Relatively good fetch. Little information describing site. As indicated in Table 4.1, the closest station to the KDWCD service area was the Visalia Station (33). This station was located within the KDWCD service area but was discontinued in 27. Additionally, the site provided relatively little fetch to support accurate estimation of ET o. Based on review of available weather stations, the Porterville station (169) was selected based on it being relatively close to the KDWCD service area, at a similar elevation to the KDWCD service area, having relatively good fetch, and having available data for the majority of the analysis period. Individual parameters from the available data including incoming solar radiation, air temperature, relative humidity, and wind speed were quality-controlled according to the procedures of Allen et al. (25). The quality-controlled data were then used to calculate daily ET o for the available period of record. CIMIS data for Porterville were not available prior to August 2. As a result, it was necessary to estimate ET o for the period from October 1, 1998 to August 1, 2. ET o for Porterville was estimated by developing a linear regression to estimate Porterville ET o using quality-controlled data from the Stratford Kaweah Delta Water Conservation District 32 March 213

40 Model Development CIMIS station for the period of overlapping data availability. The results of the linear regression are provided in Figure y = 46x + 8 R² = Porterville ETo (in/d) Stratford ETo (in/d) Figure 4.2. Linear Regression to Estimate Daily Porterville ET o Based on Stratford CIMIS Station. As indicated in Figure 4.2, there is a strong correlation between ET o at Porterville and Stratford, with Porterville ET o being generally less than Stratford ET o as ET o increases. The SEBAL crop coefficient (K cs ) values shown previously in Figure 3.1 were determined by dividing SEBAL actual ET by the quality-controlled Porterville ET o values to avoid any biases in estimating ET using the daily root zone water balance model that could occur if the crop coefficients were calculated based on ET o data from another weather station. 4.2 Estimation of Daily Precipitation To develop a time series of daily precipitation within the KDWCD service area, available National Climatic Data Center (NCDC) and CIMIS weather stations were identified within and surrounding the KDWCD service area, and precipitation records for the period of analysis were reviewed. Weather stations evaluated and available periods of record are summarized in Table 4.2. Kaweah Delta Water Conservation District 33 March 213

41 Model Development Table 4.2. Summary of Weather Stations Evaluated for Development of Daily Precipitation Records. Station Name Type Latitude Longitude Elevation (ft) Start Date End Date Corcoran NCDC /1/1948 Active Hanford NCDC /19/1998 Active Visalia NCDC /1/1895 Active Porterville CIMIS /2/2 Active Visalia CIMIS /5/1983 3/1/27 Distance and Direction 16 miles SW 14 miles W 6 miles NE 22 miles SE 1 miles E Comments Located at Corcoran Irrigation District Located at Hanford Municipal Airport Located near Visalia Fire Station 51 in Central Visalia Located on a private farm Located at experimental plots of agchemical company Annual precipitation totals for the 1998 to 21 period of study are shown in Figure 4.3. For 1998, October to December totals are provided, while calendar year totals are provided for the other years. Based on the data, annual totals are similar in each year, with the exception of the Visalia NCDC station, which shows substantially greater precipitation in some years, suggesting the possibility of inaccurate records. Review of the daily data suggested that high annual precipitation values result from data entry errors on relatively few days. Additionally, the Corcoran NCDC station appears to receive substantially less precipitation than the other stations in most years, suggesting it is not representative of the KDWCD service area as a whole. In order to provide a daily precipitation record representative of the KDWCD service area, the precipitation from the Visalia CIMIS station, which agrees closely with the Hanford and Porterville NCDC station in most years with available data from all three stations, was selected for the period for which data were available (1998 to 26). For the remainder of the analysis period (27 to 21), average daily precipitation from the Hanford and Porterville stations were used. In the future, development of spatially distributed precipitation estimates could be considered to refine precipitation estimates at the field and hydrologic unit scale; however, the relatively good agreement of annual precipitation among the Visalia CIMIS, Hanford NCDC, and Porterville NCDC stations for the period of overlapping data suggests that precipitation is relatively uniform within the KDWCD service area. The resulting annual precipitation is shown as a black line in Figure 4.3. Kaweah Delta Water Conservation District 34 March 213

42 Model Development Corcoran Hanford Visalia Porterville Visalia CIMIS Final Data Precipittation, in Figure 4.3. Annual Precipitation Totals for Selected Precipitation Stations. 4.3 Estimation of Root Zone Water Balance Parameters Root zone parameters that influence the amount of available soil moisture storage were estimated based on crops and soils present in KDWCD. Crop parameters of interest include root depth, NRCS curve number 6, and management allowable depletion (MAD). Root depth was estimated by crop group based on published values and a representative mix of individual crops within each crop group for KDWCD, as determined based on the 27 DWR crop survey. Curve numbers were estimated based on values published in the NRCS National Engineering Handbook, which provides estimates based on crop type and condition. MAD values by crop were estimated based on values published in FAO Irrigation and Drainage Paper No. 56 (Allen et al., 1998). Soil hydraulic parameters of interest include field capacity (% by vol.), wilting point (% by vol.), saturated hydraulic conductivity (ft/day), total porosity (% by vol.), and the pore size distribution index (λ, dimensionless). These parameters were estimated by first determining the depth-weighted average soil texture (sand, silt, clay, etc.) based on available NRCS soil surveys. Then, the hydraulic parameters were estimated using hydraulic pedotransfer functions developed by Saxton and Rawls (26). Next, hydraulic parameters were adjusted within reasonable physical ranges for each soil texture so that the modeled time required for water to drain by gravity from saturation to field capacity agreed with typically accepted agronomic values. Required drainage time ranged from 4 days for sand to 13 days for clay soils. Unsaturated hydraulic conductivity (e.g. deep percolation) within the root zone was modeled based on a modified version of the equation developed by Campbell (1974). The Campbell equation was modified to force the deep percolation rate to zero once the soil moisture content fell to field capacity after a 6 The curve number runoff estimation method developed the Natural Resources Conservation Service (NRCS) was used to estimate runoff from precipitation in the model. For additional information, see NRCS NEH Chapter 2 (NRCS, 1993). Kaweah Delta Water Conservation District 35 March 213

43 Model Development precipitation or irrigation event, which is consistent with the agronomic definition of field capacity. This modification was needed to avoid substantial continued drainage of applied irrigation water following irrigation that resulted in increased irrigation requirements and reduced application efficiency as compared to accepted values by crop and irrigation method. 4.4 Estimation of Annual Crop Consumptive Use Fraction and Other Irrigation Parameters As described previously, the crop consumptive use fraction (CCUF) is the ratio of ET of applied water to total applied irrigation water. Values of the CCUF were estimated based on typical application efficiencies by irrigation method reported by Canessa et al. (211). Based on these data, the following CCUF values were estimated by irrigation method: Furrow irrigation.7 Border irrigation.75 Microirrigation 5 Based on the typical cropping pattern in KDWCD for the analysis period, the estimates above result in an overall average CCUF for the area of approximately.73. This estimate, while subject to uncertainty, is consistent with typical estimates for similar irrigated areas. Other irrigation parameters estimated include the fraction of ground surface wetted during irrigation, which affects soil evaporation rates; the maximum depth of irrigation water applied per day, which varies by irrigation method and affects the number of days required to refill the root zone for a given MAD, soil waterholding capacity, and root depth; and the percentage of applied water that runs off as tailwater during irrigation. Based on discussion with KDWCD staff, it is believed that although tailwater may be generated for individual fields as part of irrigation, very little if any tailwater leaves the service area. Tailwater produced during irrigation in KDWCD is typically recaptured and reused at the field, is reused by downstream irrigators, or enters drains and seeps into the groundwater system. 4.5 Development of Database Model Model calculations were initially implemented in an MS Excel spreadsheet for a sample of fields to test and refine individual model calculations. The spreadsheet was set up based on the dual crop coefficient approach described in FAO Irrigation and Drainage Paper No. 56 (Allen et al., 1998), including modeling of 2-stage soil evaporation, and was modified to incorporate the estimation of K cb based on the analysis described previously. In order to accommodate the modeling of approximately 8, fields within KDWCD over the analysis period of over twelve years (4,475 days, resulting in approximately 36 million individual daily time step records), it was necessary to design a database application to perform model calculations and store the results. Ultimately, the model was implemented in MicroSoft SQL Server. Once implemented in SQL server, daily model results were compared to spreadsheet model results for 49 selected fields representing the range of crops and irrigation methods within KDWCD for the full twelve Kaweah Delta Water Conservation District 36 March 213

44 Model Development year period. Once SQL Server results were found to agree exactly with the spreadsheet model results, confirming that the SQL procedures were programmed correctly, the model structure was finalized. Example daily root zone model results for selected fields for 29 and 21 are shown in Figures 4.4 through 4.8. In each figure, the time series of the basal crop coefficient K cb and combined crop coefficient K cs is provided, followed by the time series of crop transpiration, total ET, applied water, and precipitation. To the left of each figure the location of the field is provided. Kaweah Delta Water Conservation District 37 March 213

45 Model Development Kc, Kcb /1 2/2 4/11 5/31 7/2 9/8 1/28 12/17 Kc Kcb T, ET (in) /1 1/31 3/2 4/2 5/2 6/2 7/2 8/1 9/1 1/1 11/1 12/1 ETc T Irr P RO Irr, Precip (in) Figure 4.4. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Surface Irrigated Deciduous Orchard Field (Field 3614), 21. Kaweah Delta Water Conservation District 38 March 213

46 Model Development Kc, Kcb /1 2/2 4/11 5/31 7/2 9/8 1/28 12/17 Kc Kcb T, ET (in) /1 1/31 3/2 4/2 5/2 6/2 7/2 8/1 9/1 1/1 11/1 12/1 ETc T Irr P RO Irr, Precip (in) Figure 4.5. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Surface Irrigated Field and Truck Field (Field 3593), 21. Kaweah Delta Water Conservation District 39 March 213

47 Model Development Kc, Kcb /1 2/2 4/11 5/31 7/2 9/8 1/28 12/17 Kc Kcb T, ET (in) /1 1/31 3/2 4/2 5/2 6/2 7/2 8/1 9/1 1/1 11/1 12/1 ETc T Irr P RO Irr, Precip (in) Figure 4.6. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Surface Irrigated Pasture Field (Field 3372), 21. Kaweah Delta Water Conservation District 4 March 213

48 Model Development Kc, Kcb /1 2/2 4/11 5/31 7/2 9/8 1/28 12/17 Kc Kcb T, ET (in) /1 1/31 3/2 4/2 5/2 6/2 7/2 8/1 9/1 1/1 11/1 12/1 ETc T Irr P RO Irr, Precip (in) Figure 4.7. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Micro-Irrigated Subtropical Field (Field 446), 21. Kaweah Delta Water Conservation District 41 March 213

49 Model Development Kc, Kcb /1 2/2 4/11 5/31 7/2 9/8 1/28 12/17 Kc Kcb T, ET (in) /1 1/31 3/2 4/2 5/2 6/2 7/2 8/1 9/1 1/1 11/1 12/1 ETc T Irr P RO Irr, Precip (in) Figure 4.8. Daily Rootzone Model Kcb, Kcs, Applied Water, and Precipitation for Selected Micro-Irrigated Vineyard Field (Field 4741), 21. Kaweah Delta Water Conservation District 42 March 213

50 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model 5 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model 5.1 Model Calibration Calibration of the model was accomplished as a two-step process. First, total actual ET estimated based on remotely sensed was compared at the field scale to corresponding actual ET estimated by SEBAL for 27, 28, and 29. Based on the comparison, adjustments were made to model parameters including the K cb,max parameter for each crop group, which affects transpiration, and the fraction wetted for each irrigation event, which affects evaporation. The 27, 28, and 29 SEBAL coverages covered different time periods within each year. For the 27 analysis, fourteen satellite images were processed covering the period from January through December as summarized in Table 5.1. The purpose of evaluating a full twelve-month period was to determine total calendar year actual ET to support comparisons to other available estimates of ET for KDWCD. Two MODIS images with relatively coarser resolution were selected for November and December of 27, as no cloud-free Landsat images were available (SNA, 29). For 28, fourteen images spanning the period from February to November were selected as summarized in Table 5.2. The primary objective of the 28 analysis was to evaluate crop coefficients in the Southern San Joaquin Valley as part of a Proposition 5 Water Use Efficiency study conducted by the University of California and SEBAL North America with funding from the California Department of Water Resources. For 29, eight images spanning the period from mid-march to mid-october were selected as summarized in Table 5.3. The primary objective of the 29 analysis was to support the refinement of economic models used by researchers in the Department of Natural Resource Economics at the University of California at Davis to evaluate economic responses to surface water supply limitations. Table 5.1. Analysis Periods and Image Selection for 27 SEBAL Analysis. Period Start End Length (days) Image Date Satellite 1 1 Jan 31 Jan Jan Landsat Feb 3 Mar Feb Landsat Mar 31 Mar Mar Landsat Apr 2 Apr 2 1 Apr Landsat Apr 8 May Apr Landsat May 5 Jun 28 2 May Landsat Jun 29 Jun Jun Landsat Jun 23 Jul 24 7 Jul Landsat Jul 16 Aug 24 8 Aug Landsat Aug 5 Sep 2 24 Aug Landsat Sep 3 Oct Sep Landsat Oct 5 Nov Oct Landsat Nov 3 Dec Nov MODIS Terra 14 4 Dec 31 Dec Dec MODIS Terra Kaweah Delta Water Conservation District 43 March 213

51 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model Table 5.2. Analysis Periods and Image Selection for 28 SEBAL Analysis. Period Start End Length (days) Image Date Satellite 1 1 Feb 3 Mar Feb Landsat Mar 4 Apr Mar Landsat Apr 6 May 32 2 Apr Landsat May 3 May May Landsat May 15 Jun 16 7 Jun Landsat Jun 27 Jun Jun Landsat Jun 13 Jul 16 1 Jul Landsat Jul 2 Aug 2 25 Jul Landsat Aug 18 Aug 16 1 Aug Landsat Aug 3 Sep Aug Landsat Sep 19 Sep Sep Landsat Sep 5 Oct Sep Landsat Oct 29 Oct Oct Landsat Oct 3 Nov Nov Landsat 5 Table 5.3. Analysis Periods and Image Selection for 29 SEBAL Analysis. Period Start End Length (days) Image Date Satellite 1 15 Mar 14 Apr Mar Landsat Apr 15 May 31 3 Apr Landsat May 15 Jun May Landsat Jun 11 Jul 26 3 Jul Landsat Jul 4 Aug Jul Landsat Aug 5 Sep 32 2 Aug Landsat Sep 29 Sep Sep Landsat Sep 15 Oct 16 7 Oct Landsat 5 Despite the SEBAL analyses from 27, 28, and 29 covering different time periods within each year, all analyses include the primary growing period from March to October when approximately 8 to 9 percent of annual ET occurs. As a result, it is believed that the SEBAL data provide a robust dataset for model calibration and validation. Relatively less data available during January, February, November, and December may lead to somewhat greater uncertainty in model results during the winter months; however, because the potential for evapotranspiration in these months is small, there is little effect on overall model accuracy. The second step of the calibration process was to calibrate the daily root zone model such that the CCUF on an annual basis by crop group and irrigation method equaled the estimated CCUF values based on Canessa et al. (211). Model results from the final ET calibration model run were used to calculate the Kaweah Delta Water Conservation District 44 March 213

52 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model CCUF on an annual basis by crop group and irrigation method; then, the event application efficiency 7 was adjusted iteratively such that the seasonal ratio of modeled ET of applied water to total applied water was approximately equal to the target value. CCUF on an annual basis may differ from application efficiency for a given irrigation due to the potential for the crop to extract and transpire stored soil moisture during the period that soil moisture storage is above field capacity and is draining gravimetrically to field capacity and due to the dynamics of applied water storage and flushing of applied water out of the root zone by precipitation over the course of a given year, including the period outside of the primary growing season. As a final calibration step, adjustments were made to soil parameters affecting evaporation rates for heavy soils, which were found to differ somewhat from the SEBAL results. Specifically, the total evaporable water (TEW) and readily evaporable water (REW) parameters as described by Allen et al. (1998) were modified to improve agreement between SEBAL ET and ET estimated from the model. Remaining differences between SEBAL ET and modeled ET for these soils are likely due to a relatively small sample size for the heavy soil types (e.g., clay loam, silt loam, and silty clay), and random error in the individual field-scale estimates of actual ET. Calibration was performed for one half of the approximately 8, modeled fields within KDWCD, selected at random and uniformly distributed across the service area. The remaining fields were used to validate the model following the final model run. Validation was performed by crop-irrigation method group across KDWCD and by hydrologic unit. A profile of the number of fields and acres for each cropmethod group in the calibration and verification datasets, as compared to the District as a whole is provided in Table 5.4 for 27. As demonstrated by Table 5.4, the number and portion of area represented by the calibration fields is representative of the study area as a whole. Table 5.4. Summary of Calibration Field Group, Validation Field Group, and District-Wide Cropping Based on 27 Crop-Method Groups. Calibration Fields Validation Fields Study Area Percent No. of Percent No. of Acres of Area Fields Acres of Area Fields Acres No. of Fields Percent of Area Crop Method Group Deciduous, Micro 181 5,1 3% 197 4,674 3% 378 9,674 3% Deciduous, Surface 841 2,6 14% 81 19,37 13% 1,651 39,637 14% Field and Truck, Surface 1,793 8,399 56% 1,816 81,139 57% 3,69 161,539 56% Pasture, Surface ,18 19% ,961 19% 1,139 54,141 19% Subtropical, Micro 338 7,643 5% 332 6,458 5% 67 14,1 5% Vineyard, Micro 75 2,183 2% 64 2,241 2% 139 4,424 2% Vineyard, Surface 39 1,236 1% 43 1,283 1% 82 2,519 1% All Crops and Methods 3, ,242 1% 3, ,793 1% 7, ,36 1% 7 Event application efficiency is defined as the ratio of the soil moisture depletion at the start of the irrigation event divided by the total applied water for the event. In cases in which leaching requirements are significant, the targeted leaching amount should be included in the numerator. (Howell, 23) Kaweah Delta Water Conservation District 45 March 213

53 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model 5.2 Validation of Actual Crop ET Field and Section Scale Validation Comparisons of total annual ET by field between the root zone water balance model and SEBAL are presented in Figures 5.1, 5.2, and 5.3 for the 27, 28, and 29 SEBAL analyses, respectively. Note that the 28 SEBAL analysis covered the period from February to November, and the 29 SEBAL analysis covered the period from March through September, thus excluding the winter and fall months, as discussed previously. A table comparing area-weighted average actual ET totals for the verification fields for the years 27 through 29 and the combined three-year period is provided in Table 5.5. For each year, although individual field ET a values estimated based on the root zone model may vary significantly from corresponding SEBAL ET a, values estimated by both methods agree closely over the full observed range. As indicated, in Table 5.5, differences on an annual or seasonal basis were one percent or less for all years and for the combined approximately three-year period. 6 y = 53x R² = 332 RZ Model ETa (in) SEBAL 27 ETa (in) Figure 5.1. Comparison of 27 Annual Crop ET from Root Zone Water Balance and SEBAL, KDWCD Verification Fields. Kaweah Delta Water Conservation District 46 March 213

54 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model RZ Model ETa (in) y = 16x R² = SEBAL 28 ETa (in) Figure 5.2. Comparison of 28 February - November Crop ET from Root Zone Water Balance and SEBAL, KDWCD Verification Fields. RZ Model ETa (in) y = 116x R² = SEBAL 29 ETa (in) Figure 5.3. Comparison of 29 March - September Crop ET from Root Zone Water Balance and SEBAL, KDWCD Verification Fields. Kaweah Delta Water Conservation District 47 March 213

55 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model Table 5.5. Comparison of Root Zone Model to SEBAL Area-Weighted Average Actual Evapotranspiration for KDWCD Verification Fields: 27, 28, and 29. Mean ETa (in) % Year SEBAL RZ model Difference 27, January December % 28, February November % 29, March October % Total % For individual fields, the root mean square error of estimated seasonal ET was 5.7 inches, and the mean absolute error of the estimated seasonal ET was 4.3 inches or 13 percent of total seasonal ET. As demonstrated by Table 5.5, these errors cancel when the results are aggregated to the entire study area. Similar cancellation of error is expected to also occur at smaller spatial scales aggregating multiple fields, such as groundwater model cells or hydrologic units. This is because errors at the field scale resulting from uncertainties in field-scale precipitation and runoff characteristics, irrigation practices, and cropping produce relatively small, random errors that are cancelled when fields are aggregated, even over relatively small areas. In order to demonstrate the reduction in random error that occurs as the results are aggregated spatially, root zone model results were compared to SEBAL results at the section scale. A section is a one square mile area encompassing 64 acres. Fields were assigned to sections based on the location of their centroid. Scatter plots comparing root zone model results are presented in Figures 5.4, 5.5, and 5.6 for the 27, 28, and 29 SEBAL analysis periods, respectively. As indicated, random error decreases substantially even for aggregation at the relatively small section scale. For individual sections, the root mean square error of estimated seasonal ET was 3.2 inches, and the mean absolute error of the estimated seasonal ET was 2.3 inches or 7 percent of total seasonal ET, essentially one half of the error at the field scale. The combined approach of applying remote sensing of with a root zone water balance model demonstrates a substantial reduction in the uncertainty in actual ET estimates as compared to traditional crop coefficient approaches. Even the best approaches using traditional crop coefficients result in uncertainties on the order of 15 to 25 percent when aggregated over large areas by experienced professionals with detailed cropping information (Thoreson et al., 29; Allen, 1999). Comparisons of the remote sensing results by crop to traditional crop coefficients are provided in Section 7. Kaweah Delta Water Conservation District 48 March 213

56 Applied Water Estimatess from Remote Sensing Calibration, Validation, and Application of Field-Scale Root Zone Water Balance Model 6 y =.9997x R² =.998 RZ Model ETa (in) SEBAL 27 ETa (in) Figure 5.4 Comparison of 27 Annual Crop ET from Root Zone Water Balance and SEBAL, Section Scale. 6 y =..9993x R² =.9987 RZ Model ETa (in) SEBAL 28 ETa (in) Figure 5.5. Comparison of 28 February - November Crop ET from Root Zone Water Balance and SEBAL, Section Scale. Kaweah Delta Water Conservation District 49 March 213

57 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model 6 y = 9x R² =.9992 RZ Model ETa (in) SEBAL 29 ETa (in) Figure 5.6. Comparison of 29 March - September Crop ET from Root Zone Water Balance and SEBAL, Section Scale Validation by Crop Method Group, Hydrologic Group, and Soil Type In addition to comparing annual and seasonal ET a estimates from the root zone water balance model to corresponding estimates from SEBAL for individual fields, comparisons were made by individual crop irrigation method group, by hydrologic unit, and by soil type. Results of these comparisons are provided in Tables 5.6, 5.7, and 5.8, respectively. In each case, area-weighted average ET a depths are provided. Results are provided for the SEBAL analysis period for each year. For 27, this period was January to December; for 28, this period was February to November; and for 29, this period was March to October. Accordingly, the tables are not intended to compare the SEBAL results across years or the root zone model (RZ) results across years, rather the tables are intended to compare the SEBAL results to the RZ model results within each year and across the full time period for which SEBAL results are available to validate the RZ model. The typical uncertainty in SEBAL ET a estimates on a seasonal or annual basis is estimated to be 5% based on the 95% confidence interval (Bastiaanssen et al., 25; Thoreson et al., 29). As indicated in Table 5.6, differences in ET a by crop-irrigation method group between the root zone water balance model and SEBAL ranged from -6 percent to 6 percent between 27 and 29, with average differences remaining within 3 percent over the three-year validation period. For the top three crop-irrigation method groups (field and truck crops, pasture, and surface-irrigated deciduous orchards), which represent approximately 89% of the total acreage, differences were within 2 percent in each year and for the threeyear period as a whole were within approximately 1 percent. Kaweah Delta Water Conservation District 5 March 213

58 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model As indicated in Table 5.7, differences in ET a by hydrologic unit between the root zone water balance model and SEBAL ranged from -7 percent and 9 percent between 27 and 29, with average differences remaining within 4 percent over the three-year validation period. For the largest four hydrologic units (2, 4, 5, and 6), which represent approximately 87% of the total cropped acreage, differences were within 9% in each year and within 4% for the three-year period as a whole. As indicated in Table 5.8, differences in ET a by soil type between the root zone water balance model and SEBAL ranged from -14 percent and 19 percent between 27 and 29, with average differences remaining within 8 percent over the three-year validation period. For the largest three soil types (sandy loam, loam, and sandy clay loam), which represent approximately 88% of the total cropped acreage, differences were within 4 percent in each year and within 1 percent for the three-year period as a whole. The relatively large differences in SEBAL and root zone model results by soil type could likely be reduced to some extent through modification of soil parameters that affect soil surface evaporation; however, due to the greatest differences occurring for minor soil types and close agreement in ET a by hydrologic unit, further adjustments to model parameters have not been made at this time. As indicated in Tables 5.6 through 5.8, the greatest variability in ET a differences among crop-method, hydrologic unit, or soil type occurred in 27. Determination of the cause of the greater differences in 27 would require additional investigation. It is possible that the 27 SEBAL results may have greater uncertainty than in 28 and 29 due to the use of MODIS imagery for the November December time period. Due to the coarser resolution of MODIS imagery as compared to Landsat, individual satellite pixels evaluated using MODIS data may overlap multiple fields, crop-method groups, or soil types, resulting in greater difficulty to develop accurate field-scale evapotranspiration estimates. As discussed previously, potential errors at the field scale are expected to cancel when aggregated to groundwater model cells, hydrologic units, or other coarser scales. Kaweah Delta Water Conservation District 51 March 213

59 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model Table 5.6. Comparison of SEBAL and Root Zone Water Balance Model Evapotranspiration by Crop Method Group, 27 to , January December 28, February November 29, March October Totals Crop/Method Acres SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff Deciduous, Micro 5, % % % % Deciduous, Surface 18, % 4 4 % % % Field and Truck, Surface 81, % % % % Pasture, Surface 26, % % % % Subtropical, Micro 6, % % % % Vineyard, Micro 2, % % % % Vineyard, Surface 1, % % % % TOTALS 141, % % % % Table 5.7. Comparison of SEBAL and Root Zone Water Balance Model Evapotranspiration by Hydrologic Unit, 27 to , January December 28, February November 29, March October Totals Hydrologic Unit Acres SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff 1 5, % % % % 2 16, % % % % 3 9, % % % % 4 27, % % % % 5 32, % % % % 6 31, % % % % TOTALS 122, % % % % Table 5.8. Comparison of SEBAL and Root Zone Water Balance Model Evapotranspiration by Soil Type, 27 to , January December 28, February November 29, March October Totals Soil Type Acres SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff SEBAL ETa (in) RZ ETc (in) % Diff clay loam 3, % % % % loam 52, % % % % sandy clay loam 12, % % % % sandy loam 59, % % % % silt loam 7, % % % % silty clay 4, % % % % others 3, % 3 3 3% % % TOTALS 141, % % % % Kaweah Delta Water Conservation District 52 March 213

60 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model Validation by Crop Method Group for SEBAL Periods SEBAL results are developed on a period basis, with a period typically nominally representing a 15-day or 3-day period (i.e., two weeks to one month), as discussed previously in Section 5.1. A comparison of results over the course of the year or growing season was completed for each crop-method group. These results are shown in Figures 5.7, 5.8, and 5.9 for 27, 28, and 29, respectively. Deciduous, Micro Period ETa (in) SEBAL RZ Model Period Field and Truck, Surface Period ETa (in) Period Subtropical, Micro Period ETa (in) SEBAL RZ Model Period Vineyard, Surface Period ETa (in) SEBAL SEBAL RZ Model Period RZ Model Deciduous, Surface Figure 5.7. Comparison of January December Period and Total SEBAL and Root Zone Model Results, 27. Period ETa (in) All SEBAL RZ Model Period Pasture, Surface Period ETa (in) SEBAL RZ Model Period Vineyard, Micro Period ETa (in) Seasonal ETa (in) SEBAL RZ Model Period SEBAL RZ Model micro surface surface surface micro micro surface deciduous deciduous field and truck pasture Crop Group subtropical vineyards vineyards Kaweah Delta Water Conservation District 53 March 213

61 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model Deciduous, Micro Period ETa (in) SEBAL RZ Model Period Field and Truck, Surface Period ETa (in) Period Subtropical, Micro Period ETa (in) SEBAL RZ Model Period Vineyard, Surface Period ETa (in) SEBAL SEBAL RZ Model Period RZ Model Deciduous, Surface Figure 5.8. Comparison of February November Period and Total SEBAL and Root Zone Model Results, 28. Period ETa (in) All SEBAL RZ Model Period Pasture, Surface Period ETa (in) SEBAL RZ Model Period Vineyard, Micro Period ETa (in) Seasonal ETa (in) SEBAL RZ Model Period SEBAL RZ Model micro surface surface surface micro micro surface deciduous deciduous field and truck pasture Crop Group subtropical vineyards vineyards Kaweah Delta Water Conservation District 54 March 213

62 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model Deciduous, Micro 8 SEBAL RZ Model Deciduous, Surface 1 SEBAL RZ Model Period ETa (in) Period ETa (in) Deciduous, Micro Period Field and Truck, Surface Period ETa (in) SEBAL RZ Model Field and Truck, 1 Surface Period Subtropical, Micro Period ETa (in) Subtropical, 1Micro Period Vineyard, Surface Period ETa (in) SEBAL SEBAL RZ Model RZ Model Vineyard, Surface Period Deciduous, Surface Period Pasture, Surface Pasture, Surface Period Vineyard, Micro Vineyard, Micro Period All Figure 5.9. Comparison of March October Period and Total SEBAL and Root Zone Model Results, 29. As indicated in Figures 5.7 through 5.9, SEBAL and root zone model results generally agree over the course of a year or growing season, but substantial differences can occur in a given period. This is likely due in part to increased uncertainty in SEBAL results for a given period; it is generally estimated that there is an uncertainty of 15% (95% confidence interval) in SEBAL results for a given period due to the fact that a single image date is used to estimate ET over the full time period (Bastiaanssen et al., 25). Although precipitation antecedent to the image date is considered as part of the SEBAL calibration process, the occurrence and temporal and spatial distribution of precipitation during the period are not Period ETa (in) Period ETa (in) Seasonal ETa (in) SEBAL SEBAL SEBAL RZ Model RZ Model RZ Model micro surface surface surface micro micro surface deciduous deciduous field and truck pasture Crop Group subtropical vineyards vineyards Kaweah Delta Water Conservation District 55 March 213

63 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model directly accounted for and may affect the accuracy of SEBAL ET estimates for individual images. By developing a daily time series of and tracking precipitation on a daily basis in the root zone model, these limitations are largely overcome, providing an advantage of the daily root zone water balance approach over an approach based solely on a remotely sensed surface energy balance. 5.3 Calibration and Validation of Crop Consumptive Use Fraction As described previously, following the calibration of total ET a, application efficiencies for individual irrigation events by crop-method group were adjusted to result in seasonal CCUF values consistent with estimates of CCUF based on Canessa et al. (211). CCUF on an annual basis may differ from application efficiency for a given irrigation event due to the potential for the crop to extract and transpire stored soil moisture during the period that soil moisture storage is above field capacity and is draining gravimetrically to field capacity and due to the dynamics of applied water storage and flushing of applied water out of the root zone by precipitation over the course of a given year, including the period outside of the primary growing season. Annual CCUF values were calculated based on the final model run to calibrate ET a and compared to target values by crop-method group. Then, individual irrigation event application efficiencies were adjusted iteratively to achieve the targeted CCUF values. The results of the CCUF and applied water calibration process by crop-method group are shown in Table 5.9. Table 5.9. Summary of Modeled and Target CCUF by Crop-Method Group and Additional Applied Water Fluxes, 1999 to 21 Average. Applied Water (in) ETaw (in) TW (in) DPaw (in) Change in Storage of AW (in) Modeled Target Crop Method Group Acres ETc (in) CUF CUF deciduous micro 1, (.3) 86% 85% deciduous surface 38, () 74% 75% field and truck surface 164, (.3) 69% 7% pasture surface 52, () 74% 75% subtropical micro 13, () 84% 85% vineyards micro 3, () 84% 85% vineyards surface 3, () 74% 75% As indicated in the last two columns of Table 5.9, modeled CCUF values were within approximately one percent of target values for the full analysis period. CCUF values vary somewhat from year to year, primarily due to differences in precipitation patterns which affect the timing and amount of applied water stored in the root zone and available for extraction and consumption by the crop as ET. Other columns in Table 5.9 summarize typical total ET a, total applied water, ET of applied water, tailwater (assumed to be zero, as discussed previously), deep percolation of applied water, and annual change in storage of applied water. Root zone vertical fluxes of ET aw, applied water, and deep percolation of applied water are presented in greater detail in the following section. Also included in the following section is deep percolation of precipitation, which provides an estimate of the amount of infiltrated precipitation on farmed lands that is stored in the root zone and deep percolates as a result of additional precipitation or being flushed through the vadose zone by applied irrigation water. Kaweah Delta Water Conservation District 56 March 213

64 Calibration, Validation, and Application of Field Scale Root Zone Water Balance Model In addition to the summary of applied water fluxes, a summary of the modeled precipitation fluxes by crop-irrigation method group is provided in Table 5.1. Table 5.1. Summary of Modeled Precipitation Fluxes, 1999 to 21 Average. Precipitation (in) Change in Storage of Precip. (in) Effective Precip. Pct Crop Method Group Acres ETc (in) ETpr (in) ROpr (in) DPpr (in) deciduous micro 1, % deciduous surface 38, % field and truck surface 164, % pasture surface 52, % subtropical micro 13, % vineyards micro 3, % vineyards surface 3, % Kaweah Delta Water Conservation District 57 March 213

65 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes 6 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes This section provides a summary of ET aw, applied water (AW), DP aw, and DP pr estimates based on the calibrated root zone water balance model for the 1999 to 21 analysis period. First, average annual acreage for each crop-method group by hydrologic unit is shown in Figure 6.1 and tabulated in Table 6.1. Then, the results are presented in the following forms: Annual total depths of ET aw, AW, DP aw, and DP pr by crop-method group (Figure 6.2) Annual total volumes of ET aw, AW, DP aw, and DP pr by crop-method group (Figure 6.3) Annual total depths of ET aw, AW, DP aw, and DP pr by hydrologic unit (Figure 6.4) Annual total volumes of ET aw, AW, DP aw, and DP pr by hydrologic unit (Figure 6.5) Average monthly depths of ET aw, AW, DP aw, and DP pr by crop-method group (Figure 6.6) Average monthly volumes of ET aw, AW, DP aw, and DP pr by crop-method group (Figure 6.7) Average monthly depths of ET aw, AW, DP aw, and DP pr by hydrologic unit (Figure 6.8) Average monthly volumes of ET aw, AW, DP aw, and DP pr by hydrologic unit (Figure 6.9) Irrigated Area, Acres 7, 6, 5, 4, 3, 2, Vineyards Surface Vineyards Micro Subtropical Micro Deciduous Micro Deciduous Surface Pasture Surface Field&Truck Surface 1, Hydrologic Unit Figure 6.1. Average Annual Crop-Method Group Acreage by Hydrologic Unit. Table 6.1. Average Annual Acreage by Crop-Method Group by Hydrologic Unit. Average Acres by Hydrologic Unit Crop Method Group TOTALS Deciduous Micro ,145 1, ,25 Deciduous Surface 1,285 6,4 5,386 14,93 5,544 1,949 35,494 Field & Truck Surface 2,962 18,787 1,285 24,465 41,797 49,71 147,367 Pasture Surface 836 5,922 2,688 9,883 13,656 12,952 45,937 Subtropical Micro 3,674 1, , ,535 Vineyards Micro , ,287 Vineyards Surface 28 1, , ,395 TOTALS 9,913 34,862 19,343 56,447 64,796 64,678 25,39 Kaweah Delta Water Conservation District 58 March 213

66 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes ET of Applied Water, Inches Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Deep Percolation of Applied Water, Inches Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Year Year Applied Water Depth, Inches Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Year Figure 6.2. Annual Total Depth of ET aw, AW, DP aw, and DP pr by Crop-Method Group. Deep Percolation of Precipitation, Inches Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Year Kaweah Delta Water Conservation District 59 March 213

67 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes ET of Applied Water, Acre Feet 5, 45, 4, 35, 3, 25, 2, 15, 1, 5, Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Deep Percolation of Applied Water, Acre Feet 22, 2, 18, 16, 14, 12, 1, 8, 6, 4, 2, Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Year Year Applied Water Volume, Acre Feet 7, 6, 5, 4, 3, 2, 1, Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Deep Percolation of Precipitation, Acre Feet 6, 5, 4, 3, 2, 1, Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Year Year Figure 6.3. Annual Total Volume of ET aw, AW, DP aw, and DP pr by Crop-Method Group. Kaweah Delta Water Conservation District 6 March 213

68 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes ET of Applied Water, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Deep Percolation of Applied Water, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit Year Year Applied Water Depth, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Deep Percolation of Precipitation, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit Year Year Figure 6.4. Annual Total Depth of ET aw, AW, DP aw, and DP pr by Hydrologic Unit. Kaweah Delta Water Conservation District 61 March 213

69 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes ET of Applied Water, Acre Feet 22, 2, 18, 16, 14, 12, 1, 8, 6, 4, 2, Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Deep Percolation of Applied Water, Acre Feet 9, 8, 7, 6, 5, 4, 3, 2, 1, Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit Year Year 3, Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 25, Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Applied Water Volume, Acre Feet 25, 2, 15, 1, 5, Deep Percolation of Precipitation, Acre Feet 2, 15, 1, 5, Year Year Figure 6.5. Annual Total Volume of ET aw, AW, DP aw, and DP pr by Hydrologic Unit. Kaweah Delta Water Conservation District 62 March 213

70 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes c ET of Applied Water, Inches Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Deep Percolation of Applied Water, Inches Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Applied Water Depth, Inches January February March April May June July August September October November December Month Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Deep Percolation of Precipitation, Inches Month Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Month Month Figure 6.6. Average Monthly Depth of ET aw, AW, DP aw, and DP pr by Crop-Method Group. Kaweah Delta Water Conservation District 63 March 213

71 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes ET of Applied Water,Acre Feet 9, 8, 7, 6, 5, 4, 3, 2, 1, Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Deep Percolation of Applied Water, Acre Feet 35, 3, 25, 2, 15, 1, 5, Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Applied Water Volume, Acre Feet 14, 12, 1, 8, 6, 4, 2, January February March April May June July August September October November December Month Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Deep Percolation of Precipitation, Acre Feet 6, 5, 4, 3, 2, 1, Month Deciduous Micro Deciduous Surface Field & Truck Surface Pasture Surface Subtropical Micro Vineyards Micro Vineyards Surface Month Month Figure 6.7. Average Monthly Volume of ET aw, AW, DP aw, and DP pr by Crop-Method Group. Kaweah Delta Water Conservation District 64 March 213

72 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes ET of Applied Water, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Deep Percolation of Applied Water, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Month Month Applied Water Depth, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Deep Percolation of Precipitation, Inches Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Month Month Figure 6.8. Average Monthly Depths of ET aw, AW, DP aw, and DP pr by Hydrologic Unit. Kaweah Delta Water Conservation District 65 March 213

73 Summary of ET of Applied Water, Applied Water, and Deep Percolation Fluxes ET of Applied Water, Acre Feet 4, 35, 3, 25, 2, 15, 1, 5, Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Deep Percolation of Applied Water, Acre Feet 14, 12, 1, 8, 6, 4, 2, Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Month Month 6, Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 2,5 Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6 Applied Water Volume, Acre feet 5, 4, 3, 2, 1, Deep Percolation of Precipitation, Acre Feet 2, 1,5 1, 5 Month Month Figure 6.9. Average Monthly Volumes of ET aw, AW, DP aw, and DP pr by Hydrologic Unit. Kaweah Delta Water Conservation District 66 March 213

74 Comparison to Water Resources Investigation Estimates 7 Comparison to Water Resources Investigation Estimates In 23, KDWCD performed a Water Resources Investigation (WRI) to investigate and quantify the water resources of the District (Fugro West, 27). The WRI quantifies the water resources of the District, including the quantity of groundwater underlying KDWCD, inflows to and outflows from the groundwater system, and groundwater elevation trends. As part of the WRI, estimates of agricultural groundwater pumpage were prepared for the nineteen-year period from 1981 to Pumpage was estimated based on estimated crop water requirements using a crop coefficient based approach. 7.1 Comparison of Monthly and Annual Crop Coefficients and Crop ET Total crop ET for the WRI was calculated on a monthly basis using crop coefficients from University of California Publication (Goldhamer and Snyder, 1988). The crop groups evaluated and corresponding monthly crop coefficients are listed in Table 7.1. Note that crop coefficients for deciduous nuts/fruits, grapes, and citrus were multiplied by a factor of to account for the presence of immature orchards and vineyards (Fugro West, 27). Table 7.1. WRI Crop Groups and Monthly Crop Coefficients (Fugro West, 23). In order to evaluate potential differences in crop coefficients and resulting crop ET estimates between the crop coefficient based approach previously applied and the remote sensing based approach described herein, crop coefficients and resulting crop ET estimates were compared. Specifically, estimates of crop ET were developed on a monthly basis based on the crop coefficients of Table 7.1. Then, crop coefficient and crop ET results from the remote sensing based root zone model (RS-RZ) were extracted for the 11 crop groups listed in Table 7.1 based on the 1999 DWR land use survey for Tulare County. The number of fields and acres for which results were extracted from the RS-RZ results for each WRI crop group is summarized in Table 7.2. Kaweah Delta Water Conservation District 67 March 213

75 Comparison to Water Resources Investigation Estimates Table 7.2. Summary of RS-RZ Fields and Acres by WRI Crop Group. Number of WRI Crop Group Fields Acres Alfalfa ,88 Citrus ,911 Deciduous 1,826 39,987 Fallow Field Cotton ,39 Misc Field 1,639 61,768 Pasture 169 2,272 Small Grain 245 8,12 Sugar beets Truck 115 3,884 Vineyard 286 8,566 Totals 6,43 24,541 As indicated in Table 7.2, RS-RZ model results were not extracted for rice fields because none were present in KDWCD based on the DWR 1999 Tulare County crop data. Note that the miscellaneous field crops crop group is dominated by corn, a dominant crop in KDWCD s service area. RS-RZ model results were additionally extracted for fallow fields although they were not included in the WRI analysis and were likely assumed to have no ET. In Figures 7.1 through 7.1, presented in Sections through , the blue bars depict WRI estimates of crop coefficients and ET c, while the solid line with diamond markers shows the areaweighted average RS-RZ results. The dotted lines show the 1 th and 9 th percentile RS-RZ model results for individual fields. Eighty percent of fields within a given crop group fall between the 1 th and 9 th percentile values. The dotted lines demonstrate the substantial variability under actual growing conditions among fields of the same general crop type at any given time. Such variability reflects a variety of actual field conditions that cannot be estimated using a crop coefficient approach without detailed information describing individual fields and management practices Alfalfa Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.1 for alfalfa. As indicated in Figure 7.1, the mean actual crop coefficient for alfalfa during the November to February time period is not zero as assumed based on the WRI estimates. This is likely due to a combination of factors including the presence of green vegetation and wet soil evaporation due to precipitation. Additionally, during the March to October growing season, the average crop coefficient for alfalfa is less than the assumed value of. This is likely due to the effects of cutting, grazing, water stress, areas of fields without green vegetation, and other factors that affect actual crop ET for a given field. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. Kaweah Delta Water Conservation District 68 March 213

76 Comparison to Water Resources Investigation Estimates Due to the underestimation of crop ET during the winter and overestimation during the growing season by the WRI crop coefficient approach, annual ET values are similar from the two approaches, with an estimate of 42. inches using the RS-RZ model and 43.9 inches using the WRI crop coefficient approach. Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 9 WRI RS RZ 1th & 9th percentiles Month Figure 7.1. WRI and RS-RZ Model Results for Alfalfa, Crop Evapotranspiration (in) Citrus Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.2 for citrus. As indicated in Figure 7.2, the mean actual crop coefficient for citrus is generally greater than the assumed value of.59 8, with the exception of the June to September period. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 7 WRI RS RZ 1th & 9th percentiles Month Figure 7.2. WRI and RS-RZ Model Results for Citrus, Crop Evapotranspiration (in) 8 The estimated crop coefficient for citrus from the WRI is 5 multiplied by a factor of to account for the presence of immature orchards with less than full cover as described in the WRI. Kaweah Delta Water Conservation District 69 March 213

77 Comparison to Water Resources Investigation Estimates Due to the underestimation of crop ET between October and May by the crop coefficient approach, actual annual ET values are believed to be greater than estimated for the WRI, with an estimate of 33.8 inches using the RS-RZ model and 29.5 inches using the WRI crop coefficient approach Deciduous Orchards Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.3 for deciduous orchards. As indicated in Figure 7.3, the mean actual crop coefficient for deciduous orchards during the November to February time period appears to be greater than estimated for the WRI. This is likely due to the presence of green vegetation (e.g. cover crops or weeds) and wet soil evaporation resulting from precipitation. Additionally, during the June to September period, the average actual crop coefficient for deciduous orchards is less than the estimated value from the WRI 9. This may be due to water stress or other factors that affect actual crop ET for a given field. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 8 WRI RS RZ 1th & 9th percentiles Month Figure 7.3. WRI and RS-RZ Model Results for Deciduous Orchards, Crop Evapotranspiration (in) Due to the underestimation of crop ET during the winter and overestimation during the June to September period by the crop coefficient approach, annual ET values from the two approaches are similar, with an estimate of 36.1 inches using the RS-RZ model and 37.5 inches using the WRI crop coefficient approach Fallowed Fields Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.4 for fallowed fields. As indicated in Figure 7.4, the mean actual crop coefficient for fallowed fields appears to decrease from approximately 3 in January to approximately.1 in June and is not zero as appears to have been assumed for the WRI. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. 9 The estimated crop coefficient for deciduous orchards from the WRI is multiplied by a factor of to account for the presence of immature orchards with less than full cover as described previously. Kaweah Delta Water Conservation District 7 March 213

78 Comparison to Water Resources Investigation Estimates Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 4.5 WRI RS RZ 1th & 9th percentiles Month Figure 7.4. WRI and RS-RZ Model Results for Citrus, Crop Evapotranspiration (in) Due to the apparent underestimation of ET by the WRI crop coefficient approach, actual annual ET values are greater than estimated for the WRI, with an estimate of 12.3 inches using the RS-RZ model and inches using the WRI crop coefficient approach. Annual precipitation between October 1998 and April 1999 was estimated to be approximately 8.1 inches, only 66 percent of the estimated annual ET for fallowed fields. The estimated annual ET for fallowed fields from the RS-RZ model may reflect the consumption of stored soil moisture from the prior irrigation season, misidentification of fields as being fallow during the DWR land use survey, or the presence of unknown water supplies, such as runoff from adjacent fields. Additional investigation of the fallow fields could help identify why mean ET rates exceed the annual precipitation amount. Due to the use of a remote sensing approach, validated across the full range of observed actual ET as described in Section 6, it is believed that the estimated ET rates for apparently fallowed fields are reasonably accurate Cotton Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.5 for cotton. As indicated in Figure 7.5, the mean actual crop coefficient for cotton during November, January, February, and March does not appear to be zero as assumed for the WRI crop coefficient approach. This may be due to the presence of green vegetation (e.g. weeds) and wet soil evaporation due to precipitation. During the May to June period, the crop coefficient approach appears to have overestimated crop ET for cotton, while during September and October, the crop coefficient approach appears to have underestimated crop ET for cotton. The differences between the crop coefficient approach and the RS-RZ model for 1999 may be due to differences in the timing of cotton planting, development, and harvest in 1999 as compared to the conditions for which the crop coefficients used in the WRI were developed. These results demonstrate the ability of the RS-RZ model to inherently account for changes in the timing of crop growth through regular observation of at the field scale via satellite imagery. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field, although they tend to be more uniform for cotton than for other crop groups. Kaweah Delta Water Conservation District 71 March 213

79 Comparison to Water Resources Investigation Estimates Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 9 WRI RS RZ 1th & 9th percentiles Month Figure 7.5. WRI and RS-RZ Model Results for Cotton, Crop Evapotranspiration (in) Due to the apparent overestimation of crop ET early in the growing season and underestimation late in the growing season by the crop coefficient approach, annual ET values from the two approaches are similar, with an estimate of 29.6 inches using the RS-RZ model and 27.7 inches using the WRI crop coefficient approach Miscellaneous Field Crops Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.6 for miscellaneous field crops. Based on the WRI report, miscellaneous field crops include melons, potatoes, tomatoes, beans, onions, and corn. In KDWCD, the primary field crop is corn, which is typically preceded by a winter grain crop. As indicated in Figure 7.6, the mean actual crop coefficient for miscellaneous field crops from the RS-RZ model supports that the crop group is dominated by a winter grain crop followed by corn. This is confirmed through review of the DWR 1999 Tulare County crop survey within KDWCD s service area, except that the winter grain crop is typically not identified as part of the ground-based survey due to field visits being conducted during the peak growing season, after the winter grain crop has been harvested and the corn crop has been planted. As a result, the actual crop coefficients differ substantially from those estimated for the WRI crop coefficient approach. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. As a result of the apparent underestimation of crop ET during January to April, overestimation in May and June, and underestimation for the remainder of the year using the WRI crop coefficient approach, annual ET values differ substantially between the two approaches, with an estimate of 3 inches using the RS-RZ model and 22.1 inches using the WRI crop coefficient approach. Kaweah Delta Water Conservation District 72 March 213

80 Comparison to Water Resources Investigation Estimates Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 9 WRI RS RZ 1th & 9th percentiles Month Figure 7.6. WRI and RS-RZ Model Results for Miscellaneous Field Crops, Crop Evapotranspiration (in) Pasture Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.7 for pasture. As indicated in Figure 7.7, the mean actual crop coefficient for pasture appears to be substantially less than the assumed value of.9. This is likely due to the effects of cutting, grazing, water stress, areas of fields without green vegetation, and other factors that affect actual crop ET for a given field. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 8 WRI RS RZ 1th & 9th percentiles Month Figure 7.7. WRI and RS-RZ Model Results for Pasture, Crop Evapotranspiration (in) Due to the apparent overestimation of crop ET throughout the year by the WRI crop coefficient approach, actual annual ET values appear to be less than estimated for the WRI, with an estimate of 32.8 inches using the RS-RZ model and 45.4 inches using the WRI crop coefficient approach Small Grains Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.8 for small grains. As indicated in Figure 7.8, the mean actual crop Kaweah Delta Water Conservation District 73 March 213

81 Comparison to Water Resources Investigation Estimates coefficient for small grains appears to be less than the estimated value for the WRI during January through March and during December. Crop coefficients additionally appear to be underestimated during the May through October period. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 7 WRI RS RZ 1th & 9th percentiles Month Figure 7.8. WRI and RS-RZ Model Results for Small Grains, Crop Evapotranspiration (in) Apparent overestimation of crop ET early in the year by the WRI crop coefficient approach is offset to some extent by underestimation during the May through October period; however actual annual ET values are greater than those estimated for the WRI, with an estimate of 17.5 inches using the RS-RZ model and 13.7 inches using the WRI crop coefficient approach Sugarbeets Due to the limited number of sugarbeet fields in KDWCD s Tulare County service area during 1999 (eight fields, as indicated in Table 7.2), a comparison of WRI and RS-RZ ET estimates has not been performed at this time Truck Crops Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.9 for truck crops. As indicated in Figure 7.9, the mean actual crop coefficient for truck crops appears to be substantially greater than zero, as assumed based on the WRI crop coefficient approach, for January, February, and July through December. The actual mean crop coefficient during April and May appears less than the value estimated for the WRI. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. This is likely due to truck crops including a variety of individual crops that may be grown at different times, as well as the presence of winter grains in some cases that may not be identified by the DWR land use survey. Apparent underestimation of crop ET in most months by the WRI crop coefficient approach results in overall underestimation of ET for the calendar year, with an estimate of 23.8 inches using the RS-RZ model and 14.7 inches using the crop coefficient approach. Kaweah Delta Water Conservation District 74 March 213

82 Comparison to Water Resources Investigation Estimates Crop Coefficient WRI RS RZ 1th & 9th percentiles Month 8 WRI RS RZ 1th & 9th percentiles Month Figure 7.9. WRI and RS-RZ Model Results for Truck Crops, Crop Evapotranspiration (in) Vineyards Monthly estimates of crop coefficients and crop ET from the WRI crop coefficient approach and from the RS-RZ model are shown in Figure 7.1 for vineyards. As indicated in Figure 7.1, the mean actual crop coefficients for vineyards during the January to March and September to December time periods appear greater than estimated for the WRI 1. This is likely due to the presence of green vegetation (e.g. cover crops or weeds), wet soil evaporation due to precipitation, and delay of leaf drop from the vines until late fall when the first rains arrive. During the April to August period, the average crop coefficients for vineyards appear similar to the estimated values from the WRI. As indicated based on the 1 th and 9 th percentile lines, crop coefficients and resulting ET rates vary substantially from field to field. Crop Coefficient WRI RS RZ 1th & 9th percentiles WRI RS RZ 1th & 9th percentiles Month Month Figure 7.1. WRI and RS-RZ Model Results for Vineyards, Crop Evapotranspiration (in) Due to the apparent underestimation of crop ET during the January to March and September to December time periods by the WRI crop coefficient approach, actual annual ET appears greater than estimated for 1 The estimated crop coefficient for deciduous orchards from the WRI is multiplied by a factor of to account for the presence of immature orchards with less than full cover as described previously. Kaweah Delta Water Conservation District 75 March 213

83 Comparison to Water Resources Investigation Estimates the WRI, with an estimate of 3.5 inches using the RS-RZ model and 23.2 inches using the WRI crop coefficient approach Summary of Comparisons by Crop A summary of estimated annual crop ET by WRI crop group for 1999 is provided in Figure 7.11 for the WRI and RS-RZ approaches. For the RS-RZ Approach, the mean annual crop ET is presented, along with error bars representing the standard deviation of field scale values. In general, mean annual crop ET estimates for the WRI fall within approximately one standard deviation of field-scale estimates of crop ET from the remote sensing approach. As demonstrated by the relative standard devation in RS-RZ estimates of field scale average annual crop ET, variability across fields varies substantially between WRI crop groups, with the greatest observed variability for citrus and deciduous orchards and the least variability for cotton. Annual Crop ET (inches) WRI RS RZ Crop Group Figure Comparison of 1999 Annual Crop ET for WRI and RS-RZ Approaches by WRI Crop Group. 7.2 Comparison of Remote Sensing and Root Zone Model Results to Water Resources Investigation for 1999 The WRI provides aggregated estimates of various surface layer fluxes by hydrologic unit and for KDWCD as a whole between 1981 and For purposes of comparison, aggregated results are compared for 1999, a year in which coincident estimates are available for both approaches. Results are compared by hydrologic unit and for KDWCD as a whole for the following quantities: Total crop area (acres) Total crop ET (acre-feet) Precipitation (acre-feet) Kaweah Delta Water Conservation District 76 March 213

84 Comparison to Water Resources Investigation Estimates Effective precipitation (acre-feet) 11 Gross applied irrigation water (acre-feet) Deep percolation of precipitation (acre-feet) Deep percolation of applied water (acre-feet) Net applied irrigation water (acre-feet) WRI estimates are provided in Tables 7.3 and 7.4. In Table 7.3, flux estimates are reported as volumes in acre-feet. In Table 7.4, flux estimates are reported in inches of depth based on the acreages listed in Table 7.3. Table 7.3. WRI Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole. Hydrologic Unit Total Cropped Area (ac) Total Crop ET (af) Precipitation (af) Effective Precipitation (af) Gross Applied Irrigation Water (af) Deep Percolation of Precipitation (af) Deep Percolation of Applied Water (af) Net Applied Irrigation Water (af) 1 14,15 34,149 17,215 4,177 38,926 1,684 7,7 31, ,562 92,662 41,97 13,391 13,696 21,255 19,24 84, ,33 52,48 2,58 5,214 61,256 11,619 11,359 49, , ,54 64,765 16, ,488 37,415 33, , , ,455 59,265 19, ,247 27,929 32, , , ,376 6,133 19, ,27 27,996 34,65 148,665 Total 285,9 648, ,793 79, , , ,894 65,989 Table 7.4. WRI Flux Depth Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole. Hydrologic Unit Total Crop ET (in) Precipitation (in) Effective Precipitation (in) Gross Applied Irrigation Water (in) Deep Percolation of Precipitation (in) Deep Percolation of Applied Water (in) Net Applied Irrigation Water (in) Total RS-RZ Model estimates are provided in Tables 7.5 and 7.6. In Table 7.5, flux estimates are reported as volumes in acre-feet. In Table 7.6, flux estimates are reported in inches of depth based on the acreages listed in Table For the combined remote sensing and root zone model approach, effective precipitation is referred to as ET of precipitation. The terms are analogous for purposes of comparison. Kaweah Delta Water Conservation District 77 March 213

85 Comparison to Water Resources Investigation Estimates Table 7.5. RS-RZ Model Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole. Hydrologic Unit Total Cropped Area (ac) Total Crop ET (af) Precipitation (af) Effective Precipitation (af) Gross Applied Irrigation Water (af) Deep Percolation of Precipitation (af) Deep Percolation of Applied Water (af) Net Applied Irrigation Water (af) 1 9,94 28,178 6,697 4,763 3,347 1,72 7,185 23, ,862 95,458 23,573 14,912 12,674 7,748 4,946 79, ,343 54,88 13,79 8,275 68,826 4,48 22,874 45, , ,51 38,168 25, ,262 1,971 56, , , ,43 43,85 28, ,448 13,599 71, , , ,1 43,734 26, ,19 13,654 76,53 146,515 Total 25,16 682,98 169,56 18, ,576 52,99 275, ,294 Differences between the RS-RZ Model and WRI results are provided in Tables 7.7 and 7.8. In Table 7.7, differences are reported as acres or as volumes in acre-feet. In Table 7.8, differences are reported in inches of depth based on the acreages listed in Tables 7.3 and 7.5. A comparison of acreages and flux volumes for KDWCD as a whole is provided in Figure A comparison of flux depths for KDWCD as a whole is provided in Figure Comparison of the RS-RZ Model results to the WRI estimates indicates substantial differences in estimated acreages, flux volumes, and flux depths for individual hydrologic units (HUs) and, in some cases, the KDWCD service area as a whole. These differences are discussed below. Table 7.6. RS-RZ Model Flux Depth Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole. Hydrologic Unit Total Crop ET (in) Precipitation (in) Effective Precipitation (in) Gross Applied Irrigation Water (in) Deep Percolation of Precipitation (in) Deep Percolation of Applied Water (in) Net Applied Irrigation Water (in) Total Kaweah Delta Water Conservation District 78 March 213

86 Comparison to Water Resources Investigation Estimates Table 7.7. Difference between RS-RZ Model and WRI Acreage and Flux Volume Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole. Hydrologic Unit Total Cropped Area (ac) Total Crop ET (af) Precipitation (af) Effective Precipitation (af) Gross Applied Irrigation Water (af) Deep Percolation of Precipitation (af) Deep Percolation of Applied Water (af) Net Applied Irrigation Water (af) 1 (4,246) (5,971) (1,518) 586 (8,579) (8,964) 178 (8,757) 2 (6,7) 2,796 (18,334) 1,521 16,978 (13,57) 21,76 (4,728) 3 (1,69) 2,832 (7,429) 3,61 7,57 (7,211) 11,515 (3,945) 4 (6,738) (1,453) (26,597) 8,911 2,774 (26,444) 22,424 (19,65) 5 (3,6) 2,948 (15,46) 8,299 41,21 (14,33) 38,667 2,534 6 (12,91) 15,634 (16,399) 7,11 39,749 (14,342) 41,898 (2,15) Total (35,884) 34,786 (94,737) 29,487 99,693 (84,799) 136,388 (36,695) Table 7.8. Difference between RS-RZ Model and WRI Flux Depth Estimates for 1999 by Hydrologic Unit and for KDWCD as a Whole. Gross Deep Percolation Deep Perco- Total Precipitation Effective Applied of lation of Crop Precipitation Irrigation Precipi- Applied ET (in) (in) (in) Water (in) tation (in) Water (in) Hydrologic Unit Net Applied Irrigation Water (in) (6.5) (7.) (4.) (3.5) (3.6) (3.9) (4.2) (4.8) 5.5 () (2.3) (2.4) (1.2) (1.8) Total 5.8 (2.9) (3.2) Kaweah Delta Water Conservation District 79 March 213

87 Comparison to Water Resources Investigation Estimates Cropped Area (acres) and Flux (acrefeet) 9, 8, 7, 6, 5, 4, 3, 2, 1, Cropped Area Crop ET Precip. Eff. Precip. Gross Applied Deep Perc. Precip. Figure Comparison of WRI and RS-RZ Model Acreage and Flux Volume Estimates for 1999 for KDWCD as a Whole. WRI Deep Perc. Applied RS-RZ Net Applied Flux (inches) Crop ET Precip. Eff. Precip. Gross Applied Deep Perc. Precip. Figure Comparison of WRI and RS-RZ Model Flux Depth Estimates for 1999 for KDWCD as a Whole Comparison of Cropped Acres Deep Perc. Applied Net Applied As indicated in Table 7.7, the total cropped area between the RS-RZ and WRI differ by approximately 36, acres for KDWCD as a whole (approximately 13 percent). Differences by hydrologic unit from negative 1,69 acres for HU 3 to negative 12,91 acres for HU 6. For each analysis, fields with their centroid within the KDWCD service area were included. For the WRI analysis, fields were delineated and acreages estimated based on the 1996 DWR land use survey for Kings County and the 1999 DWR land use survey for Tulare County. For the current study, fields were delineated based on the FSA CLU land use data, which is believed to better represent the actual irrigated area, as discussed previously in Section 2.1. WRI RS-RZ Kaweah Delta Water Conservation District 8 March 213

88 Comparison to Water Resources Investigation Estimates Despite differences in the source of field delineations between the WRI and remote sensing analyses, it is not clear why the WRI acres are so large. Due to the DWR delineations often including areas adjacent to irrigated areas, an estimate of less irrigated acres for the current analysis is expected, to some extent. Another possible explanation is that the WRI analysis includes double cropping. In other words, acreages associated with fields including both a winter grain and a summer corn crop could be counted twice, although if this were the case, the difference could be even greater. The difference in estimated irrigated acreage resulting from the use of different sources of field boundary delineations is illustrated in Figure Field delineations from DWR land use surveys are shown in red, and field delineations for the remote sensing analysis based on the FSA CLU data are shown in blue. For the upper left image, the DWR delineation includes an irrigation ditch and a open area in the northeast corner of the field. In the upper right image, the DWR delineation includes an irrigation ditch along the west edge of the two fields and portions of roads along the east and south edges of the fields. For the lower left image, again the DWR delineation includes non-cropped areas along field edges. In the lower right image, the DWR delineation of the northeast field includes a canal along the north edge. For the southern two fields, the DWR delineation includes a substantial area that appears to represent dairy feedlots, a lagoon, and open areas. Note that the DWR delineated crop area and CLU field coverage do not include the dairy area in the northwestern portion of the image. In order to further investigate the difference in acreages, the 1999 Tulare County and 23 Kings County DWR land use surveys were combined, and the cropped fields with centroids within the KDWCD service area were selected. The total area represented by these fields is approximately 267, acres based on the DWR land use surveys. The total area represented by fields in the remote sensing model, including the 2,29 acres not included because they were too small for extraction of satellite data or not cropped for the original DWR surveys, as described in Section 2.2.3, is approximately 252, acres. The difference in these acreages is likely due to the difference in the delineation of field boundaries by DWR and FSA. The remaining difference of approximately 22, acres between the 27 WRI report and the DWR land use surveys has not been explained at this time. Kaweah Delta Water Conservation District 81 March 213

89 Comparison to Water Resources Investigation Estimates Figure Comparison of DWR and FSA Field Delineations Comparison of Crop Evapotranspiration The RS-RZ Model results suggest an additional 34,786 acre-feet of crop ET in 1999 as compared to the WRI estimates (5.8 additional acre-inches per acre). Differences for individual HUs range from negative 5,971 acre-feet to 2,948 acre-feet. Expressed as a unit depth, the differences range from 3.2 to 7.8 acreinches per acre. Expressed as a unit depth, the differences are all positive as a result of differences in acreage by hydrologic unit. In order to better understand the differences, a comparison of crop ET volume estimates based on the product of the estimated ET depth for each crop type reported in Section 7.1 and the total crop acres from the FSA CLU fields in HUs 1 through 5 (corresponding to the Tulare County DWR survey area, for which 1999 cropping data were available) are provided in Table 7.9. Thus, this comparison uses a consistent acreage for each crop to provide insight into the effect of differences in Kaweah Delta Water Conservation District 82 March 213