C a l - S I M E T A W

Transcription

1 C a l - S I M E T A W Daily Soil Water Balance Program New Model to Estimate Agricultural Water Demand in California Morteza Orang 2015 Annual UC, ANR Water Program Team February 13, 2015 California Study Area Map Hydrologic Regions North Coast San Francisco Bay Central Coast South Coast Sacramento River San Joaquin River Tulare Lake North Lahontan South Lahontan Colorado River 1

2 California Study Area Map HR and DAU Boundaries North Coast San Francisco Bay Central Coast South Coast Sacramento River San Joaquin River Tulare Lake North Lahontan South Lahontan Colorado River Detail Analysis Unit California Study Area Map HR and DAU/County Boundaries North Coast San Francisco Bay Central Coast South Coast Sacramento River San Joaquin River Tulare Lake North Lahontan South Lahontan Colorado River Detail Analysis Unit County Line 2

3 California PRISM Grid Map HR, DAU Boundaries and PRISM Grids North Coast San Francisco Bay Central Coast South Coast Sacramento River San Joaquin River Tulare Lake North Lahontan South Lahontan Colorado River Detail Analysis Unit PRISM Grid Objectives Improve the estimation of ETc and ETaw for regional water planning Consistency among other models using same data: WEAP and CalSIM Update information for CWP 2018 Use Spatial CIMIS and PRISM data that can be aggregated up to the DAU/Co scale 3

4 Objectives Rapidly and accurately determine ETc and ETaw for 20 crop categories and 4 land use categories by DAU/Co, PA, HR, and State. Refine and improve Kc factors for 20 Crop categories using CDFA reports and DAU/Co boundaries Develop a database of AWHC and SDx over the State Objectives Compute weighted mean daily Pcp, ETo, and soil by DAU/Co and 4 land use groups (irrigated land, urban, native vegetation, and water surface) 4

5 Input Data Requirements Daily weather data on a 4 X 4 Km grid spacing Crop Coefficients Crop information AWHC and SDx data at 4 Km resolution Seasonal consumed fraction (%) Historical land use records for 20 crop categories Cal SIMETAW Output Simulated and non simulated daily and monthly weather and ETo data for 26,318 grids Weighted mean daily and monthly weather and ETo data by DAU/Co and 4 land use groups on each DAU/County Daily ETo, Kc, ETc, water balance, and ETaw for each crop and land use category by DAU/Co 5

6 Cal SIMETAW Output (continued) Seasonal and annual total ETc, ETaw, and AW for 20 crop categories by DAU/Co within each PA, HR, and State Seasonal and annual total ETc, ETaw, and AW for 20 crop categories by PA, HR, and California California Weather Data Daily PRISM: Daily ETo is calculated from T max & T min using a calibrated HS equation from Oct to Dec Monthly PRISM: Daily weather data are simulated from Monthly PRISM data to simulate ETo from Oct to Dec

7 California Weather Data (continued) Daily Spatial CIMIS: Daily ETo is calculated from Rs, Tmax, Tmin, U2, and Tdew using PM equation from October 2004 to December 2013 Monthly GCM: Daily regional Rs, Tmax, Tmin, U2, Tdew, and Precipitation are simulated from monthly GCM data for 2030, 2050, 2080, and 2100 Daily ETo estimates using daily PRSIM data for a PRISM grid number 99_62 within Davis, October 2004 September

8 Comparison of daily ETo estimates from two different methods for a PRISM grid number 99_62 within Davis, October September 2010 Comparison of daily ETo estimates from three different methods for a PRISM grid number 99_62 within Davis, October September

9 Comparison of daily ETo estimates from four different methods for a PRISM grid number 99_62 within Davis, October September 2010 Monthly total ETo estimates from Cal-SIMETAW for a PRISM grid number 99_62 within Davis, October September

10 Comparison of monthly total ETo estimates from two different methods for a PRISM grid number 99_62, January December 2006 Comparison of monthly total ETo estimates from three different methods for a PRISM grid number 99_62, January December

11 Comparison between monthly ETo estimates from Cal- SIMETAW using daily PRISM and CIMIS with correlation coefficient at a PRSIM grid from October September 2010 Comparison between monthly ETo estimates from Cal- SIMETAW using daily Spatial CIMIS and CIMIS with correlation coefficient at a PRSIM grid from October September

12 Simulation of Weather Data Simulation of daily weather data Evaluate climate change Solar Radiation, Clouds, and Fog (ETo) Temperature (ETo) CO2 Concentration (ETo) Rainfall Patterns (ETaw) Simulated daily ETo for current conditions in Davis, California 12

13 ETo comparison for current and climate change conditions in Davis, California ETo comparison for current and climate change conditions in Davis, California 13

14 ETo comparison for current and climate change conditions in Davis, California Changes in ETo for current and climate change conditions in Davis, California 14

15 Comparison of measured and simulated daily solar radiation data in Davis, California Comparison of measured and simulated daily maximum air temperature data in Davis, California 15

16 Comparison of measured and simulated daily minimum air temperature data in Davis, California Comparison of measured and simulated daily wind speed data in Davis, California 16

17 Comparison of measured and simulated daily dew point temperature data in Davis, California Comparison of measured and simulated monthly total rainfall data in Davis, California 17

18 Comparison of simulated and estimated daily ETo from Cal SIMETAW and CIMIS in Davis, California Relationship between daily ETo estimates from CIMIS and simulated data by Cal SIMETAW in Davis, California 18

19 Crop & Soil Information Crop and land use categories Starting and ending dates Wetting frequency during the initial growth period Presence of cover crops Crop & Soil Information (continued) Immaturity factors Maximum rooting depths AWHC and SDx on a 4 X 4 km grid from SSURGO Allowable depletion (50%) 19

20 Structure of Cal SIMETAW and Its Database Off Season (Bare Soil) Coefficients 20

21 Off Season (Bare Soil) Coefficients (continued) Crop Coefficients Irrigation frequency initial K c Cover crop adjustment when a cover crop is present, 0.35 is added to the clean cultivated Kc Immature crop adjustment Correcting Midseason Kc Values for climate Kcmid = Kctab (ETo 7.3) (Kctab 1.0) 21

22 Daily Soil Water Balance Yield threshold depletion YTD = AD X PAW Allowable Depletion For most crops, 50% AD is a reasonable value. For drought sensitive crops with shallow root systems or heavy compacted soils, the AD can be decreased by 10 to 15% (30 50%). For drought tolerant crops with dense root systems, the AD can be increased by 10 to 15% (50 65%). Daily Soil Water Balance (continued) Plant Available Water (amount of water held in the root zone) I. If SDx > RDx then PAW = AWHC X RDx II. If SDx < RDx then PAW = AWHC X SDx 22

23 Daily Soil Water Balance (continued) Off season maximum depletion (50% of PAW in upper 30 cm) Soil water depletion SWD = Field Capacity Soil Water Content Irrigate when SWD > YTD Daily Soil Water Balance (continued) Effective Seepage of Groundwater I. If Spg > SWD then E spg = SWD II. If Spg < SWD then E spg = Spg SWD = SWD E spg Effective rainfall I. If Pcp > SWD then R e = SWD II. If Pcp < SWD then R e = Pcp 23

24 Daily Soil Water Balance (continued) Pre irrigation (start at FC at planting or leaf out) No pre irrigation (start at FC on January 1) ET of Applied Water (ETaw) ET of Applied Water n ETaw = NA 1 + NA NA n = NA Or ETaw = SETc CESpg CRe WC i=1 24

25 A daily soil water balance for almond in Colusa, 1998 Additional Features Allows easy update input land use data files for individual field crops and crop categories by DAU/Co Allows easy input of crop information by DAU/Co Includes a large database of climate, soil, ETo, ETc, ETaw, and AW data by DAU/Co, PA, HR, and state Outputs daily calculated ETo grid data by lat. and long. over California from 1921 to Dec

26 Statewide Irrigation Methods Survey Trends in irrigated area (percent) by irrigation methods in California 26

27 A user friendly program for obtaining 1991, 2001, and 2010 irrigation methods survey data Spatial Analysis of application efficiency in Irrigation 27

28 Purpose: Reduce uncertainty in the analysis of water demand How efficiently irrigation water is being applied Identify potential places where further improvements can be made Trends in irrigated area (percent) by irrigation methods in California 28

29 The End Questions 29