Satellite derived Groundwater Storage Es4mates and Opportuni4es for Expanding Research in Arid Environments

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1 Satellite derived Groundwater Storage Es4mates and Opportuni4es for Expanding Research in Arid Environments NASA Ames DEVELOP Program Summer 2011 Amber Kuss, San Francisco State University William Brandt, California State University, Monterey Bay Joshua Randall, Arizona State University Bridget Floyd, University of California, Berkeley Abdelwahab Bourai, Cuper4no High School Michelle Newcomer, San Francisco State University Cindy Schmidt, Mentor Dr. J.W. Skiles, Mentor

2 NASA Applied Sciences DEVELOP Na4onal Program 9-10 week summer internship Paid Using NASA data to solve real world problems Student-run student-led Groundwater level measurements in West Sacramento 2011 Summer Interns "Training the next generation of Earth Explorers" Diana Delgado at the NASA Ames Science Symposium

3 INTRODUCTION Introduc4on to California Groundwater The Central Valley Region of California supplies nearly 7 percent of the U.S. food supply ($21 billion annually). The Central Valley contains 1/6 th of na4on s irrigated land. California law does not require repor4ng of groundwater pumping. The aquifer is the second most heavily pumped in the U.S., supplying nearly 20% of the Na4on s groundwater. Monitoring groundwater is essen4al, but there are numerous spa4al limita4ons.

4 INTRODUCTION Study Area -Two regions where GRACE data is obtained: The Sacramento Hydrologic Region (blue) and the San Joaquin Hydrologic Region (red). -While not the regions and the aquifer are not the same size, the majority of change occurs in the Central Valley aquifer.

5 INTRODUCTION Study Goals Assess applicability for using remotely sensed data for assessing groundwater storage. Compare the California DWR C2VSIM hydrological model with GRACE derived changes in groundwater storage. Compare the DWR GIS Change in storage tool with GRACE derived changes in groundwater storage. Explore the use of satellite data on scales smaller than the Central Valley for aquifer management.

6 Gravity Recovery and Climate Experiment (GRACE) INTRODUCTION Twin satellites with iden4cal orbits Changes in gravity are determined by the rela4ve distance between the two satellites Time variable gravity can be detected by GRACE Output is total water storage anomaly

7 INTRODUCTION What is a gravity anomaly? The standard of Earth s gravity anomalies: -mountains = higher gravity -deltas = lower gravity (University of Texas Austin, 2011) Time-variable gravity anomalies converted into equivalent units of height in mm. Provides explanation for short term changes on earth like variations in water or ice.

8 Smoothing Radii 1000 km 300 km 100 km

9 INTRODUCTION Methods Calculate a water budget for the Central Valley to check GRACE TWS values. (Method 1) Calculate trend in groundwater anomalies using GRACE total water storage (TWS) values and satellite derived variables from October 2002 to October 2009 and compare it to the DWR s C2VSIM hydrological model. (Method 2) Compare GRACE derived groundwater es4mates with insitu groundwater levels (GIS change in storage tool). (Method 3)

10 Workflow Model METHODS

11 (Method 1) GRACE Total Water Storage METHODS Δ TWS (GRACE)

12 METHODS Δ TWS (GRACE) The TWS anomalies are varia4ons in the hydrological budget over a region. San Joaquin River Basin Gaussin Halfwidth: km Time Anomaly Measurement Error (mm) months (7 yrs) of GRACE data were obtained. Smoothing radius of 300 km Data obtained from a TRIP Basin model for both the Sacramento and San Joaquin River Basins and applied in GIS. Sacramento San Joaquin

13 METHODS Δ TWS (GRACE) Total water storage TWS = GW + SP + SM + SW To calculate groundwater anomalies GW = TWS (SP + SM + SW) Units of TWS are in mm, then converted into a volume by mul4plying by the area over which the anomaly is observed. For this study, all units are in km³

14 (Method 1) GRACE Total Water Storage METHODS Δ TWS (Hydrological Budget) Δ TWS (GRACE) Precipita4on River Runoff Evapotranspira4on

15 METHODS ΔTWS = P (ET + Q) Precipita4on PRISM from Oregon State University Based on models and real observa4ons (Method 1) Evapotranspira4on MODIS Sensor on the Satellite Aqua University of Montana River Runoff/Discharge Real 4me daily mean discharge data U.S. Geological Survey (USGS)

16 METHODS Comparison of ΔTWS! TWS "! #, GRACE TWSBudget Calculation of significance at a = 0.05 level

17 (Method 2) GRACE Total Water Storage METHODS Δ TWS (Hydrological Budget) Δ TWS (GRACE) Precipita4on River Runoff Evapotranspira4on Snow Water Equivalent Reservoir Storage Soil Moisture GW

18 METHODS Calcula4ng GW Anomaly Trends (Method 2)

19 METHODS Surface Water Variables Surface Water Storage California Data Exchange Center (CDEC) 20 largest reservoirs in study area Soil Moisture The Advanced Microwave Scanning Radiometer for EOS (AMSR E) Algorithm converts surface reflectance into soil moisture The value for each pixel is applied to an average depth of 15 meters Snow Pack Snow Data Assimila4on System (SNODAS) operated by the Na4onal Oceanic and Atmospheric Administra4on (NOAA) Calculates snow water equivalent (SWE)

20 METHODS Propaga4on of Uncertainty Rule StaRsRcal Method to determine appropriate amount of uncertainty to be applied to groundwater calcularons GW ( S ) ( S ) 2 ( S ) 2 ( S ) 2 TW 2 SW SM SP S = GRACE TW storage data: 2 Types of Error 1. Measurement Error 2. Leakage Error Error for each hydrological variable is assumed to be 15% Error varies depending on the smoothing radius

21 Anomaly Trends -Seasonal trends in all variables -SW, SM, and SP display fairly steady levels (slight or no decline) while TWS shows decreasing trend after 2006

22 (Method 2) GRACE Total Water Storage METHODS C2VSIM Δ TWS (Hydrological Budget) Δ TWS (GRACE) Precipita4on River Runoff Evapotranspira4on GW Snow Water Equivalent Reservoir Storage GW Soil Moisture

23 METHODS C2VSIM Groundwater A finite element hydrological model built to es4mate water storage in the Central Valley aquifer Model parameters (not limited to): P, Q, ET, subsidence, beginning and ending GW for each month Calculated anomalies for the data set A trend equa4on was fiped from these anomalies, which was used to find a total volume (Method 2)

24 (Method 3) GRACE Total Water Storage METHODS Δ TWS (Hydrological Budget) Δ TWS (GRACE) C2VSIM GIS Change in Storage Tool Precipita4on River Runoff Evapotranspira4on GW Snow Water Equivalent Reservoir Storage GW GW Soil Moisture

25 METHODS Δ GW: GIS Change in Storage Tool (Method 3) The GIS change in storage tool was developed by the DWR The tool interpolates the water levels of wells into TIN layers Can be used to find change in depths and change in volume over the Central Valley Storage coefficient of 0.07 is used (based on a mixture of unconfined and confined aquifer) Comparisons are made on a yearly basis (spring to spring)

26 GRACE Total Water Storage METHODS Δ TWS (Hydrological Budget) Δ TWS (GRACE) C2VSIM GIS Change in Storage Tool Precipita4on River Runoff Evapotranspira4on GW Snow Water Equivalent Reservoir Storage GW GW Soil Moisture

27 Comparison of GW Storage Trends RESULTS

28 RESULTS Comparison of GW Storage - Groundwater estimates for 300 km smoothing radius km chosen based on study area and acceptable amount of error in GRACE estimates - Although estimates from GRACE and C2VSIM are comparable for Central Valley, estimates are not similar for smaller basins

29 RESULTS Groundwater Storage and Smoothing Radius 1,000 km 750 km 300 km 100 km

30 CONCLUSION Conclusions: California Study The GRACE method produces varying results depending on the smoothing radius used. The GRACE method (with a 300 km smoothing radius) and C2VSIM were comparable, however GRACE cannot be downscaled without significant errors. In the Sacramento River Basin, the GRACE method, C2VSIM, and the GIS Change in Storage Tool produced varying results During the time period considered, there was a net decrease in groundwater storage according to all three methods.

31 GRACE Data Throughout the World Amazon River Basin Northwestern India (Rodell et al., 2009)

32 Obtaining GRACE Data 1. TRIP-defined Basins 2 Methods for Obtaining GRACE data: 1.Download specified monthly values for each basin (TRIP) 2. GRACE Tellus 2.Download monthly values for the world and then cut and smooth data to specific region (GRACE Tellus) TRIP could be used for Spain, but not for Saudi Arabia GRACE Tellus could be used for both, but involves significantly more processing

33 TRIP Defined Basins in Spain (FDV, Madrid, 2008) TRIP data could be obtained for each of the basins outlined (top right), but TRIP does not define all other outlined basins (above). Example of data obtained from the Erbo River Basin using TRIP.

34 GRACE Tellus Data in Saudi Arabia GRACE Tellus Processing Requirements The 1 degree pixels (shown here) needs to be smoothed to reduce the error Additional processing must be performed

35 Required Data Necessary: Total Water Storage (TWS) Satellite Reservoir Storage (SW) Country Specific Data Soil Moisture (SM) Satellite Snow Water Equivalent (SP) Country Specific Data Helpful to validate GRACE data : Δ TWS = P (ET + Q) Precipitation (P) Country Specific Data Evapotranspiration (ET) Satellite River Discharge (Q) Country Specific Data

36 Next Steps Obtain and process GRACE data Obtain and process additional variables data Compare data with existing hydrological models or well data from specific region Any additional data that may be useful or aid in the development of future projects such as geologic characteristics, pumping and groundwater data for agriculture, industrial, or domestic usage.

37 CONCLUSION Thank You! Thank you to our partner Thank you to everyone else who helped with this project Thank you to everyone who made this mee4ng possible.

COMPARISON OF CHANGES IN GROUNDWATER STORAGE USING GRACE DATA AND A HYDROLOGICAL MODEL IN CALIFORNIA S CENTRAL VALLEY

COMPARISON OF CHANGES IN GROUNDWATER STORAGE USING GRACE DATA AND A HYDROLOGICAL MODEL IN CALIFORNIA S CENTRAL VALLEY Amber Kuss, San Francisco State University William T. Brandt, California State University,