This document summarizes a study that aims to quantify potential recharge levels of Lake Cachuma reservoir in Santa Barbara County, California under different rainfall scenarios. The study will use hydrological modeling to estimate reservoir levels given average drought conditions and an El Niño year. Key factors that will be incorporated into the models include soil infiltration rates, evaporation rates calculated from solar radiation data, and evapotranspiration rates. The results will help water resource planners understand future water supply levels in Lake Cachuma under varying rainfall conditions.

1. Evan Morgan
ENVS 196
2/1/2016
Quantifying and Predicting Lake Cachuma Reservoir Recharge Given Various Rainfall
Levels
Abstract
California has historically been prone to periodic droughts that have major impacts on citizens
and the agricultural sector of the economy (Swain, 2014). The ability to predict and quantify
water supplies in California can provide integral information in regards to water resource
management. Hydrological modeling of watersheds can provide valuable insight into current
water balances (Paulson, 1991). The Lake Cachuma reservoir currently supplies primary surface
water to more than 200,000 people in Santa Barbara County. The reservoirs current reserves are
at 17% of total capacity. The loss of this water supply will have severe impacts of Santa Barbara
Counties source of potable water (SBCPWD, 2013). This study aims to quantify two possible
scenarios in which Lake Cachumas surrounding watersheds receive various amounts of
precipitation. The models will incorporate average precipitation for drought and an El Nino year.
A weighted raster input to the Accumulation Flow will reflect the loss of precipitation due to
evapotranspiration and infiltration through soils. Rainfall totals within the derived watersheds
will provide data regarding the future state of Lake Cachuma as water resource. Water resource
planners within Santa Barbara County can integrate the derived data into a comprehensive water
resource management strategy.

2. Background
California's topography and vegetation primarily exhibit Mediterranean features,
characterized by warm to hot, dry summers and mild to cool, wet winters (Aschmann, 1973).
There are a wide variety of biomes within California such as grassland, desert, chaparral,
woodland, conifer, and wetland. The prolonged drought and unusually warm temperatures have
affected the health of multiple ecosystems, and caused exceptionally poor air quality (Swain,
2014). California's drought has resulted in the declaration of a state-level “drought emergency”
and the federal designation of all 58 California counties as “natural disaster areas” (Swain,
2014). In order to effectively manage water resources Geographical Information Systems can be
used to model various regions in California.
This analysis concerns Santa Barbara's County source of potable water, the Lake
Cachuma Reservoir. The coordinates of Cuyama Valley, the area in which Lake Cachuma rests
is 34.9295° N, 119.5971° W (GEI, 2013). The reservoir was created in 1953 after the
construction of the Bradbury Dam to store the discharge from the Santa Ynez River (SBCPWD,
2013). Water is diverted from Lake Cachuma to distribute water to Summerland, Carpinteria,
Montecito, Goleta, and municipal users in the city of Santa Barbara. Lake Cachuma is currently
the primary surface water source for more than 200,000 people that reside in Santa Barbara
County. California is facing one of the most severe droughts on record, Jerry Brown has
declared a Drought State of Emergency in January 2014, asking for urban water agencies to
reduce water consumption by 20% (SBCPWD, 2013). Currently Santa Barbara County is
receiving 41% of historical mean rainfall, and rainfall for 2014 is ranked the driest year in
recorded history (SBCPWD, 2013). The current drought requires various counties to assess their
ability to supply water to the inhabitants and agricultural producers in their community. An

3. integral aspect of California's water supply is the possibility of an upcoming El Nino period in
2016. At present ocean temperatures along the California coast are 2 degrees Celsius above
normal, which places this year's El Nino with the most severe El Nino events on record similar to
1972–73, 1982–83, and 1997–98 droughts (AAAS, 2015). Recent studies indicate that the
probability of El Nino conditions persisting through spring of 2016 are roughly at 80%
(SBCPWD, 2013). It is important to note that a short term recover from an extreme weather
event such as El Nino does not necessarily imply a drought is over. In 1987–92, where March
1991 was exceptionally wet and temporarily reduced critical water demands, the increase in
rainfall did not end the 5 years drought (Steinemann, 2015).
This project concerns possible rainfall predictions in Santa Barbara County in 2016 and
years to come. Lake Cachuma reservoir storage is at an all-time low, pressuring Santa Barbara
County for new reliable sources of water. The California Department of Water Resources
estimates Lake Cachuma reservoir is at 17 percent capacity (RMC, 2015). In 2012 the water
demands of Santa Barbara County were estimated at 280,00 acre feet per year with 75% of that
water being allocated to agricultural irrigation (RMC, 2013). Santa Barbara County receives
various water sources including: 28,000 AFY imported water, 8,000 AFY local surface water,
25,000 AFY Lake Cachuma water, 154,000 AFY groundwater and 2,000 AFY recycled water
provided by water purveyors (GEI, 2013). Since Lake Cachuma source of water is significant to
Santa Barbara Counties supply, it seems necessary to forecast possible scenarios for the Lake
Cachuma Reservoir.
There are several options being considered to increase water supply for Santa Barbara
County given a prolonged drought. The Lake Cachuma Dam could be raised to its maximum
height of 800 feet, increasing storage up to 197,000 acre feet. This increase in storage would

4. allow for storing peak weather events when they occur adding an additional 34,500 acre feet per
year from the Santa Ynez River (RMC, 2015). An additional option that would increase water
supply is to remove the accumulated sediment within the Lake Cachuma Reservoir. The removal
of this sediment would allow for 20,900 additional acre feet of capacity to the Lake Cachuma
Reservoir (RMC, 2015). It must be noted that removing sediment is not without controversy,
Santa Barbara County is highly dependent on Lake Cachumas water supply so reducing the
water level low enough to remove sediment will strain supply to the cities within the county
(RMC, 2015).
Study Area
There are four major reservoirs located within the County of Santa Barbara. The study
area involved in my analysis is primarily focused on the reservoir of Lake Cachuma. Lake
Cachuma is owned and operated by the U.S. Bureau of Reclamation (SBCPWD, 2013). Water
from the reservoir is diverted through the Tecolote Tunnel to the Counties Coastal Area. The
tunnel outlet carries water through the South Coast Conduit, and uses lateral systems to deliver
water to Goleta, Montecito, Summerland, Carpinteria, and municipal users in the city of Santa
Barbara (SBCPWD, 2013). Lake Cachumas reservoir total surface area spans 3,100 acres within
Cuyama Valley. Within Cuyama Valley rainfall can vary drastically, annual precipitation ranges
from 8 inches near Cuyama Valley to a maximum of about 36 inches at the uppermost elevations
of the Santa Ynez Mountains (RMC, 2015). The capacity of Lake Cachuma when constructed
was roughly 214,200 acre feet. Currently the reservoir can store 193,399 acre feet, 20,900 acre
feet less than its original capacity (RMC, 2015). This difference in capacity resulted from 20,900
acre feet of sediment that has accumulated within the reservoir.

5. The rainfall around Lake Cachuma averages roughly 20.19 inches per year. During recent
drought years such as 2014 rainfall was 10.43 inches, and the minimum rainfall since Lake
Cachuma Reservoir construction is 7.33 inches in 2007. The maximum rainfall recorded for
Lake Cachuma is 53.37 inches in 1998 (SBCPWD, 2013). These estimates of rainfall within
Cuyama Valley will serve as a guide for my hydrological analysis. Two models of rainfall within
my analysis will be presented. One model will consist of the trending drought rainfall pattern.
Given that an inch or so of variance in rainfall would be arbitrary within this model, 11 inches of
rainfall per cell will be the starting point of analysis prior to other variables affecting this
value. The same logic will be used to determine an El Nino rainfall year. Since there is more
variance in regards to the range of rainfall above the mean, a value such as 40 inches of rain per
cell before alteration with other factors will suffice.
An important factor that will affect reservoir recharge is evaporation on the surface of
Lake Cachuma. Additionally, evapotranspiration that will occur in the watersheds draining into
Lake Cachuma. It is estimated that Lake Cachuma loses 16,000 acre feet of water annually to
evaporation (SBCPWD, 2013). Obtaining evapotranspiration rates is necessary to quantify the
amount of precipitation lost within the watersheds that contribute to Lake Cachuma. The tool
Area Solar Radiation is used to calculate the amount of solar insolation each cell is receiving. A
simple conversion using Latent Heat of Vaporization of water will provide estimates of rainfall
lost per cell to evapotranspiration.
The watershed drainage will be affected by the various soils leading up to each watershed
pour point. The soil attributes will either force water downstream of the watershed or percolate
through the soil to ultimately reach regional groundwater. To incorporate this data into my
model, soil data is retrieved from Natural Resource Conservation Service. The data is sourced

6. from the National Resource Conservation Service SSURGO database in the form a feature layer
with projected coordinates of USA_Contiguous_Albers_Equal_Area_Conic. The soil types will
be reclassified by their rainfall permeability. A DEM will be necessary to derive the watersheds
that drain into Lake Cachuma. This layer will be retrieved from the US Geological Survey, and
will come in the form of a 30 by 30 raster DEM. Using the various hydrology tools the
watersheds will be derived from the elevation data.
The primary variables affecting the reservoirs potential recharge is soil, evaporation, and
evapotranspiration. These variables are chosen since they are the dominant determinants of Lake
Cachuma’s current capacity. The mentioned factors are integrated to express the average rainfall
that Lake Cachuma retains in a drought year, as well as an El Nino year.
Methods and Data
Watershed and Pour Point Derivation
Two DEM’s are obtained from USGS EarthExplorer database. The formats of DEMs are
SRTM, resolution 1 Arc Second Global. In order to contain calculations within one DEM the
Mosaic to New Raster tool was utilized. The combined rasters define the extent of the study area
within this model. The output mosaic raster was still in the geographic coordinates of
GCS_WGS_1984 when obtained. The Project tool is used to convert the DEM to the projected
coordinate system NAD1983_2011StatePlaneCalifornia_III_FIPS0403.
For accurate hydrological modeling the Fill tool is used to correct for spurious errors
within the DEM. The output creates a filled DEM, which can be used as input for the Flow
Direction tool. After removal of elevation errors, the range of elevation is determined to be 0
meters to 2693 meters. Subsequently, the output flow direction layer is input for the flow
accumulation layer. The symbology is altered within the flow accumulation layer to have two

7. distinct classes. The first class is assigned 0-5,000 flows, which designates cells with low
accumulation. The second class 5,000-2,906,381 represent topographic features which are likely
to be areas that are rivers or streams in wet periods. The terminal ends of these features are
connected to Lake Cachuma, which will be established as the Pour Points.
An ArcGIS Map Package containing a feature layer of Lake Cachuma is retrieved from
the USGS National Hydrography Dataset. The feature layer geographic coordinate system was
different from that of the data frame. A transformation is used to change the coordinate system
GCS_North_American_1983 to NAD_1983_2011. The Lake Cachuma polygon is used for
representation in several maps.
Soil Infiltration and Runoff
Accurate hydrological modeling requires incorporation of soil characteristic data. In 1955
the NRCS created the Hydrology National Engineering Handbook assigning various soils to
hydrologic groups based on runoff, rainfall, and infiltrometer data. The soils classified indicate
minimum rate of infiltration obtained for bare soil after protracted wetting. The functionality of
these grouping depends on the premise that soils found within a region are similar in depth to a
restrictive layer, texture, structure, transmission rate of water, and degree of swelling when
saturated (USDA, 2005). These factors are expected to create homogenous run-off rates.
The SCS soil scientists have defined for major soil types that exemplify runoff potential
(USDA, 2005). Group A soils consist primarily of sand or gravel, and have high rates of water
transmission (greater than 0.30 in/hr.). Group B soils consist of moderately fine to moderately
coarse grains. These soils have a moderate infiltration when thoroughly saturated (0.15- 0.30
in/hr.). Group C soils primarily consist of moderately fine to fine grains that have a low
infiltration rate (0.05-0.15 in/hr.). Group D soils are chiefly composed of clay soils with a high

8. swelling potential. This soil creates a close to impervious surface, and has a very low rate of
water transmission (0-0.05 in/hr.).
Soil Groups A-D are used to quantify rainfall lost within the SSURGO data layer. A new
field in the soil layer is created attributing numerical values representing rain loss to each soil
type. An average of each soil types inch/hour infiltration rate is multiplied by the number of
hours of rainfall per year. There is a lack of empirical data in regards to hourly rainfall duration
due to data collection constraints. Average Annual “Precipitation Days” (i.e. precipitation
amounts to 0.01 inches 0.25 millimeters or more) data has been collected from 1981 to 2010 for
large cities in the United States. National Climatic Data Center confirms that Los Angeles
receives on average 35 days of rainfall per year (Arguez, 2012). California typically has rainfall
in concentrated short bursts, succeeded by longer periods of low rainfall (Aschmann, 1973). A
value of 30 minutes per day is chosen to emulate California's climatic behavior of rainfall in
short bursts. Given these factors, 17.5 hours per year of rainfall is chosen for this model. The
amount of hours is relatively low because infiltration requires prolonged wetting (USDA, 2005).
This criterion excludes common light rainfall events in California that do not fully saturate soils.
Using the various soil infiltration rates and annual hours of precipitation, values of lost
precipitation annually per unit area are determined: Soil A - 5.5 inches, Soil B - 3.93, Soil C -
1.75, and Soil D - .44. As an input into the weighted raster, the soils layer must be converted to
raster format. The Feature to Raster tool is used which converts the infiltration rate field to the
cell value, and output cell size to 30 meters. The raster is created to represent loss of
precipitation due to infiltration, and will be part of the weighted raster for the flow accumulation
layer.

9. Loss of Precipitation to Evapotranspiration
A major component of the hydrologic budget is evapotranspiration, making it vital to
incorporate this factor into hydrological models (Hanson, 1991). Regional and seasonal
variability of evapotranspiration plays a key role in the variance of evapotranspiration rates
(Hanson, 1991). The process of evapotranspiration involves evaporation from wetlands, snow
cover, bare soil, and transpiration from vegetation (Hanson, 1991).
Evapotranspiration is affected by three sources of illumination in the solar spectrum.
Direct Irradiance which includes shadows and self-shadowing cast on the nearby area. Diffuse
irradiance that is reflected toward the location of interest by nearby terrain. Diffuse Sky
Irradiance describes the obstruction of the overlying hemispheres radiation due to nearby
topographic features. Topography is the primary determining factor of the amount of solar
energy incident at a location on the earth’s surface (Dubayar, 1996). Solar insolation gradients
are created by orientation, variability in elevation, and shadows cast by topographic features
(Ruiz‐Arias, 2009).
A cost efficient method of estimating evapotranspiration is to create spatially- based solar
radiation models (Ruiz‐Arias, 2009). This can avoid large expenditures that involve building and
maintaining insolation monitoring stations. A necessary input to solar insolation models are
DEMs. The DEMs are used to derive topographic features such as shadow casting and surface
orientation to estimate incoming solar radiation at every point in the DEM (Ruiz‐Arias, 2009).
The estimations of these topographic features depend on the DEM resolution of choice (Ruiz‐
Arias, 2009).
There are a variety of input parameters to derive solar irradiation. The necessary
parameters consist of time of day, DEM, latitude, and length of time for calculation (Ruiz‐Arias,

10. 2009). The solar radiation occurring in the watersheds can be calculated by measuring number of
hours of radiation at a single site, and then converting the hours into radiation values by the use
of empirical relationships (Kumar 1997). This model seeks to calculate the potential solar
radiation, other parameters such as precipitable water content of the atmosphere or cloud cover
are not included. This information is not readily available, and the current parameters are
common practice (Kumar 1997).
The Area Solar Radiation tool is used to create a grid of solar radiation per cell. The input
layer is a 1 Arc-second Global DEM retrieved from the SRTM database. The DEM is in
the projected coordinate system NAD1983_2011StatePlaneCalifornia_III_FIPS0403 so that the
analysis can run without a Z factor. The input surface rasters spatial reference is used to
automatically calculate the correct latitude of the study area. The time configuration is set as
“Whole Year with Monthly Interval” in the current year of 2016. The time interval is restricted
to bi-weekly calculation due to the large period of calculation. Since the study time period is
relatively large the size of the skymap is set to 200 by 200 cells, which is the default setting. The
output raster represents the total amount of incoming solar insolation (direct and diffuse). The
unit of the output raster is expressed in watt hours per square meter.
There are several methods that attempt to derive evapotranspiration estimates for given
study areas. Methods often include parameters such as measured air temperature, cloudiness,
wind speed, and air humidity (Banimahd, 2016). Due to time constraints and a desire for
simplicity the mentioned parameters have been excluded.
The quantity of solar insolation received by each cell is used to estimate the lost
precipitation due to evapotranspiration. A series of conversions are expressed in Raster
Calculator to derive total lost rainfall. Wattage expressed in the output raster is converted to

11. kilojoules. A conversion factor of Latent Heat of Vaporization is calculated to estimate the total
volume of precipitation lost to evapotranspiration. The Latent Heat of vaporization 2257 kJ/kg
constant is used in our model. The final output raster is expressed in cubic inches of water lost
due to evapotranspiration. This layer is incorporated into the weighted raster layer through the
Raster Calculator
tool, and is input
for the weighted
raster flow
accumulation.
Results
11 and 40 Inches
of Rainfall
The process of
deriving
watersheds
surrounding Lake
Cachuma created
10 watersheds.
The drought year
model received 11 Inches of annual rainfall for all derived watersheds, and precipitation totaled
to 494692 cubic inches. The El Nino model received 40 inches of annual rainfall for the derived
watersheds, and totaled to 1,798,880 cubic inches of rainfall. The various Hydrologic Soil
Groups in total lost 53,140 cubic inches of water to annual soil infiltration. The annual quantity

12. of solar radiation
evaporated
5,958,308,784 cubic
inches of water. The
lakes annual
evaporation of 16,000
acre feet is converted
to 12,043,468,000
cubic inches.
Discussion
Derived Watersheds
and Pour Points
The derived
watersheds are
smaller and partially
misplaced in
comparison to defined
watersheds. Errors in
watershed derivation
are due to pour point creation and Lake Cachuma raster values. Optimized placement of pour
points may have established larger, more realistic watersheds. Additionally, the values within the
Lake Cachuma perimeter likely skewed watershed placement.

13. Evapotranspiration and
Evaporation Values
Topography is the
determinant of the
amount of solar energy
hitting a landscape, so
derived data should be
predominantly accurate.
To optimize
evapotranspiration
parameters such as air
temperature, cloudiness,
landcover, wind speed,
and air humidity should
be included.
Soil Infiltration
Soil classification within
the models are based off
empirically derived infiltration rates. The Hydrological Soil Classification is a strong foundation
to extrapolate infiltration values. A lack of empirical data in regards to rainfall duration creates
uncertainty in the extent of precipitation infiltration. Additionally, the exclusion of light rainfall
events that do not saturate soils could alter infiltration rates.

14. Overall Model Performance
The estimated precipitation sink variables greatly outweighed derived watershed
precipitation quantities. Inexact watershed and pour point derivation skewed precipitation values
to a lower range. Loss of precipitation by evapotranspiration and soil infiltration may have been
exaggerated due to oversimplifying their processes. Future hydrological analysis should consider
adding complexity to evapotranspiration derivation and incorporating empirical climate data to
influence soil infiltration rates.

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