EvanMorganSample

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.

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

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

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.

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

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

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

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.

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,

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

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 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.

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.

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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