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Analyzing the Potential for Small Hydroelectric Power

Installment in the Dominican Republic

Blake D. Buehler

A project submitted to the faculty of

Brigham Young University

in partial fulfillment of the requirements for the degree of

Master of Science

E. James Nelson, Chair

Rollin H. Hotchkiss

Gustavious P. Williams

Department of Civil and Environmental Engineering

Brigham Young University

December 2011

Copyright © 2011 Blake D. Buehler

All Rights Reserved

ABSTRACT

Analyzing the Potential for Small Hydroelectric Power

Installment in the Dominican Republic

Blake D. Buehler

Department of Civil and Environmental Engineering, BYU

Master of Science

The Dominican Republic (DR) is in need of reliable, cost-effective power generation. A

prolonged electricity crisis and increasing power demand have left over seven percent of citizens

without access to electricity, and much of the population suffers from sporadic outages. The

purpose of this project is to build a methodology to evaluate small hydropower potential, which

can be used to alleviate the DR‟s energy problem among rural communities. The work is being

done for the DR‟s national water resource institute—the Instituto Nacional de Recursos

Hidràulicos (INDRHI)—which is overseeing the design and construction of multiple small

hydroelectric power projects (SHEPs) within the country.

This project has three major tasks: the design of a simple SHEP for a single location

along a river in the DR; the development of water flow prediction equations through a linear

regression analysis; and the design of an ArcGIS toolset to estimate the flow duration curves

(FDCs) at locations where data do not exist. An explanation of the inputs to the tool, as well has

how it produces a suitable output for SHEP evaluation will be presented. The paper also gives an

explanation of hydroelectric power generation in the DR, SHEPs, and the technical and practical

aspects of hydroelectric power.

INDRHI provided the temporal and spatial hydrologic data—drainage areas, precipitation

values, curve numbers (CNs), and slopes—for 13 different sites within the DR. An ordinary least

squares (OLS) analysis and manual numerical search for least square error (MNS) were both

calculated while implementing the regression procedure. The flow prediction equations are based

on the more accurate MNS method. Equations for flows ranging from 99 percent to 20 percent

were developed, with the emphasis being placed on the top 30 percent of flows. The specific

percentages used are 99, 95, 90, 85, 80, 75, 70, 60, 50, 40, 30, 20, 10, and 1. The regression

analysis was unable to yield equations for the 10 and 1 percentages because of high variability

introduced for less frequent flows. Because the lower percentages are not critical for locating

SHEP sites, the missing equations do not prohibit the analysis of flow availability for SHEPs.

The flow prediction tool performs three main functions: the delineation of a watershed

using a pour point placed upon a digital elevation model (DEM); the extraction of temporal and

spatial hydrologic data from raster and polygon feature layers; and the calculation of a watershed

FDC using the flow prediction equation and extracted data. Using the statistical flow prediction

equations and ArcGIS toolset, INDRHI engineers can now determine which sites will make the

best use of the available SHEP financing. Future efforts to improve the accuracy of the equations

can be achieved by expanding the collection and implementation of additional hydrologic data.

Keywords: Blake D. Buehler, small hydropower, ArcGIS, regression analysis

ACKNOWLEDGMENTS

I would like to express appreciation for Dr. E. James Nelson, Dr. Rollin H. Hotchkiss,

and Dr. Gustavious P. Williams for their assistance in editing this paper, as well as their

suggestions and ideas on how to approach this project. I would also like to thank Steve Hall and

Tyson Beaman for their research on turbines and their contributions to the SHEP design.

Additionally, I would like to thank INDRHI, and Fidel Perez in particular, for presenting this

project to work on and providing me with the data to accomplish it. Most importantly, I would

like to express my gratitude to my wife for her patience and support during the many months it

took to complete this project.

v

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................................ vii

LIST OF FIGURES ....................................................................................................................... ix

1 INTRODUCTION ................................................................................................................... 1

2 HYDROELECTRIC POWER GENERATION IN THE DOMINICAN REPUBLIC ........... 3

2.1 Current Power Generation in the DR ............................................................................... 3

2.2 Access for the Poor .......................................................................................................... 3

3 SHEPs ...................................................................................................................................... 5

3.1 History .............................................................................................................................. 5

3.2 Unique Aspects ................................................................................................................ 6

3.3 The Types ......................................................................................................................... 7

3.4 Turbines .......................................................................................................................... 10

3.5 SHEP Design .................................................................................................................. 14

4 TECHNICAL ASPECTS OF HYDROELECTRIC POWER GENERATION .................... 17

4.1 The Theory ..................................................................................................................... 17

4.2 Performance ................................................................................................................... 22

5 PRACTICAL ASPECTS OF HYDROELECTRIC POWER GENERATION ..................... 25

5.1 Advantages ..................................................................................................................... 25

5.2 Disadvantages................................................................................................................. 26

5.3 Financing ........................................................................................................................ 27

6 CASE STUDIES.................................................................................................................... 29

6.1 China .............................................................................................................................. 29

6.2 India ................................................................................................................................ 30

6.3 Turkey ............................................................................................................................ 30

vi

7 ANALYSIS ........................................................................................................................... 33

7.1 Regression Analysis ....................................................................................................... 33

7.2 Flow Duration Curves .................................................................................................... 44

7.3 ArcGIS Toolset .............................................................................................................. 49

7.3.1 Preparation .............................................................................................................. 50

7.3.2 Rivers ...................................................................................................................... 54

7.3.3 Pour Point Placement .............................................................................................. 55

7.3.4 Delineate ................................................................................................................. 56

7.3.5 Area_Convert .......................................................................................................... 57

7.3.6 Extract_Precip ......................................................................................................... 58

7.3.7 Slope ....................................................................................................................... 60

7.3.8 Extract Values to Table ........................................................................................... 61

7.3.9 Extract_CN ............................................................................................................. 63

7.3.10 ParametersToWatershed ......................................................................................... 63

7.3.11 FDC_Generation ..................................................................................................... 68

7.4 Comments....................................................................................................................... 72

8 CONCLUSION ..................................................................................................................... 75

REFERENCES ............................................................................................................................. 77

vii

LIST OF TABLES

Table 3-1: Five SHEP Setups (10). ................................................................................................ 9

Table 7-1: Data Provided by INDRHI ......................................................................................... 34

Table 7-2: Correlation Analysis ................................................................................................... 36

Table 7-3: Average Percent Error for Various Flows .................................................................. 37

Table 7-4: Standard Deviation for Various Flows ....................................................................... 37

Table 7-5: Minimum Square Error for Various Flows ................................................................ 38

Table 7-6: R2 Value for Various Flows ....................................................................................... 38

Table 7-7: Calculated vs. Original Canastica (Rincon) Hydrologic Parameters ......................... 68

Table 7-8: Basin Characteristics Used by States to Predict Stream Low Flows (23) .................. 73

ix

LIST OF FIGURES

Figure 3-1: A Typical SHEP (9) .................................................................................................... 7

Figure 3-2: Hydroelectric Power Station Types (10) ..................................................................... 8

Figure 3-3: Subdivision of SHEPs with a Powerhouse Located in the Riverbed (10) .................. 9

Figure 3-4: An Existing Diverted Pipe System Currently in Place Within the DR ..................... 10

Figure 3-5: A Pelton Turbine (14) ............................................................................................... 12

Figure 3-6: How Water Hits a Pelton Turbine (15) ..................................................................... 12

Figure 3-7: Michell-Banki Turbine (17) ...................................................................................... 13

Figure 3-8: SHEP Design—Bottom View (Google SketchUp) ................................................... 15

Figure 3-9: SHEP Design—Top View (Google SketchUp) ........................................................ 16

Figure 3-10: SHEP Design—Front View (Google SketchUp) .................................................... 16

Figure 4-1: Physical Layout of Typical Hydropower Station (10) .............................................. 19

Figure 4-2: Schematic of a Typical SHEP (11) ........................................................................... 21

Figure 4-3: System and Operating Performance (10) .................................................................. 23

Figure 4-4: Discharge, Head, and Power Duration Curves (10) .................................................. 24

Figure 7-1: Comparison of Mean Square Error for Different Regression Models ...................... 39

Figure 7-2: Relationship between Observed and Predicted Q99 Flow ......................................... 41




Figure 7-6: Relationship between Observed and Predicted Qmean Flow ...................................... 43

Figure 7-7: Relationship between Observed and Predicted Flow for All Recurrence Intervals .. 44

Figure 7-8: Predicted FDC for Boca de Lajas (Palomino) .......................................................... 45

Figure 7-9: Actual FDC for Boca de Lajas (Palomino) ............................................................... 46

Figure 7-10: Comparison FDCs for Boca De Lajas (Palomino) .................................................. 46

Figure 7-11: Predicted FDC for Carata (Joca El Corte) .............................................................. 47

Figure 7-12: Actual FDC for Carata (Joca El Corte) ................................................................... 48

Figure 7-13: Comparison FDCs for Carata .................................................................................. 49

Figure 7-14: Custom Flow Prediction ArcGIS Toolset ............................................................... 50

Figure 7-15: Mosaic DEM of the DR .......................................................................................... 51

Figure 7-16: Preparation Tool in Model Builder ........................................................................ 51

x

Figure 7-17: Flow Direction Output (Zoomed in to Show River Lines) ..................................... 52

Figure 7-18: Flow Accumulation Output ..................................................................................... 53

Figure 7-19: Rivers Tool in Model Builder ................................................................................. 54

Figure 7-20: Rivers Tool Output (Zoomed in to Show River Lines) ........................................... 54

Figure 7-21: Pour Point Placement .............................................................................................. 55

Figure 7-22: Delineate Tool in Model Builder ............................................................................ 56

Figure 7-23: Delineated Canastica (Rincon) Watershed ............................................................. 56

Figure 7-24: Two Delineated Watersheds ................................................................................... 57

Figure 7-25: Area_Convert Tool in Model Builder ..................................................................... 57

Figure 7-26: Canastica (Rincon) Watershed Polygon Attribute Table ........................................ 58

Figure 7-27: Extract_Precip Tool in Model Builder ................................................................... 58

Figure 7-28: Isohyetal Line Feature Class ................................................................................... 59

Figure 7-29: Canastica (Rincon) Extract_Prec_Table ................................................................. 59

Figure 7-30: Slope Tool in Model Builder ................................................................................... 60

Figure 7-31: Canastica (Rincon) Output Slope Raster ................................................................ 60

Figure 7-32: Environment Settings .............................................................................................. 61

Figure 7-33: Extract Values to Table Input for Slope .................................................................. 62

Figure 7-34: Canastica (Rincon) Extract_Slope_Table ............................................................... 62

Figure 7-35: Extract_CN Tool in Model Builder ........................................................................ 64

Figure 7-36: Canastica (Rincon) Combined Slope and Land Use Polygon Feature Class .......... 65

Figure 7-37: Canastica (Rincon) Average_CN Table .................................................................. 65

Figure 7-38: ParametersToWatershed Tool in Model Builder.................................................... 66

Figure 7-39: Canastica (Rincon) Watershed Complete Attribute Table ...................................... 67

Figure 7-40: FDC_Generation Tool in Model Builder ............................................................... 69

Figure 7-41: Blank FDC Table .................................................................................................... 70

Figure 7-42: Populated FDC Table .............................................................................................. 70

Figure 7-43: Visual Representation of Canastica (Rincon) FDC ................................................ 71

Figure 7-44: All Joins Must be Removed Before the Tool Can be Used Again ......................... 71

Figure 7-45: Statistical, Original, and ArcGIS FDCs .................................................................. 72

1

1 INTRODUCTION

Like many developing countries, the Dominican Republic (DR) is in need of reliable,

cost-effective, power generation. A prolonged electricity crisis has led to frequent power

outages, high operating costs for distribution companies, electricity theft through illegal

connections, and low collection rates. The DR has electricity transmission and distribution

losses of 40 percent, which are the highest among all Latin American countries. These

problems, as well as increasing power demand and over seven percent of citizens lacking access

to electricity within the DR, necessitate a solution (1). Consequently, small hydroelectric power

projects (SHEPs) can be an economical and effective means of alleviating the DR‟s energy

problem among rural areas.

The purpose of my master‟s project is to build a methodology to evaluate small

hydropower potential, which can be used to alleviate the DR‟s energy problem among rural

areas. The work is being done for the DR‟s national water resource institute—the Instituto

Nacional de Recursos Hidràulicos (INDRHI)—which is overseeing the design and construction

of multiple hydropower projects within the country.

This project has three major tasks: the design of a simple SHEP for a single location

along a river in the DR; the development of water flow prediction equations through a linear

regression analysis; and the design of an ArcGIS toolset to estimate the flow duration curves

(FDCs) at locations where data do not exist. An explanation of the inputs to the tool, as well has

how it produces a suitable output for SHEP evaluation will be presented. The paper also gives an

2

explanation of hydroelectric power generation in the DR, SHEPs, and the technical and practical

aspects of hydroelectric power.

This paper outlines the methods used to accomplish the three tasks outlined above. An

explanation of hydroelectric power generation in the DR, SHEPs, the technical and practical

aspects of hydroelectric power, the financing of hydroelectric power, and three case studies will

also be provided.

3

2 HYDROELECTRIC POWER GENERATION IN THE DOMINICAN REPUBLIC

Seven-and-one-half percent of the people in the Dominican Republic do not have access

to electricity. Compounding the problem is the reality that the DR lacks professionals with

electric energy systems expertise (2). Large investments in electric power, which can support

economic development and improve peoples‟ quality of life, are sorely needed (3).

2.1 Current Power Generation in the DR

As of July 2003, 16 percent of the DR‟s power generating capacity came from hydro-

electric power plants (4). SHEPs are becoming more popular in the country and many have been

installed in rural during the past few years. However, a comprehensive list of the SHEPs and

their power generation capacity has not been published at this time.

2.2 Access for the Poor

Poor urban households in developing countries spend 15-22 percent of their income on

energy. The poor usually pay higher prices for energy than the rich because the heat content of

the fuels used by the poor are lower and their appliances are inefficient (e.g., appliances fueled

by wood or charcoal are generally very inefficient compared to appliances fueled by electricity).

Some governments have attempted to target subsidies for certain fuels to make energy services

more accessible and affordable to the urban poor. These attempts have largely failed because the

subsidies have actually restricted access to the poor and have been diverted to other economic

groups. Ideally, energy would be neither heavily subsidized, heavily taxed, nor have import

4

restrictions. Such conditions would help all households because they would keep traditional

fuels, used by the poor, at affordable levels (5).

5

3 SHEPs

For over 70 years hydroelectric projects (HEPs) have been built for water storage and

hydroelectric power generation. More recently, SHEPs have become popular alternatives for

small, rural communities with limited financial resources. SHEPs are different than HEPs in

many ways and can be built in various setups.

3.1 History

During the 20th

century hundreds of massive barriers of concrete, rock, and earth were

placed across river valleys all over the world. These dams created huge artificial lakes and

provided many benefits: reliable power supplies, irrigation and flood control drinking water,

jobs, and recreational uses (6; 7). However, these dams did not come without a cost: thousands

of local inhabitants were displaced because of the flooding of nearby land as reservoirs filled.

Additionally, numerous environmental problems resulted from such major interference with river

flows: erosion, disruption of natural ecosystems, and possible impact on global climate change

(6).

Small and micro-hydro plants are more environmentally friendly power generation

options than larger projects and are now playing a key role in the rural electrification of many

countries. In fact, SHEPs are the main prospect for future hydroelectric developments in Europe

and other locations where large-scale dams have already been exploited or are now considered

environmentally unacceptable (6).

6

3.2 Unique Aspects

SHEPs are usually differentiated from larger HEPs by the amount of power they generate

(6). When referring to small hydropower in developing countries, and specifically in the DR, a

designation of 10 MW of hourly capacity will be used, as outlined by the Electrical Power

Resources Survey and Development Administration (EIE) (8). When referring to micro

hydropower, the European Union definition will be applied: plants having between 115kW and

1MW of hourly capacity (8).

SHEPs differ from HEPs in more than just size. SHEPs are generally used in smaller, less

developed communities where power generating equipment must be simple, reliable, and easily

maintained by non-specialists. They are best suited to generate power for dispersed rural

communities with limited needs and have five principal components: a suitable rainfall

catchment area, hydraulic head, a means of transporting water from intake to turbine, a turbine

house containing the power generation equipment and valve gear, and a tailrace to return water

to its natural course (3). Figure 3-1 depicts a typical SHEP.

Recent developments in small hydro technology have led to extremely robust systems

that can last for 50 plus years with minimal maintenance. SHEPs can also make a more

immediate impact on the replacement of fossil fuels when adequate water flow is available: they

can produce electricity on demand more efficiently than alternate energy sources that require

costly storage or backup systems. Some studies indicate that SHEPs are cost competitive with

fossil fuel power stations in remote rural areas that would be uneconomical to serve from larger

power networks (6).

7

Figure 3-1: A Typical SHEP (9)

Recent developments in small hydro technology have led to extremely robust systems

that can last for 50 plus years with minimal maintenance. SHEPs can also make a more

immediate impact on the replacement of fossil fuels when adequate water flow is available: they

can produce electricity on demand more efficiently than alternate energy sources that require

costly storage or backup systems. Some studies indicate that SHEPs are cost competitive with

fossil fuel power stations in remote rural areas that would be uneconomical to serve from larger

power networks (6).

3.3 The Types

Hydroelectric power stations can be divided into three main categories: low, medium, and

high head. Stations can further be classified as run-of-river or hydroelectric stations with

reservoirs. In actuality, most stations are mixed types, but for discussion purposes it is best to

distinguish them. Figure 3-2 shows the various breakdowns (10).

8

Figure 3-2: Hydroelectric Power Station Types (10)

SHEPs can be designed with the power house and dam placed in various locations within

the riverbed or near the riverbank. Three different setups are commonly used for powerhouses

placed in the riverbank: connected, detached, and submerged. These three setups can be further

subdivided, as show in Figure 3-3, resulting in five unique setups. Table 3-1 contains

descriptions of each of the five design types (10). Canals, pipes, or tunnels can also be used to

transport water to a powerhouse, which is the more likely scenario in the DR. Figure 3-4 shows

an existing diverted pipe system currently in place within the DR.

9

Figure 3-3: Subdivision of SHEPs with a Powerhouse Located in the Riverbed (10)

Table 3-1: Five SHEP Setups (10)

Setup Description

Conventional block design

The longitudinal axis of the power house and the dam are perpendicular

to the course of the river

Can only be used in areas with low flood potential)

Indented power station

Powerhouse set up outside the riverbed in an artificial bay

Required in very narrow streams so dam can use entire width of the river

Twin block power station Powerhouses placed on both sides of river

Built on rivers boarding two countries/regions

Power station in pier

Powerhouse is identical to the piers that support the gates of the barrage

Space-saving design for rivers with favorable flow conveyance

Submersible power station

Power station and dam are built in one block

Blends well with surrounding landscape

Minimum space required

10

Figure 3-4: An Existing Diverted Pipe System Currently in Place Within the DR

3.4 Turbines

Energy can be extracted from water in different ways, but the basic principles are always

the same: there must be a water source with a relative steady flow and a turbine that can harness

the energy that is produced by this flow. Depending on the characteristics of the water source,

different turbines can be used. This section gives a brief explanation of two turbine types—

Pelton and Michell-Banki—that could be successfully used in SHEPs within the DR, as well as

the reasons for my design using a diverted pipe system.

The Pelton turbine comes from the family of impulse1 turbines. This turbine is ideal for

rivers with medium to high change in head because it requires high velocity, rather than a high

1 power is derived from the force of water at high pressure hitting the passing buckets

11

volume of water (11). According to Popular Science Magazine (12), Pelton turbines can be run

on as little as 1.5 cfm of water, making it extremely useful for low flow SHEPs.

Pelton turbines can accept multiple jet streams, which improve their efficiency and power

generation potential. According to Z.H. Zhang (13), engineering professor at Dalian University

of Technology in China, Pelton turbines can easily achieve hydraulic efficiencies greater than 90

percent. The number of nozzles is determined by the size of the Pelton turbine and the number of

bowls. Nozzles are aligned so that each stream will only come in contact with one bucket at a

time.

Pelton turbines use two interlocking bowls, depicted in Figure 3-5, to harness a velocity

jet stream of water, as shown in Figure 3-6. When the water hits the bowls, the water is split and

each half is deflected at nearly 180 degrees: water is constantly forcing the turbine forward (11).

The Michell-Banki cross-flow turbine, as shown in Figure 3-7, was first developed in

1903 by Australian engineer Anthony Michell. However, it did not generate much interest and

was never widely distributed. Later, a Hungarian professor named Donat Banki independently

invented a similar turbine in Germany. Through a series of publications from 1917 to 1919

awareness of this turbine increased, which led to its more common use (14).

Unlike the Pelton model, the Michell-Banki turbine uses a broad rectangular water jet

that generates only a small amount of back pressure. The turbine uses two velocity stages.

During the first stage water goes through the runner blade, which then goes inward to the center

where it meets the cross blade. Once at the center, the same process is continued to release the

water outward without producing any back pressure. The introduction of air into the draft tube2

has enhanced the turbine‟s performance because it helps regulate the water head (14)

2 the flared passage leading vertically from a water turbine to its tailrace.

http://dictionary.reference.com/browse/water

12

Figure 3-5: A Pelton Turbine (15)

Figure 3-6: How Water Hits a Pelton Turbine (16)

http://www.ucmr.com/galerii/imagepages/image-cap31.html

http://www.ucmr.com/galerii/imagepages/image-cap31.html

13

Figure 3-7: Michell-Banki Turbine (17)

The Michell-Banki turbine is best implemented in rivers with low head and high water

volumes because it uses blades, which limit disruption to the flow of water, instead of bowls

(11). In fact, electric power can be generated in rivers with as little as three feet of head

difference (12). The hydraulic efficiencies of Michell-Banki turbines can be as high as 88

percent (18).

During my trip to the DR in March 2011, I visited four SHEPs currently in operation.

Each uses a diverted pipe system in place of a penstock and has a powerhouse placed near, but

not in, a stream. INDRHI wants to build SHEPs using diverted pipe systems because they are

less expensive than other alternatives and still achieve INDRHI‟s main goal: meeting the

electricity demands of small, rural locations throughout the country (19). As such, powerhouses

can be built alongside riverbanks, rather than within riverbeds. I recommend the use of the

14

Pelton turbine for SHEP designs in the DR because it can best make use of the large, drastic head

changes in fast moving rivers found throughout the country‟s rugged highlands and mountains.

3.5 SHEP Design

Using Google SketchUp, I designed a basic SHEP. I decided to use a small diversion

weir in the design of the SHEP because weirs are excellent at building up river elevation head

and providing a consistent flow to generate power. An inlet box, which will take in water and

filter obstructions such as sticks, rocks, and other debris that may damage the turbines, is located

in front the weir. A steady stream of water will travel through a 4-12 inch pipe and split into two

nozzles, from which the water will strike the Pelton turbine, thereby turning the motor and

creating power.

According to INDRHI, there should not be any boat travel along the rivers where SHEPs

will be placed, so a weir should not be a human hazard (20). Figure 3-8, Figure 3-9, and Figure

3-10 show various angles of the SHEP design that was created using Google SketchUp. The

figures are for visualization purposes only and were not built to scale.

15

Figure 3-8: SHEP Design—Bottom View (Google SketchUp)

16

Figure 3-9: SHEP Design—Top View (Google SketchUp)

Figure 3-10: SHEP Design—Front View (Google SketchUp)

17

4 TECHNICAL ASPECTS OF HYDROELECTRIC POWER GENERATION

In order to increase understanding, a brief explanation of hydroelectric power is provided.

The operating performance of SHEPs is explored as an introduction to the importance of being

able to predict river flow rates and calculate power generation over time.

4.1 The Theory

The potential energy found within falling water can be converted to electricity using

hydroelectric power plants. As defined by the authors of Renewable Energy: Technology,

Economics, and Environment (10), theoretical water power PWa,th between two points on a river

can be calculated using Equation 4-1:

( ) (4-1)

where, = water density

g = gravitational constant

= volumetric flow rate (through the hydroelectric power station)

= headwater elevation head

= tailwater elevation head

Transfer losses, often referred to as head loss, within a hydroelectric station cause a

portion of power calculated in Equation 4-1 to be lost. The Bernoulli equation, Equation 4-2, can

be written to represent these actual conditions (10).

18

(4-2)

where, z = potential energy (i.e., elevation head)

= pressure energy

= kinetic energy

= head loss

ξ = the loss coefficient

Hydro turbines convert water pressure into mechanical shaft power which, in turn, can be

used to drive an electricity generator or other machinery. The power available is proportional to

the product of pressure head and water discharge (6)

A hydroelectric power station is made up of many parts: a dam or weir, intake works, a

penstock3, a headrace

4, a powerhouse, and a tailrace

5. Figure 4-1 shows the physical layout of a

typical hydro station. The numbers in the figure indicate the path that water travels through the

hydropower plant in order to generate power. The energy line, energy loss, velocity head,

pressure head and the geodetic elevation6 of the streamline are also displayed (10).

3 a sluice or gate that controls water flow or an enclosed pipe that delivers water to the turbine

4 a channel that delivers flowing water to the turbine), a powerhouse, and a tailrace

5 a channel that delivers flowing water from the turbine

6 elevation head

http://en.wikipedia.org/wiki/Sluice

http://en.wikipedia.org/wiki/Floodgate

19

Figure 4-1: Physical Layout of Typical Hydropower Station (10)

The intake structure (i.e., the “screen” from Figure 4-1), which keeps floating debris out

of a plant, connects the headwater to the penstock or turbine. Stoplogs, contained within the

structure, enable the hydroelectric power station to be drained during maintenance work.

Similarly, quick-action stop valves, located nearby, stop the water flow into the station if an

accident were to occur. Local energy losses ( ) in the intake structure and flow resistance at the

screen prevent some energy from being generated by the turbine. Losses can be calculated using

Equation 4-3 (10).

( )

(4-3)

The penstock, the structure extending from point 2 to point 3 in Figure 4-1, bridges the

distance between the intake structure and the turbine. Potential energy is converted into pressure

20

energy in the penstock, with some friction losses in the pipes. Equation 4-4 shows the Bernoulli

Equation for the penstock (10).

( )

(4-4)

The loss coefficient of the penstock (ξPS) is a product of a friction factor and the diameter

of the penstock. One way to increase turbine power within a station is to increase the diameter of

the penstock, which reduces the friction encountered by flowing water. Station operators must

weigh the benefit of this added power generation to the increase in plant costs, due to the more

expensive penstock (10).

Run-of-river power stations7 transfer water flows from the intake structure into the

turbine via an upstream canal, pipe, or tunnel as show in Figure 4-2 (10). These SHEPs produce

power in the same way as HEPs only they require little, if any, damming and turbines are located

off the river banks or within the riverbed.

The turbine converts pressure energy into mechanical energy. Equation 4-5 describes

how much of the water power can be transformed into mechanical energy at the turbine shaft

(10).

7 power stations that use the natural flow of rivers and turbine generators to capture the kinetic energy carried by

water

21

Figure 4-2: Schematic of a Typical SHEP (10)

(4-5)

where, = power generated by the turbine

= turbine efficiency

= density of water

= gravitational constant

= volumetric flowrate

= usable head at the turbine

Some turbines, especially reaction types, generate more power when using a draft tube.

The cross-section of the flow at the end of the draft tube is larger diameter where the water exits

the generator. The gradual change in diameter causes a reduction of flow velocity before it enters

the tailwater. The fact that the tailwater energy line is much lower than the initial energy line is

22

evidence that the generator has successfully extracted the energy from the falling water.

Additional kinetic energy is lost due to turbulence in the tailwater. The Bernoulli equation

between the turbine outflow and draft tube outlet is written in Equation 4-6 (10).

(4-6)

The main hydraulic losses in a hydroelectric power station occur in the intake structure,

the penstock, and the outflow. The actual water power generated is calculated by subtracting

these losses from the theoretical water power, as shown in Equation 4-7 (10).

( )

(4-7)

An optimized plant design and layout can minimize losses that are dependent on the flow

velocity, and consequently improve turbine efficiency. Optimization is important because the

power from the shaft is determined my multiplying the actual available water power by the

turbine efficiency (10).

4.2 Performance

The operating performance for a SHEP, with respect to electrical energy output, depends

heavily on the available flow and current head. Figure 4-3 shows an example of the interaction

of these elements, as well as turbine flow, over the course of a single year (10).

23

Figure 4-3: System and Operating Performance (10)

Turbine flow is directly linked to the discharge of the river. Consequently, turbines can

only generate power for the maximum/design flow, and additional discharge goes unutilized. The

power generated by the hydroelectric power station is nearly identical to the flow through the

turbine, as found in Equation 4-5. Available head also affects power output, but it is usually less

significant than flow because it is more consistent (10).

Knowing the design flow and head allows operators to calculate the power generated

over time. The power forecast can be adjusted according to increasing and decreasing flows at

any given time of year. Decreasing flows result in decreased power generation and, at low

enough flows, a hydroelectric plant must be turned off to prevent the turbines from being

damaged. Such flows should be rare if the plant is designed properly and positioned correctly.

Conversely, increasing flows above the design capacity results in decreased power generation

24

because the turbine cannot process the discharge (i.e., increased flows cause the difference

between the head and the tail water heights to become insignificant) (10).

The ability to predict flow rates is necessary to properly operate a hydroelectric plant.

Water flow varies during different daily hours and seasons of the year and therefore must be

studied and evaluated before installing SHEPs. Calculating the optimal installation capacity8 at

any site involves determining, at minimum, the downstream river flow, environmental needs and

rights, and a FDC (10). Figure 4-4 shows how the discharge, head, and power duration curves

for a hydroelectric plant relate to plant design (10). One of the main purposes of my project is to

generate FDCs that can be used to predict flows, which should prevent plants from unnecessarily

being turned off.

Figure 4-4: Discharge, Head, and Power Duration Curves (10)

8 optimal designed flow

25

5 PRACTICAL ASPECTS OF HYDROELECTRIC POWER GENERATION

Implementing hydroelectric power projects is not limited to having a firm grasp of their

technical aspects. Practical aspects—economic, social, and environmental—must also be

considered. This section is dedicated to a discussion of the advantages and disadvantages of

HEPs and SHEPs, as well as the financing of such products.

5.1 Advantages

SHEPs have three distinct advantages over HEPs. First, SHEPs have lower operating

costs and longer life cycles than other large-scale generating options. Second, the water that

drives the generation is not affected by market fluctuations because it can still be used for

consumption downstream (3).Third, small hydro projects are becoming increasingly popular in

exploiting energy potential because they are a much less risky investment, via reduced economic

costs, than large dams (6).

Hydropower in developing countries, collectively, had more than double the hydropower

potential of industrialized countries as of 1999. Only about 20 percent of that potential had been

tapped in developing countries, whereas nearly 70 percent of the potential had been tapped in

developed countries (5). The DR is estimated to have nine TWh per year of economically

feasible untapped hydropower potential (13). If accessed, the hydropower could increase the

2007 production level by 64 percent (15).

26

Recently, SHEPs have been used to generate power in many rural areas that might not

have otherwise had power accessible to them. Previously, only HEPs, associated with high

investment costs, had been available options (8). The construction of large dams and reservoirs is

unnecessary when implementing SHEPs because small scale hydropower is produced mainly by

run-of-river systems: SHEPs require little or no reservoir storage and can be designed for various

river types because they use river stages within their natural ranges. Consequently, the

environmental impact of SHEPs is usually negligible compared to that of HEPs attached to large

dams. Depending on the desired environmental impact, all or some of the river flow can be used

for power generation. In addition, SHEPs create job opportunities in rural areas, provide

significant forward and backward linkages9, and increase demand for power-generating turbines

and other equipment that will benefit the industrial sectors of economies that produce them (6).

5.2 Disadvantages

SHEPs do have some disadvantages that offset their hydropower potential. Although

SHEPs require little water, in comparison to HEPs, the management of competing water uses and

the creation of barriers for fish migration must still be considered. Droughts could eliminate

most, if not all, power generation potential from SHEPs because minimum flows would likely

not occur without water storage10

. Additionally, when SHEPs are built directly into the riverbed

three considerations must be taken into account: flood management, shiplock operation, and

groundwater stabilization. In particular, if ships must navigate the river, power generation

revenues may not negate the cost of shiplock operation (10).

9 the benefits of economic investment tends to stay within a local economy and enhance other domestic industries

10 this disadvantage is not as critical in the DR where average rainfall varies between 50 and 60 inches (21)

27

5.3 Financing

SHEPs are desired in the DR because they are more economical for the needs of the rural

populations than HEPs, do not require storage, and have less of an environmental impact.

Generally, the main economic advantage comes in the form of smaller capital and maintenance

costs (10).

During my March 2011 visit to the DR I discovered that the European Union, as well as

the DR Government and various Latin American agencies, will be providing the financing for

the SHEPs being designed by INDRHI. This is a huge advantage for the DR because of the

challenges associated with finding investors and capital.

29

6 CASE STUDIES

Developing countries have been investing in SHEPs for over 110 years. However, in

recent years the level of investment has increased dramatically. Dr. IbrahimYüksel (8), professor

of construction at Sakarya University in Turkey, recently performed case studies of SHEP

development in three such countries: China, India, and Turkey. Summaries of his research

follow.

6.1 China

China has abundant SHEP resources, scattered throughout the country, with development

potential around 100 GW. From 1993-2005 the country invested $1.6 billion in small

hydropower to increase rural capacity by 1000MW yearly. By the end of 2002 the country had

42,221 SHEPs with installed capacity of 28,489 MW (approximately one quarter of the country‟s

total development potential). In the same year, the ratio of SHEPs to HEPs was projected to be

33.7%. Additional installed capacity via SHEPs is projected to increase at 2,000 MW annually

(8).

By 2010, small hydropower in China was projected to provide electricity to 1,400 rural

provinces—an increase of 800 provinces from 2001. Additionally, the country was expected to

have had a nonconventional renewable energy increase of 14.3 GW from 2001 to 2010 (8). I

have been unable to locate a published paper indicating whether or not hydropower growth in

China has met these predictions.

30

6.2 India

India began using micro hydropower in 1897. Since then, many SHEPs have been set up

in the country‟s hilly regions and at canal drops. However, huge electricity demands have led the

country to shift its focus to HEPs. The need for SHEPs still exists in some rural areas, so India

has maintained mature and reliable technology to implement them when necessary. Efforts

during the past decades have focused on four improvements: efficiency of the equipment; design

and use of silt resistive material/coatings; reliable auto controls; and remote operation of units

(8).

In 2002 India had an estimated 15,000 MW of SHEP potential at 4,096 sites. By 2006 the

country had 420 plants with capacity up to 25 MW, and combined capacity of 1,423 MW. 187

projects, combining for 521 MW of installed capacity, were under construction at the time of this

article (8).

6.3 Turkey

Turkey began developing small hydropower in 1902. Since then, SHEPs have been

installed in many parts of the country. As with India, large, domestic energy demand has led the

government to focus on the development of HEPs. However, from the early 1970s to 2002,

SHEP capacity in Turkey still increased from 5-10 percent each year (8).

As of 2004, Turkey had an estimated installed capacity of 175 MW—or one-and-one-half

percent of total hydropower potential in country—within its SHEP network. The 70 in-operation

SHEPs were generating roughly 650 GW of energy, annually. Six more SHEPs were under

construction when the article was published (8).

31

In 2004, SHEPs constituted four percent of total exploited hydropower generation

potential, with HEPS exploiting the remaining 96 percent. In addition, 35 percent of Turkey‟s

total electricity production that year was generated from hydropower, with the remaining 65

percent coming from thermal plants (8).

33

7 ANALYSIS

Following a statistical procedure used by GLM Engineering (22), I performed a

regression analysis on 13 sites within the DR where drainage areas, precipitation values, curve

numbers, slopes, and FDCs were available. An ordinary least squares (OLS) and manual

numerical search for least square error (MNS) were both calculated while implementing the

procedure; the flow prediction equations are based on the more accurate MNS method. Using

these equations, as well as watershed hydrologic parameters from known datasets extracted with

tools from ArcMap®, predicted FDCs can be generated in Microsoft Excel for any site within

the DR.

7.1 Regression Analysis

INDRHI provided me with the hydrologic information in Table 7-1 and Table 7-2.

Temporal and spatial data—drainage areas, precipitation values, curve numbers, and slopes, and

FDCs—were collected from 13 different sites within the DR. Each of the four variables used in

the flow prediction equations can come from a GIS dataset and can be measured directly, except

for the CN. However, because the CNs is an indication of storage I determined to use it. The

duration and time frame of data collection varied at the 13 reference sites, although the raw data

was not available for the study. Hence, the regression analysis and subsequent water flow

equations are only as accurate as the given data.

34

Table 7-1: Data Provided by INDRHI

Station Name Drainage Area

(km2)

Rainfall

(mm/yr) CN Slope

Aguacate (Cerro El Medio) 77.48 1470.57 70.79 0.3897

Boca de Lajas (Palomino) 64.88 1293.96 76.39 3.3869

Boca de Lajas (Bohechio) 598.21 841.01 61.07 0.0273

Cabirma (Bulla) 653.49 1872.34 71.98 0.3114

Canastica (Inage) 111.91 1759.21 82.29 0.1981

Canastica (Rincon) 416.83 1508.76 82.03 0.3689

Carata (Arbonito El Corte) 466.50 1798.47 82.25 0.2508

Carata (Joca El Corte) 451.43 1798.47 82.25 0.2592

Majagual (El Millo) 34.13 1784.24 73.32 0.4484

Ingenito (Jaquime) 73.62 1327.04 82.29 0.4583

Vallesito (Palomino) 64.88 1293.96 76.39 3.3869

Vallesito (Bohechio) 598.21 841.01 61.07 0.0273

Montazo (Palomino) 64.88 1293.96 76.39 3.3869

Montazo (Bohechio) 598.21 841.01 61.07 0.0273

Higuera (Los Valencio) 196.20 1706.48 76.30 0.3605

Higuera (Jaquime) 218.82 1327.04 76.42 0.3663

Rancho el Pino (Arroyo el Limon) 405.83 1106.84 78.9 0.3789

Q99

(cms)

Q95

(cms)

Q90

(cms)

Q85

(cms)

Q80

(cms)

Q75

(cms)

Q70

(cms)

Q60

(cms)

Q50

(cms)

Q40

(cms)

Q30

(cms)

Q20

(cms)

0.26 0.28 0.32 0.35 0.38 0.41 0.45 0.51 0.58 0.64 0.71 0.93

0.89 2.29 2.84 3.22 3.60 3.94 4.27 5.15 6.15 7.26 8.82 11.46

1.34 2.11 2.60 2.97 3.29 3.65 3.98 4.71 5.62 6.91 8.62 11.25

4.39 5.64 6.58 7.49 8.40 9.34 10.38 12.91 15.79 19.21 23.30 30.28

0.30 0.51 0.75 0.83 0.90 0.97 1.05 1.19 1.53 2.41 3.01 3.50

1.97 2.76 3.22 3.64 4.00 4.36 4.77 5.68 6.66 8.50 10.07 13.55

1.70 2.20 2.83 3.34 3.91 4.50 5.09 6.80 8.33 10.12 12.41 15.52

0.94 1.64 1.89 2.14 2.40 2.69 3.01 3.75 4.86 6.04 7.31 9.15

0.52 0.59 0.68 0.77 0.86 0.94 1.03 1.21 1.39 3.63 2.14 2.69

0.27 0.36 0.47 0.57 0.68 0.78 0.86 1.01 1.17 1.39 1.70 2.21

0.89 2.29 2.84 3.22 3.60 3.94 4.27 5.15 6.15 7.26 8.82 11.46

1.34 2.11 2.60 2.97 3.29 3.65 3.98 4.71 5.62 6.91 8.62 11.25

0.89 2.29 2.84 3.22 3.60 3.94 4.27 5.15 6.15 7.26 8.82 11.46

1.34 2.11 2.60 2.97 3.29 3.65 3.98 4.71 5.62 6.91 8.62 11.25

1.60 1.99 2.48 2.68 2.88 3.07 3.27 3.72 4.28 4.94 5.72 6.82

4.20 6.65 9.68 11.38 12.21 12.23 12.65 13.48 14.71 16.38 18.43 21.69

0.83 1.52 1.83 1.99 2.15 2.30 2.45 2.73 3.00 3.26 3.61 4.21

35

Following the recommendation of INDRHI, flows at specific percentages were

interpolated from FDCs created for the 13 sites. I attempted to develop equations for flows

ranging from 99 percent to 1 percent, with the emphasis being placed on the top 30 percent of

flows. The specific percentages used are 99, 95, 90, 85, 80, 75, 70, 60, 50, 40, 30, 20, 10, and 1.

The regression analysis was unable to yield equations for the 10 and 1 percentages because of

high variability introduced at such low values. Because the lower percentages are not critical for

locating SHEP sites, the missing equations do not prohibit the analysis of flow availability for

SHEPs.

Following methods used by GLM Engineering in a minimum instream flow estimation

study at ungaged sites in Puerto Rico, I analyzed two regression models. First, the ordinary least

squares (OLS) model was used to estimate the regression coefficient vector of the linear model

for regression analyses. Second, the manual numerical search for least square error (MNS) was

used (22).

OLS minimizes the sum of squared residuals to estimate the following parameters:

drainage areas, precipitation values, curve numbers, and slopes. The OLS regression coefficient

vector ( ) was determined using matrix analysis and Equation 7-1 (22).

( ) (7-1)

Where, = Matrix of the watershed parameters

= Observed discharge vector

= Transform of Matrix X

36

The MNS method involves a numerical search of the least square error (MNS) using an

iterative Excel spreadsheet solver. I used the results from the OLS model as initial values and

with the „Solver‟ tool in Excel varied the parameters until the regression coefficients created the

minimum square error, relative to the original data.

I performed an analysis of variance (ANOVA) with a 0.05 significance level to determine

if any of the independent variables11

were significantly related to the dependent variable12

. The

results, found in Table 7-2, show that none of the variables were meaningful and that differences

are probably a product of chance.

A correlation analysis was also performed on the independent variables and the results

are shown in Table 7-3. Precipitation values and curve numbers had a 0.67 correlation, which

was not high enough to eliminate either. None of the other variables showed any significant

correlations, which were defined to be values greater or equal to 0.90.

Table 7-2: Correlation Analysis

Area Precipitation CN Slope

Area 1.00

Precipitation -0.26 1.00

CN -0.46 0.67 1.00

Slope -0.55 -0.08 0.18 1.00

Since none of the independent variables could be eliminated based on ANOVA or

correlation analyses, I used four standards to determine which variable combination13

should be

used to create flow prediction equations: the average percent error14

, standard deviation15

,

11

drainage areas, precipitation values, curve numbers, and slopes 12

flow 13

two or more independent variables used to collectively predict flow (the dependent variable) 14

the difference between predicted flow and actual observed flow at specific locations in the DR 15

based on a sample (ignores logical values and text in sample)

37

minimum square error16

and coefficient of determination17

(R2). These four standards are applied

at five of the most important percentage flow rates: 99, 95, 90, 85, and 50. Table 7-3, Table 7-4,

Table 7-5, and Table 7-6 show a breakdown of the four standards applied to each variable

combination.

Table 7-3: Average Percent Error for Various Flows

Variable Combination Flows

99 95 90 85 50

Area & Precipitation 42 68 76 77 62

Area & CN 49 71 78 79 68

Area & Slope 33 51 60 61 49

Precipitation & CN 50 -73 -73 -73 119

Precipitation & Slope 88 -9 -25 59 -34

CN & Slope 88 -5 66 -82 66

Area, Precipitation, and CN 40 66 76 77 63

Area, Precipitation, and Slope 31 75 -29 60 -4

Area, CN, Slope 21 35 46 48 36

Precipitation, CN, and Slope 50 6 -52 -57 155

Area, Precipitation, CN and Slope 33 43 51 53 46

Table 7-4: Standard Deviation for Various Flows


99 95 90 85 50

Area & Precipitation 0.93 1.39 1.55 1.57 1.42

Area & CN 0.94 1.43 1.57 1.58 1.47

Area & Slope 0.69 1.04 1.20 1.22 1.49

Precipitation & CN 1.63 0.61 0.49 0.48 2.72

Precipitation & Slope 1.76 1.36 0.70 1.76 1.24

CN & Slope 1.62 1.32 1.88 0.39 2.14

Area, Precipitation, and CN 0.96 1.45 1.62 1.65 1.62

Area, Precipitation, and Slope 1.74 1.28 0.58 1.19 0.57

Area, CN, Slope 0.55 0.84 1.05 1.09 1.04

Precipitation, CN, and Slope 1.69 1.50 0.63 0.52 3.60

Area, Precipitation, CN and Slope 0.65 0.98 1.17 1.21 1.29

16

a minimized version of the least square error 17

the square of the sample correlation coefficient between the outcomes and their predicted values

http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient

38

Table 7-5: Minimum Square Error for Various Flows


99 95 90 85 50


Area & CN 17.40 38.32 74.51 102.62 223.75

Area & Slope 14.21 30.76 64.12 89.27 167.02



CN & Slope 22.90 86.19 88.04 253.89 580.13



Area, CN, Slope 12.11 25.61 57.84 81.37 130.08



Table 7-6: R2 Value for Various Flows


99 95 90 85 50


Area & CN 0.248 0.155 0.113 0.109 0.213

Area & Slope 0.386 0.322 0.238 0.226 0.413



CN & Slope 0.012 0.011 0.007 0.000 0.002



Area, CN, Slope 0.477 0.435 0.312 0.294 0.542



The variable combination of area, CN, and slope had the lowest overall percent error—

21, 35, 46, 48, 36—for the five flow rates. The area, precipitation, CN, and slope combination—

33, 43, 51, 53, 46—followed closely behind with percentages ranging from five to 12 percent

higher. The same pattern held true for standard deviations: area, CN, and slope were the

lowest—0.55, 0.84, 1.05, 1.09, 1.04—with the four-variable combination having just slightly

higher values—0.65, 0.98, 1.17, 1.21, 1.29. However, the four-variable combination had the

lowest minimum square error values—10.87, 25.08, 57.28, 80.61, 121.38—which were

39

determined via linear regression analysis. Area, CN, and slope had the second lowest set of

values—12.11, 25.61, 57.84, 81.37, 130.08. The four-variable combination also had the highest

R-squared values—0.530, 0.447, 0.319, 0.300, and 0.574.

While the area, CN, and slope combination had lowest overall percent errors and standard

deviations, the four-variable combination had the lowest minimum square errors, the highest R-

squared values, and only slightly higher overall percent errors and standard deviations (than the

area, CN, and slope combination). Therefore, I decided to use the four-variable combination for

calculating my flow prediction equations. The OLS and MNS methods were both used during the

regression analysis of the four-variable combination. Figure 7-1 shows a comparison of the mean

square error for the two different regression models (22).

Figure 7-1: Comparison of Mean Square Error for Different Regression Models

1

10

100

1000

10000

Q99 Q95 Q90 Q85 Q80 Q75 Q70 Q60 Q50 Q40 Q30 Q20

Mea

n S

qu

are

Err

or

Regression Methods

MNS

OLS

40

The MNS method produced significantly lower mean square errors than the OLS method

and was the basis of the flow prediction equations below:

(7-2)

(7-3)

(7-4)

(7-5)

(7-6)

(7-7)

(7-8)

(7-9)

(7-10)

(7-11)

(7-12)

(7-13)

Figure 7-2, Figure 7-3, Figure 7-4, Figure 7-5, and Figure 7-6 compare actual stream

flows at the 13 DR reference sites with the values estimated by the flow prediction equations.

The equations appear to be reliable for all estimated values, as can be seen in Figure 7-7.

41

Figure 7-2: Relationship Between Observed and Predicted Q99 Flow


0.1

1.0

10.0

0.1 1.0 10.0

Pre

dic

ted

Q99 (

cms)

Observed Q99 (cms)

𝑄99 =7.683 102 𝐴0.729 𝑃0.916 𝐶𝑁3.826 𝑆0.380

0.1

1.0

10.0

0.1 1.0 10.0

Pre

dic

ted

Q95 (

cms)

Observed Q95 (cms)

95 =2.785 104 0.695 0.362 3.553 0.473

42



0.1

1.0

10.0

0.1 1.0 10.0

Pre

dic

ted

Q90 (

cms)

Observed Q90 (cms)

0.1

1.0

10.0

100.0

0.1 1.0 10.0 100.0

Pre

dic

ted

Q85 (

cms)

Observed Q85 (cms)

90 =1.168 104 0.640 0.292 3.118 0.435

85 =1.088 104 0.636 0.295 3.071 0.430

43

Figure 7-6: Relationship Between Observed and Predicted Qmean Flow

0.1

1.0

10.0

100.0

0.1 1.0 10.0 100.0

Pred

icte

d Q

mea

n (cm

s)

Observed Qmean (cms)

mean = 4.070 104 0.713 0.551 3.758 0.472

44

Figure 7-7: Relationship Between Observed and Predicted Flow for All Recurrence

Intervals Compared to Line of Perfect Correlation to Gage Station Data

7.2 Flow Duration Curves

I have chosen two of the project sites to demonstrate the predictive ability of the custom

flow duration curve prediction ArcGIS toolset. The predicted FDC for Boca de Lajas (Palomino)

generated using the above equations is shown in Figure 7-8. The actual FDC for Boca de Lajas

(Palomino), which was provided by INDRHI, is displayed in Figure 7-9. A comparison of the

two FDCs can also be found in Figure 7-10.

0.1

1.0

10.0

100.0

0.1 1.0 10.0 100.0

Pre

dic

ted

Flo

w (

cms)

Observed Flow (cms)

45

Figure 7-8: Predicted FDC for Boca de Lajas (Palomino)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Q (

cms)

% Excedence

75%=4.20

80%=3.92

85%=3.55

90%=3.12

95%=2.46

46

Figure 7-9: Actual FDC for Boca de Lajas (Palomino)

Figure 7-10: Comparison FDCs for Boca De Lajas (Palomino)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Q (

cms)

% Excedence

75%=3.93

80%=3.60

85%=3.22

90%= 2.83

95%=2.28

0

2

4

6

8

10

12

14

16

18

20

0 20 40 60 80 100

Q (

cms)

% Excedence

Developed Curve

Original Curve

47

The FDCs are very similar, but the statistical methods used to generate the equations are

not designed to predict extreme values located near the asymptote of the curve (i.e., the area

inside the red oval in Figure 7-8). However, for the purposes of determining where to place

SHEPs, the equations should be more than sufficient.

An additional predicted FDC for Carata (Joca El Corte) generated using the flow

prediction equations is shown in Figure 7-11. The actual FDC for Carata (Joca El Corte) is

displayed in Figure 7-12. A comparison of the two FDCs can also be found in Figure 7-13.

Figure 7-11: Predicted FDC for Carata (Joca El Corte)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Q (

cms)

% Excedence

75%=4.25

80%=3.92

85%=3.54

90%=3.09

95%=2.44

48

Figure 7-12: Actual FDC for Carata (Joca El Corte)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

Q (

cms)

% Excedence

75%=4.49

80%=3.39

85%=3.34

90%=2.82

95%=2.19

49

Figure 7-13: Comparison FDCs for Carata (Joca El Corte)

7.3 ArcGIS Toolset

I have created a custom flow duration curve prediction ArcGIS toolset (Figure 7-14) that

performs three main functions: the delineation of a watershed using a pour point placed upon a

digital elevation model (DEM); the extraction of temporal and spatial hydrologic data from raster

and polygon feature layers; and the calculation of a watershed FDC using the flow prediction

equations and calculated regression parameters for the ungaged watershed. Each of the custom

tools has been created using parameters. This allows the user to change the tool input and run

various models. As mentioned previously, the extracted hydrologic parameters include drainage

area, CN, average yearly precipitation, and average watershed slope. Each can come from a GIS

dataset, and in this case data from

0

2

4

6

8

10

12

14

16

18

20

0 20 40 60 80 100

Q (

cms)

% Excedence

Developed Curve

Original Curve

50

Table 7-1were used. This section outlines the order and details the execution of each tool

in the main toolset 10_Steps for the Canastica (Rincon) watershed.

Figure 7-14: Custom Flow Prediction ArcGIS Toolset

INDRHI provided me with eight DEMs which they obtained online. Using the Mosaic

tool within ArcGIS I combined the DEMs into one larger DEM (Figure 7-15). The grid cell area

for each DEM is approximately 30 meters by 30 meters and the projected coordinate system is

NAD_1983_UTM_Zone_19N.

7.3.1 Preparation

The Preparation tool (Figure 7-16) creates flow direction (Figure 7-17) and flow

accumulation (Figure 7-18) hydrology layers from the DEM. These layers are used to determine

the boundary conditions of selected river watersheds.

51

Figure 7-15: Mosaic DEM of the DR

Figure 7-16: Preparation Tool in Model Builder

52

Figure 7-17: Flow Direction Output (Zoomed in to Show River Lines)

53

Figure 7-18: Flow Accumulation Output

54

7.3.2 Rivers

The Rivers tool (Figure 7-19) allows the user to set the minimum flow capacity of the

rivers using the raster calculator. The output (Figure 7-20) is a vector layer that allows the user to

visually locate where to place the pour point for watershed delineation.

Figure 7-19: Rivers Tool in Model Builder

Figure 7-20: Rivers Tool Output (Zoomed in to Show River Lines)

55

7.3.3 Pour Point Placement

Once the river vectors have been created the pour point can be placed (Figure 7-21) using

the ArcGIS Editor command. The user must be careful to place the pour point within an existing

flow accumulation grid cell, or the next tool (Delineate) will not work properly. Multiple pour

points may be placed if the user desires to analyze more than one watershed.

Figure 7-21: Pour Point Placement

56

7.3.4 Delineate

The Delineate tool (Figure 7-22) creates a watershed polygon outlining the drainage area

corresponding to the pour point (Figure 7-23). When delineating multiple watersheds the tool

must be run for each pour point individually (Figure 7-24).

Figure 7-22: Delineate Tool in Model Builder

Figure 7-23: Delineated Canastica (Rincon) Watershed

57

Figure 7-24: Two Delineated Watersheds

7.3.5 Area_Convert

The Area_Convert tool (Figure 7-25) first creates a new field (Shape_Area_km2) within

the watershed polygon. Then, the watershed area is converted from square meters to square

kilometers using the Calculate field tool. The polygon values can be viewed within its attribute

table (Figure 7-26).

Figure 7-25: Area_Convert Tool in Model Builder

58

Figure 7-26: Canastica (Rincon) Watershed Polygon Attribute Table

7.3.6 Extract_Precip

The Extract_Precip tool (Figure 7-27) extracts the annual yearly precipitation value of

each cell within a watershed polygon from a raster (precipitation values interpolated from an

isohyetal line feature class as shown in Figure 7-28) and places them in a table (Figure 7-29).

Later, another tool (ParametersToWatershed) will be used to sum the hundreds or thousands of

cell values and compute one average precipitation value to be used in the flow prediction

equations.

Figure 7-27: Extract_Precip Tool in Model Builder

59

Figure 7-28: Isohyetal Line Feature Class

Figure 7-29: Canastica (Rincon) Extract_Prec_Table

60

7.3.7 Slope

The Slope tool (Figure 7-30), which is identical to the one ArcGIS defines in ArcMap,

calculates the slope for each cell on a specified raster surface and generates an output slope raster

(Figure 7-31). The output is defined in percent rise. The use must be careful to specify an

appropriate watershed mask in the Raster Analysis section of the Environment Settings (Figure

7-32).

Figure 7-30: Slope Tool in Model Builder

Figure 7-31: Canastica (Rincon) Output Slope Raster

61

Figure 7-32: Environment Settings

7.3.8 Extract Values to Table

The Extract Values to Table tool is a tool already defined in ArcMap. I was unable to

duplicate the functionality of the Extract Values to Table tool in Model Builder or Python, so the

predefined tool was used. The tool is used to extract slope values from an output slope raster and

place them in a table—serving nearly the same purpose as the Extract_Precip tool within a slope

context (Figure 7-33). The output is a Table containing hundreds or thousands of individual

slope values (Figure 7-34)

62

Figure 7-33: Extract Values to Table Input for Slope

Figure 7-34: Canastica (Rincon) Extract_Slope_Table

63

7.3.9 Extract_CN

The Extract_CN tool (Figure 7-35) extracts CN values from a clipped portion of a soil

and land use polygon feature class (Figure 7-36) and generates a composite CN. First, the tool

clips the soil and land use polygon to conform to the shape of the watershed polygon. Then, it

creates three new fields within the clipped polygon and populates them. The First field contains

the product of CNs and areas for each distinct polygon within the watershed. The Computed_CN

field divides each value in the First field by the sum of the entire watershed area. The output

table, Average_CN (Figure 7-37), is the sum of all values in the Computed_CN field and is the

composite CN value for the entire watershed.

7.3.10 ParametersToWatershed

The ParametersToWatershed tool (Figure 7-38) is the most complex of the custom

ArcGIS tools created for this project. First, the tool calculates the average yearly precipitation, as

well as the average slope, of a specified watershed and generates two output tables. Two fields

are then created within each table: an average slope/precipitation and Precip_ID. Next, both

tables are populated with their corresponding average slope/precipitation value and each

Precip_ID is assigned a value of “1”. Finally, the specified watershed polygon is also given a

Precip_ID of “1” and linked to the two output tables. The watershed polygon attribute table now

contains all the hydrologic data needed to generate FDCs (Figure 7-39).

64

Fig

ure

7-3

5:

Extr

act

_C

N T

ool

in M

od

el B

uil

der

65

Figure 7-36: Canastica (Rincon) Combined Slope and Land Use Polygon Feature Class

Figure 7-37: Canastica (Rincon) Average_CN Table

66

Fig

ure

7-3

8:

Para

met

ersT

oW

ate

rsh

ed T

ool

in M

od

el B

uil

der

67

Fig

ure

7-3

9:

Can

ast

ica (

Rin

con

) W

ate

rsh

ed C

om

ple

te A

ttri

bu

te T

ab

le

68

7.3.11 FDC_Generation

The FDC_Generation tool (Figure 7-40) is the final custom ArcGIS tools created for this

project. The watershed polygon is linked to the FDC Table (Figure 7-41)—a template for

calculating the flows for the FDCs. The hydrologic variables are then entered into the flow

prediction equations, which generate the minimum flows for the watershed (Figure 7-42). The

final output is an FDC for the input watershed (Figure 7-43). The user must unjoin the Final

PopulatedTable from the FDC Table to rerun the tool (Figure 7-44)

Table 7-7 contains the final output values generated by the custom flow duration curve

prediction ArcGIS toolset, as well as the original hydrologic values provided by INDRHI. The

statistically developed, original, raw ArcGIS, and ArcGIS (with predefined polygon shape)

curves have also been plotted in Excel for comparison purposes (Figure 7-45).

Table 7-7: Calculated vs. Original Canastica (Rincon) Hydrologic Parameters

Raw ArcGIS ArcGIS (Predefined Polygon) Original

Area 533 km2 417 km

2 417 km

2

Precipitation 1600 mm/year 1552 mm/year 1509 mm/year

CN 82.78 82.31 82.03

Slope 0.14 0.13 0.37

69

Fig

ure

7-4

0:

FD

C_G

ener

ati

on

Tool

in M

od

el B

uil

der

70

Figure 7-41: Blank FDC Table

Figure 7-42: Populated FDC Table

71

Figure 7-43: Visual Representation of Canastica (Rincon) FDC

Figure 7-44: All Joins Must be Removed Before the Tool Can be Used Again

72

Figure 7-45: Statistical, Original, and ArcGIS FDCs

7.4 Comments

The custom flow duration curve prediction ArcGIS toolset does not generally predict

FDCs as accurately as the statistical equations. Drainage areas, especially, tend to be farther off

than the other basin characteristics. Therefore, the user should pay particular attention when

delineated watersheds to make sure they correspond to actual watershed boundaries. When

possible, use a predefined polygon shape file when defining watershed boundaries.

0

2

4

6

8

10

12

14

0 20 40 60 80 100

Q (

cms)

% Excedence

Statistical FDC

Original FDC

Raw ArcGIS FDC

ArcGIS FDC (Predefined Polygon)

73

The four variables—drainage areas, precipitation values, curve numbers, and slopes—

used to develop the flow prediction equations were provided by INDRHI. These and other basin

characteristics can be used when developing equations to predict FDCs. Table 7-8 shows the

types of basin characteristics, and their frequencies, used by the 22 states (within the United

States) with low-flow prediction equations. Drainage area, precipitation, and elevation are the

three most commonly used variables with in the United States.

Table 7-8: Basin Characteristics Used by States to Predict Stream Low Flows (23)

Basin Characteristic Frequency

Drainage Area 11

Drainage Area with Qualifier 9

Mean Annual Precipitation 11

Mean Monthly/Seasonal Precipitation 4

Mean Basin Elevation 9

Mean Watershed Slope 5

Mean Channel Slope 2

Basin Relief 2

Other 9

75

8 CONCLUSION

The purpose of this project was to build a methodology to evaluate small hydropower

potential, which can be used to alleviate the DR‟s energy problem among rural communities.

This was accomplished through the development of water flow prediction equations through a

linear regression analysis, as well as the design of a custom flow duration curve prediction

ArcGIS toolset that estimates FDCs at locations were data do not exist. The flow prediction tool

performs three main functions: the delineation of a watershed using a pour point placed upon a

digital elevation model (DEM); the extraction of temporal and spatial hydrologic data from raster

and polygon feature layers; and the calculation of a watershed FDC using the flow prediction

equation and extracted data. An explanation of the inputs to the tool, as well has how it produces

a suitable output for SHEP evaluation was presented. A short discussion of hydroelectric power

generation in the DR, SHEPs, and the technical and practical aspects of hydroelectric power

were also found in the paper.

The four-variable combination—area, precipitation, CN, and slope—produced the best fit

for the data and was used in developing the regression equations for custom flow duration curve

prediction ArcGIS toolset. Using the statistical flow prediction equations and ArcGIS toolset,

INDRHI engineers can now determine which sites will make the best use of available SHEP

financing. Future efforts to improve the accuracy of the equations can be achieved by expanding

the collection and implementation of additional hydrologic data.

77

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