U.S. Department of the Interior

U.S. Geological Survey

Scientific Investigations Report 2014-5168

Prepared in cooperation with the County of Maui Department of Water Supply and the State of Hawai‘i 

Commission on Water Resource Management

Spatially Distributed Groundwater Recharge Estimated Using a 

Water-Budget Model for the Island of Maui, Hawai‘i, 1978–2007
COVER

Top left: View toward summit of West Maui Mountain from a forested ridge near Honokōhau Valley on northern West Maui Mountain, 

Island of Maui, Hawai‘i. Photograph by Sarah N. Rosa, U.S. Geological Survey, 2013. Top center: View of Waihe‘e River on 

northeastern West Maui Mountain, Island of Maui, Hawai‘i, 2009. Top right: View of Ke‘anae Valley, on eastern Haleakalā, Island of 

Maui, Hawai‘i. Photograph by Adam G. Johnson, U.S. Geological Survey, 2014. Top right inset: Taro fields near Ke‘anae, eastern 

coast of Haleakalā, Island of Maui, Hawai‘i. Photograph by Adam G. Johnson, U.S. Geological Survey, 2014. Bottom right: View 

toward southern flank of Haleakalā from the coastline near Kaupō, Island of Maui, Hawai‘i. Photograph by Adam G. Johnson, U.S. 

Geological Survey, 2013. Bottom left: View of shrubs, grass, and barren rock on a steep valley wall near Ukumehame Gulch on 

southern West Maui Mountain, Island of Maui, Hawai‘i. Photograph by Adam G. Johnson, U.S. Geological Survey, 2010. Center: 

Map of the Island of Maui, Hawai‘i, showing the relative distribution of estimated mean annual recharge for average climate 

conditions. Estimated recharge is shown using a spectral color scale with red representing very low recharge and dark blue 

representing very high recharge.
Spatially Distributed Groundwater Recharge 

Estimated Using a Water-Budget Model for 

the Island of Maui, Hawai‘i, 1978–2007

By Adam G. Johnson, John A. Engott, and Maoya Bassiouni

Prepared in cooperation with the County of Maui Department of Water Supply and 

the State of Hawai‘i Commission on Water Resource Management

Scientific Investigations Report 2014–5168

U.S. Department of the Interior

U.S. Geological Survey
ii 

U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

Johnson, A.G., Engott, J.A., and Bassiouni, Maoya, 2014, Spatially distributed groundwater recharge estimated using 

a water-budget model for the Island of Maui, Hawai‘i, 1978–2007: U.S. Geological Survey Scientific Investigations 

Report 2014–5168, 53 p., http://dx.doi.org/10.3133/sir20145168.

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ISSN 2328-0328 (online)
 iii

Contents

Abstract ........................................................................................................................................................1

Introduction...................................................................................................................................................1

Purpose and Scope .............................................................................................................................4

Previous Studies..................................................................................................................................4

Description of Maui ......................................................................................................................................4

Physical Setting ...................................................................................................................................4

Climate ................................................................................................................................................5

Hydrogeology.......................................................................................................................................5

Surface Water......................................................................................................................................7

Soils ................................................................................................................................................7

Land Cover ..........................................................................................................................................7

Water-Budget Model.....................................................................................................................................7

Conceptual Model .............................................................................................................................10

Model Calculations ...........................................................................................................................10

Model Input.................................................................................................................................................14

Land Cover ........................................................................................................................................14

Impervious Surfaces .................................................................................................................15

Rainfall ..............................................................................................................................................15

Monthly Rainfall ........................................................................................................................15

Daily Rainfall.............................................................................................................................15

Fog Interception.................................................................................................................................16

Irrigation.............................................................................................................................................17

Irrigation Estimates ...................................................................................................................17

Irrigation Calibrations ................................................................................................................18

Septic-System Leaching....................................................................................................................18

Storm-Drain Systems.........................................................................................................................18

Direct Runoff......................................................................................................................................19

Computation of Seasonal Runoff-to-Rainfall Ratios..................................................................19

Runoff-to-Rainfall Ratios Assigned to Ungaged Catchment Zones .........................................24

Runoff-to-Rainfall Ratios Assigned to Gaged Basins within a Single Catchment Zone............25

Runoff-to-Rainfall Ratios Assigned to Gaged Basins with Multiple Catchment Zones..............25

Evapotranspiration.............................................................................................................................25

Forest-Canopy Evaporation and Net Precipitation....................................................................26

Potential Evapotranspiration.....................................................................................................27

Reference Evapotranspiration .........................................................................................27

Crop Coefficients .............................................................................................................27

Moisture-Storage Capacity of the Plant-Root Zone ..........................................................................30

Direct Recharge.................................................................................................................................30

Other Input.........................................................................................................................................30

Model Exclusions and Limitations ..............................................................................................................32

Model Randomness....................................................................................................................................33

Water-Budget and Groundwater-Recharge Estimates ...............................................................................33
iv 

Figures

 1. Aquifer sectors and aquifer systems on the Island of Maui, Hawai‘i ...................................................2

 2. Major geographical features on the Island of Maui, Hawai‘i................................................................3

 3. Mean annual rainfall (1978–2007) and locations of rain gages used in the water-budget 

calculations for Maui, Hawai‘i.......................................................................................................6

 4. Land cover for Maui, Hawai‘i...............................................................................................................8

 5. Generalized water-budget flow diagrams for both forest and nonforest land covers ........................ 11

 6. Mean runoff-to-rainfall ratios for the Dry-season months: May through October and Wet-season 

months: November through April for drainage basins of selected stream-gaging stations and 

catchment zones on Maui, Hawai‘i ............................................................................................20

 7. Mean annual reference evapotranspiration for Maui, Hawai‘i...........................................................28

 8. Calculated moisture-storage capacity of the plant-root zone for Maui..............................................31

 9. The average absolute percentage change in recharge of all the water-budget subareas with each 

successive model simulation .....................................................................................................33

10. The distribution of mean annual groundwater recharge for average climate conditions 

(1978–2007), Island of Maui, Hawai‘i, calculated using the water-budget model......................41

11. Fraction of total water inflow that becomes groundwater recharge in the water-budget calculations 

for average climate conditions (1978–2007) Island of Maui, Hawai‘i.........................................43

12. Percentage difference in mean annual groundwater recharge for drought conditions (1998–2002), 

relative to that for average climate conditions (1978–2007), Island of Maui, Hawai‘i ................46

Water-Budget and Recharge Estimates for Average Climate Conditions..........................................33

Comparison with Water Resources Protection Plan .................................................................42

Comparison with Previous Studies for the ‘Īao and Waihe‘e aquifer systems ..........................42

Comparison with Evapotranspiration Estimates of Giambelluca and others (2014).................44

Recharge for Drought Conditions ......................................................................................................45

Sensitivity Analysis ............................................................................................................................45

Suggestions for Future Study and Additional Data Collection....................................................................48

Summary and Conclusions.........................................................................................................................48

References Cited .......................................................................................................................................49

Tables

 1. Previous water-budget studies for parts of Maui, Hawai‘i ...................................................................4

 2. General types of land cover, as a fraction of aquifer system area, Maui, Hawai‘i...............................9

 3. Mean fog-interception rates used in the water-budget calculation for average climate conditions 

for Maui, Hawai‘i ........................................................................................................................16

 4. Mean seasonal runoff-to-rainfall ratios for drainage basins of stream-gaging stations on 

Kaua‘i, O‘ahu and Maui, Hawai‘i................................................................................................21

 5. Regional-regression equations used to compute mean seasonal runoff-to-rainfall ratios for 

catchment zones in ungaged areas on Maui Hawai‘i.................................................................24

 6. Land-cover parameters used in water-budget calculations for Maui, Hawai‘i...................................29

 7. Mean annual water-budget components from this study for aquifer sectors and all of Maui, 

Hawai‘i .......................................................................................................................................34

 8. Mean annual water-budget components from this study for average climate and drought 
 v

Acronyms

Acronyms Meaning

CWI Cloud water interception

EMI East Maui Irrigation

ET Evapotranspiration

HC&S Hawaiian Commercial and Sugar Company

MDWS County of Maui Department of Water Supply

TMK Tax Map Key

USGS U.S. Geological Survey

WRPP State of Hawai‘i Water Resource(s) Protection Plan

conditions and components from the Water Resources Protection Plan for aquifer systems of 

Maui, Hawai‘i .............................................................................................................................35

 9. Comparison of mean annual water-budget components from this study with components from 

previous studies for ‘Īao and Waihe‘e aquifer systems of Maui, Hawai‘i ...................................44

10. Results of sensitivity testing for selected water-budget parameters performed for selected aquifer 

systems and the Island of Maui, Hawai‘i....................................................................................47

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

square mile (mi2) 259.0 hectare (ha)

square mile (mi2) 2.590 square kilometer (km2)

Volume

gallon (gal) 3.785 liter (L)

gallon (gal) 0.003785 cubic meter (m3)

million gallons (Mgal) 3,785 cubic meter (m3)

Flow rate

gallons per acre per day [(gal/acre)/d] 0.935396 cubic meter per square kilometer per day 

[(m3/km2)/d]

million gallons per day (Mgal/d) 0.04381 cubic meter per second (m3/s)

Vertical coordinate information is referenced to mean sea level.

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Altitude, as used in this report, refers to distance above the vertical datum.
Acknowledgments

This study was conducted in cooperation with the County of Maui Department of Water 

Supply and the State of Hawai‘i Commission on Water Resource Management. We thank Abby 

Frazier and Thomas Giambelluca of the University of Hawai‘i at Mānoa, Cary Yamashita of 

the County of Maui Department of Public Works, and Tui Anderson of the County of Maui 

Department of Water Supply for providing data and background information. 
Spatially Distributed Groundwater Recharge Estimated 

Using a Water-Budget Model for the Island of Maui, Hawai‘i, 

1978–2007

By Adam G. Johnson, John A. Engott, and Maoya Bassiouni

additional freshwater demand. Average water production 

by MDWS was about 37.5 million gallons per day (Mgal/d) 

on the Island of Maui in 2013 (County of Maui, Hawai‘i, 

Department of Water Supply, 2014). Projected water 

demands from MDWS on the Island of Maui in 2030 (Haiku 

Design & Analysis, 2007, 2009, 2010) are between about 

12 and 57 percent more than the water production in 2013. 

Groundwater pumped from wells in the ‘Īao and Waihe‘e 

aquifer systems (fig. 1) is the chief source of freshwater for 

MDWS and is supplied to consumers in Wailuku, Kahului, 

Pā‘ia, and Kīhei (fig. 2). Average groundwater pumpage from 

the ‘Īao aquifer system by the MDWS in 2013 was about 

84 percent of the sustainable yield (David Taylor, MDWS, 

written commun., 2014). In upcountry Maui, which includes 

areas near Makawao on northwest Haleakalā (fig. 2), 

MDWS is faced with “a pressing need for additional water 

production capacity” and a considerable backlog of more 

consumers who are waiting for new water meters (Haiku 

Design & Analysis, 2009). Surface water, the chief source 

of freshwater for MDWS’ consumers in upcountry Maui, 

is already at its “practical limits” during the drier summer 

months and during drought conditions (Haiku Design & 

Analysis, 2009). In addition to MDWS, private water 

systems on Maui pump substantial amounts of groundwater. 

Some of these private water systems may also have increased 

groundwater demands in the future.

Estimates of the spatial distribution of groundwater 

recharge for Maui are needed to evaluate the availability 

of fresh groundwater. Recharge is water derived from 

precipitation and other sources of water, such as irrigation 

and leakage from septic systems, infiltrating the ground 

and replenishing aquifers. Recharge is used by the State of 

Hawai‘i Commission on Water Resource Management in the 

calculation of sustainable-yield values for aquifer systems in 

the State (State of Hawai‘i, 2008). For much of Haleakalā, 

including parts of upcountry Maui, the spatial distribution of 

recharge has not been estimated since 1999 (Shade, 1999). 

Although more recent recharge estimates for the isthmus and 

West Maui Mountain (fig. 2) are available (Engott and Vana, 

2007; Gingerich and Engott, 2012), new spatial datasets 

of rainfall and evapotranspiration (ET) for the Hawaiian 

Islands have been developed since these previous recharge 

Abstract

Demand for freshwater on the Island of Maui is 

expected to grow. To evaluate the availability of fresh 

groundwater, estimates of groundwater recharge are needed. 

A water-budget model with a daily computation interval was 

developed and used to estimate the spatial distribution of 

recharge on Maui for average climate conditions (1978–2007 

rainfall and 2010 land cover) and for drought conditions 

(1998–2002 rainfall and 2010 land cover). For average 

climate conditions, mean annual recharge for Maui is 

1,340 million gallons per day, or about 45 percent of 

precipitation (rainfall and fog interception). Recharge for 

average climate conditions is about 40 percent of total water 

inflow consisting of precipitation, irrigation, septic leachate, 

and seepage from reservoirs and cesspools. Most recharge 

occurs on the wet, windward slopes of Haleakalā and on 

the wet, uplands of West Maui Mountain. Dry, coastal areas 

generally have low recharge. In the dry isthmus, however, 

irrigated fields have greater recharge than nearby unirrigated 

areas. For drought conditions, mean annual recharge for 

Maui is about 1,035 million gallons per day, which is 23 

percent less than recharge for average climate conditions. 

For individual aquifer-system areas used for groundwater 

management, recharge for drought conditions is about 8 to 

51 percent less than recharge for average climate conditions. 

The spatial distribution of rainfall is the primary factor 

determining spatially distributed recharge estimates for 

most areas on Maui. In wet areas, recharge estimates are 

also sensitive to water-budget parameters that are related to 

runoff, fog interception, and forest-canopy evaporation. In 

dry areas, recharge estimates are most sensitive to irrigated 

crop areas and parameters related to evapotranspiration. 

Introduction

On the Island of Maui, the demand for freshwater is 

expected to increase. Groundwater is the chief source of 

freshwater for the County of Maui Department of Water 

Supply (MDWS) and is a potential source for satisfying 
2 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Pā‘ia

Kama‘ole

‘Īao Ha‘ikū

Makawao

Kahului

Waikapū

Honokōwai

Honolua

Honopou

Launiupoko

Waihe‘e

Honokōhau

Ukumehame

Kahakuloa

Olowalu

Waikamoi

Ke‘anae

Luala‘ilua

Nakula

Kaupō

Kīpahulu

Waiho‘i

Kawaipapa

Kūhiwa

LAHAINA

CENTRAL

KO‘OLAU

HĀNA

KAHIKINUI

WAILUKU

EXPLANATION

Pā‘ia

Aquifer sector area and nameAquifer system area and nameSedimentary deposits(modified from Sherrod and others, 2007)0-foot altitude contour of top of Wailuku Basalt (modified from Gingerich, 2008)0-foot altitude contour of top of Kula Volcanics/ HonomanūBasalt (modified from Gingerich, 2008)

CENTRAL

Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum.

156° 158° 160°

22°

20°

Hawai‘i

Moloka‘i

Maui

Kaho‘olawe

Lāna‘i

HAWAI‘I

O‘ahu

Kaua‘i

Ni‘ihau

PACIFIC

OCEAN

156°00'

156°10'

156°20'

156°30'

156°40'

21°00'

20°50'

20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

Figure 1. Aquifer sectors and aquifer systems on the Island of Maui, Hawai‘i (State of Hawai‘i, 2008).
Introduction 

3

"

"

"

"

"

"

"

"

"

"

"

"

"

"

"

"

"

0

0

Pu‘u Kukui

5,788 feet

Pu‘u ‘Ula‘ula 

(Red Hill)

10,023 feet

HALEAKALĀ

ISTHMUS

WEST MAUI 

MOUNTAIN

Kīpahulu Valley Kaupō Gap

ana

‘

e

K

e Val el y

U

p

c

o

u

n

t

r y

Hāna

Pā‘ia

Kaupō

Kīhei

Nāhiku

Ke‘anae

Waiehu

Nāpili

Mākena

Makawao

Kahului

Wailuku

Mā‘alaea

Lahaina

Kīpahulu

Pukalani

Kā‘anapali

1000

1000

2000 4000

2000

4000 6000 8000

CONTOUR INTERVAL, IN FEET, VARIES

VERTICAL DATUM IS LOCAL MEAN SEA LEVEL

Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum.

156°00'

156°10'

156°20'

156°30'

156°40'

21°00'

20°50'

20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

Figure 2. Major geographical features on the Island of Maui, Hawai‘i.

Introduction3
4 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

studies. These new datasets of rainfall and ET (Frazier, 2012; 

Giambelluca and others, 2013; 2014) have better spatial and 

temporal resolution than the datasets that were used before. 

For this study, the water-budget model used by Gingerich 

and Engott (2012) was modified and expanded to include the 

entire Island of Maui. Recharge estimates were calculated 

using the new datasets in combination with the modified 

water-budget model. The recharge estimates from this study 

can be used in numerical groundwater models that have the 

capacity to evaluate the effects of additional groundwater 

withdrawals on groundwater levels, streamflow, coastal 

discharge, and salinities in public and private wells on Maui. 

Purpose and Scope

This report describes the spatial distribution of mean 

annual groundwater recharge for the Island of Maui and the 

model that was used to estimate recharge for average and 

drought conditions. To estimate recharge, a water-budget 

model that uses a daily computational interval was developed. Hydrological processes and physical conditions that 

affect recharge on Maui were simulated in the water-budget 

model using the most current data available, including data 

from maps of rainfall for each month during 1978–2007 and 

mean monthly reference grass evapotranspiration (Frazier, 

2012; Giambelluca and others, 2013, 2014). The waterbudget model and the most current datasets were used to estimate the spatial distribution of mean annual recharge for two 

scenarios (1) average climate conditions (1978–2007 rainfall 

and 2010 land cover), and (2) drought conditions (1998–

2002 rainfall and 2010 land cover). Recharge estimates from 

this study were compared with estimates of mean recharge 

from previous water-budget studies. Finally, the sensitivity 

of recharge estimates to selected water-budget components 

was evaluated. 

Previous Studies

Several previous water-budget studies estimated 

recharge for various parts of Maui (table 1). Most of these 

previous water budgets used monthly or annual computational intervals, which can lead to biased evapotranspiration 

and recharge estimates (Oki, 2008). In general, uncertainty in 

recharge estimates is less for water budgets computed using 

a shorter computational interval that is consistent with the 

physical processes being represented (Oki, 2008). The water 

budget for this report used a daily computational interval, 

which provides a more realistic simulation of rainfall, soil 

moisture, ET, and recharge. 

The most recent estimates of recharge for areas in 

central and west Maui were those of Engott and Vana (2007) 

and Gingerich and Engott (2012). Engott and Vana (2007) 

developed a water-budget model with a daily computational 

interval to estimate recharge for central and west Maui 

for six time periods spanning 1926–2004. Their estimates 

incorporated historical rainfall and accounted for changes 

in land cover and agricultural irrigation. Recharge was also 

estimated for several hypothetical rainfall and land-use 

scenarios, including drought conditions and cessation of 

plantation-scale agriculture. Gingerich and Engott (2012) 

reassessed recharge for the Lahaina aquifer sector (fig. 1) 

using a modified version of the water-budget model of Engott 

and Vana (2007). The water-budget model was modified to (1) 

directly represent canopy-interception processes in forests, (2) 

distinguish between native and alien forests, and (3) account 

for differences in the transpiration properties of forests 

depending on their location with respect to the fog zone. 

Reference Area

Gingerich and Engott (2012) Lahaina aquifer sector

Engott and Vana (2007) west and central Maui

Shade (1999) part of Maui east of 156°30’ 00”

Shade (1997) ‘Īao aquifer system

Shade (1996) Lahaina aquifer sector

Austin, Tsutsumi and Associates 

(1991)

West Maui

State of Hawaii (1990) Entire island by aquifer system

Takasaki (1972) Central Maui

Takasaki (1971) Southeast Maui

Yamanaga and Huxel (1970) Wailuku Area

Division of Water and Land 

Development (1970)

Windward west Maui and central Maui

Belt, Collins and Associates 

(1969)

Lahaina District

Yamanaga and Huxel (1969) Lahaina District

Caskey (1968) ‘Īao and Waikapū Valleys

Table 1. Previous water-budget studies for parts of Maui, Hawai‘i.

Description of Maui 

The Island of Maui has an area of about 728 square 

miles (mi2). For groundwater management purposes, the 

State of Hawai‘i Commission on Water Resource Management divides the Island of Maui into 6 aquifer sectors and 25 

aquifer systems (fig. 1). 

Physical Setting

The Island of Maui was built by two shield volcanoes. The older West Maui Volcano is known as West Maui 

Mountain and may be extinct (fig. 2). The younger East Maui 

Volcano is known as Haleakalā and is considered dormant 

(Macdonald and others, 1983). The broad, gently sloping 

land between the two volcanoes is referred to as the isthmus (Stearns and Macdonald, 1942). Erosion of West Maui 

Mountain has carved deep valleys and sharp-crested ridges, 
Description of Maui 5

which radiate from near its summit, Pu‘u Kukui, at 5,788 feet 

(ft) (fig. 2). On Haleakalā, the rainy eastern slope has valleys 

that are separated by broad areas and ridges. The drier western slope of Haleakalā is less incised and retains the broad, 

shield shape of the volcano. The summit of Haleakalā, Pu‘u 

‘Ula‘ula (Red Hill), is at 10,023 ft. 

Climate

Weather patterns in Hawai‘i are affected by the interaction between northeast trade winds and the topography 

of the Hawaiian Islands (Schroeder, 1993). The Hawaiian 

Islands are in the path of persistent trade winds that originate 

from the north Pacific anticyclone, which is a region of high 

atmospheric pressure usually located northeast of the Islands. 

Mountains of the Hawaiian Islands obstruct trade-wind air 

flow and create wetter climates on north- and northeast-facing (windward) mountain slopes and drier climates on southwest-facing (leeward) mountain slopes (Sanderson, 1993). 

As moist air ascends windward mountain slopes, it cools 

and can form clouds. Persistent trade winds and orographic 

lifting of moist air result in recurrent clouds and frequent 

rainfall on windward slopes and near the peaks of all but the 

tallest mountains of the Hawaiian Islands (Giambelluca and 

others, 1986). Loss of moisture from air that ascends windward slopes leads to relatively drier climates along leeward 

slopes in the rain shadow of the mountains. 

When trade winds are present, the vertical development of clouds is restricted by the trade-wind inversion 

layer. Within the trade-wind inversion layer air temperature 

increases with altitude, whereas below the inversion layer 

air temperature generally decreases with altitude (Schroeder, 1993). Cao and others (2007) determined the tradewind inversion layer occurs about 82 percent of the time in 

Hawai‘i and has an average base altitude of about 7,100 ft. 

The altitude of the inversion, however, varies over time and 

space and is affected by thermal circulation patterns, such as 

land and sea breezes (Giambelluca and Nullet, 1991). Most 

of Maui is usually immersed in the moist air layer below 

the inversion. Areas near the summit of Haleakalā are high 

enough to extend into the layer of dry air above the inversion’s base altitude. 

The variability of weather and rainfall patterns in 

Hawai‘i during the year is typically described in terms of dry 

and wet seasons. The dry season (May through September) 

has warm temperatures and steady trade winds that blow 

80 to 95 percent of the time (Blumenstock and Price, 1967; 

Sanderson, 1993). The wet season (October through April) 

has cooler temperatures and less persistent trade winds. 

Statewide storm rainfall is more common during the rainy 

season when high- and low-pressure systems and frontal 

systems pass near the Islands (Giambelluca and others, 

1986). Much of the rainfall on leeward sides of the Hawaiian 

Islands comes from these synoptic-scale systems (Schroeder, 

1993). Low-pressure systems that develop to the west of the 

Hawaiian Islands can result in moist, southerly winds and 

rainfall that may persist for more than a week (Schroeder, 

1993). During the early part of 2006, for example, a series 

of low-pressure systems to the west of the Hawaiian Islands 

persisted for nearly seven weeks and generated an onslaught 

of storms that resulted in an unusual extended rainy period 

across the Islands (Nash and others, 2006).

Steep gradients in mean annual rainfall patterns on Maui 

(fig. 3) reflect the influence of persistent trade winds and 

orographic rainfall (Giambelluca and others, 2013). On an 

island-wide basis, mean rainfall on Maui is about 81 inches 

per year (in/yr). Mean rainfall is more than 360 in/yr at Pu‘u 

Kukui, the summit of West Maui Mountain (figs. 2 and 3). 

About 5 mi southwest of Pu‘u Kukui, mean rainfall is less 

than 15 in/yr. Mean rainfall exceeds 100 in/yr for much of 

the interior uplands of West Maui Mountain. On Haleakalā, 

mean rainfall exceeds 200 in/yr on mid-altitude windward 

slopes. At a rain gage (not shown) near 5,400 ft altitude on 

windward Haleakalā, mean rainfall is about 404 in/yr, which 

is among the highest values in the Hawaiian Islands during 1978–2007 (Giambelluca and others, 2013). Leeward 

slopes in the rain shadow of Haleakalā are much drier. Mean 

rainfall is less than 25 in/yr for most leeward areas along the 

coastline and the isthmus. The summit area of Haleakalā is 

also relatively dry, with mean rainfall between about 35 and 

50 in/yr.

Hydrogeology

West Maui Mountain and Haleakalā were built primarily 

by volcanic eruptions and layers of lava flows (Langenheim and 

Clague, 1987). The layers of lava flows were intruded in places 

by dikes, which consist of dense, low-permeability rock that 

formed when magma supplying lava flows solidified in narrow, 

near-vertical fissures below the ground surface. In the inland 

region of West Maui Mountain, near-vertical dikes radiating 

in all directions from the summit impound groundwater in 

compartments of volcanic rock in the caldera and permeable 

lava flows on the flanks. The water table of the dike-impounded 

groundwater systems in the West Maui Mountain interior may 

be more than 3,500 ft above sea level (Stearns and Macdonald, 

1942). Seaward of the dike-impounded systems within West 

Maui Mountain, freshwater-lens groundwater systems exist in 

the dike-free high-permeability volcanic rocks and sedimentary 

deposits (Gingerich, 2008). A freshwater-lens system consists 

of a lens-shaped freshwater body, an intermediate brackishwater transition zone, and underlying saltwater (for example, 

Gingerich and Engott, 2012, p. 12). Water levels of groundwater 

bodies in the dike-free volcanic rocks of West Maui Mountain 

are typically less than a few tens of feet above sea level (for 

example, Gingerich, 2008, p. 46). Fresh groundwater within the 

freshwater-lens system generally flows in a seaward direction 

from inland areas of West Maui Mountain toward the coast. 

Wedges of low-permeability sedimentary material referred 

to as caprock impede the seaward flow of fresh groundwater 

in freshwater-lens systems along parts of the northeast and 

southwest flanks of West Maui Mountain. Wedges of caprock 
6 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i 9765 8760 7194 4634 4193 4091 1930 1148 1125 1122 0175 94849376 9315 9303 9275 8955 8750 8744 3910 8688 8543 8336 8060 7566 7066 (5404) 7059 7040 6645 (9315) (4887) 6635 6521 5715 5665 5404 5177 5006 5003 5001 5000 4887 4806 4489 3910 2679 2630 2572 25520790 (1892) 1892 1887 1086 1016 1004 0999 0995 0832

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0 Pu‘u ‘Ula‘ula 

(Red Hill)

10,023 feet

EXPLANATION

Thiessen polygonLine of equal rainfall— Number is mean annual rainfall, in inches, interval varies (modified from Giambelluca and others, 2013) Rain gage with sufficient daily data— Number is gage’s Cooperative Network Index number, with prefix 51Rain gage with insufficient daily data— Numbers are Cooperative Network Index numbers, with prefix 51. Number in pink is for the gage shown. Number in black is that of a nearby gage whose data was used to compute daily rainfall within the enclosing Thiessen polygon.Cloud-water interception measurement site of Giambelluca and others (2011) and Scholl and others (2007)

200

Auwahi

48876635

(4887)

Waikamoi

Auwahi

Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum.

156°00'

156°10'

156°20'

156°30'

156°40'

21°00'

20°50'

20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

Figure 3. Mean annual rainfall (1978–2007) and locations of rain gages used in the water-budget calculations for Maui, Hawai‘i (modified from Giambelluca and others, 2011, 2013; Scholl and others, 2007). 
Water-Budget Model 7

between West Maui Mountain and Haleakalā also impede the 

flow of fresh groundwater between West Maui Mountain and 

the isthmus. 

On northeast Haleakalā, in the area between Makawao 

and Ke‘anae Valley, fresh saturated groundwater occurs as (1) 

perched, high-level water held up by relatively low-permeability 

geologic layers above an unsaturated zone, and (2) a freshwaterlens system underlain by seawater (Gingerich, 1999a, 1999b).

The perched groundwater is several tens of feet below the 

ground surface within layers of thick lava flows, ash, weathered 

clinker beds, and soils. Collectively, this assemblage of layers 

has low permeability that impedes the downward movement of 

the perched, high-level groundwater. An unsaturated zone and a 

freshwater-lens system are beneath the high-level groundwater. 

The freshwater-lens system is located within high-permeability 

basalt lava flows and has a water table that is several feet above 

sea level. In the area between Ke‘anae Valley and Nāhiku (fig. 

2), the groundwater system appears to be saturated above sea 

level to altitudes greater than 2,000 ft. For southeast and southwest Haleakalā, information related to groundwater systems 

is sparse although perched and freshwater-lens systems are 

expected to be present.

Surface Water

Streams on Maui generally originate in the wet uplands of 

West Maui Mountain and Haleakalā and flow toward the coast. 

The upper reaches of some streams on West Maui Mountain 

flow perennially and are fed by persistent rainfall and groundwater discharging from dike-impounded water bodies (Stearns 

and Macdonald, 1942). During dry-weather conditions, lower 

reaches of some streams on West Maui Mountain have reduced 

or no streamflow as a result of water captured by diversion 

systems and water infiltrating the subsurface (Gingerich, 2008; 

Gingerich and Engott, 2012). Streams on windward Haleakalā 

are fed by abundant rainfall and groundwater discharge (Gingerich, 1999a, 1999b). In the area between Makawao and 

Ke‘anae Valley (fig. 2), groundwater discharges to streams from 

a perched, high-level saturated groundwater system. East of 

Ke‘anae Valley, groundwater discharges to streams from a vertically extensive freshwater-lens system (Meyer, 2000). Water 

is diverted from many streams on windward Haleakalā and is 

mainly used to irrigate sugarcane in the isthmus. Stream reaches 

on leeward Haleakalā tend to be ephemeral.

Soils

Factors influencing soil conditions in the Hawaiian Islands 

include parent material, duration of weathering, climate, topography, and drainage conditions (Macdonald and others, 1983). 

A soil’s ability to absorb and store water affects direct runoff, 

evapotranspiration, and recharge. Properties of a soil that control its ability to absorb and store water include (1) soil texture, 

the relative percentages of sand-, silt-, and clay-sized particles, 

(2) porosity, a measure of how much water a soil can hold, and 

(3) permeability, a measure of a soil’s ability to transmit water. 

Slope, vegetation, and soil-moisture content can also affect a 

soil’s ability to absorb water.

Soils on Maui were mapped and described by the Natural 

Resources Conservation Service (2006a). Soil properties were 

estimated to depths of 60 inches for most soils on Maui. Estimates of available water capacity, the quantity of water that a 

soil is capable of storing for use by plants, range between 0 and 

0.40 inch of water per inch of soil. Areas containing soil horizons with zero available water capacity in the top 40 inches of 

soil include (1) steep, narrow ravines, (2) steep uplands on West 

Maui Mountain, and (3) leeward parts of Haleakalā and West 

Maui Mountain below 2,000 ft altitude. In general, soil horizons 

with zero available water capacity are associated with bedrock. 

Steep slopes may have thin soils overlying bedrock owing to 

erosion removing soil as fast as the soil forms (Macdonald and 

others, 1983).

Land Cover

Current patterns of vegetation and land cover on Maui 

reflect the influence of steep climate gradients, agricultural 

practices, and land development (fig. 4). The dry summit of 

Haleakalā is sparsely vegetated with large areas of barren rock 

(U.S. Geological Survey, 2010). At middle and lower altitudes 

on Haleakalā, forests cover much of the wet windward slopes; 

alien forests, grasslands, and developed areas cover much of the 

arid leeward slopes. On West Maui Mountain, wet uplands are 

predominantly forests and shrublands, whereas dry lowlands 

are chiefly grasslands and developed areas. From the late 1800s 

to present, sugarcane fields have covered much of the isthmus 

(Dorrance and Morgan, 2000). During most of the 20th century, 

sugarcane and pineapple fields covered large parts of the leeward slopes of West Maui Mountain (for example, Engott and 

Vana, 2007, fig. 2). As a result of the cessation of these agricultural operations on West Maui Mountain, many of these fields 

are now grassland and shrubland. Coastal areas are developed 

in the vicinity of Lahaina, Kahului, Kīhei, and Wailuku (fig. 2). 

Mid-altitude areas on northwest Haleakalā are developed and 

have patches of pineapple and diversified agriculture. 

About 38 percent of Maui is covered by forests (table 

2). Alien forests and tree plantations include various introduced species, whereas native forests include species such 

as Metrosideros polymorpha (‘ōhi‘a) and Acacia koa (U.S. 

Geological Survey, 2010). Alien forests are typically at lower 

altitudes than native forests. 

Water-Budget Model

Groundwater recharge replenishes aquifers and is 

fed mainly by precipitation and irrigation that infiltrates 

the ground surface and percolates beyond the root zone in 

the soil. For this study, we estimated spatially distributed 

mean annual groundwater recharge on Maui by using a 
8 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Figure 4. Land cover for Maui, Hawai‘i (modified from U.S. Geological Survey, 2010; Johnson, 2014). Reservoirs, taro, and developed land covers are difficult to see at this scale. 
Water-Budget Model 9

Aquifer system Agriculture– 

irrigated

Developed 

and 

golf course

Grassland 

and fallow/ 

grassland

Shrubland

Alien forest 

and tree 

plantation

Native 

forest

Sparsely 

vegetated Other

Waikapū 0.10 0.08 0.43 0.12 0.13 0.14 0.00 0.00

‘Īao 0.00 0.23 0.10 0.14 0.22 0.22 0.00 0.09

Waihe‘e 0.00 0.08 0.22 0.16 0.17 0.36 0.01 0.00

Kahakuloa 0.00 0.02 0.13 0.06 0.25 0.53 0.00 0.01

Honokōhau 0.00 0.06 0.17 0.06 0.14 0.57 0.00 0.00

Honolua 0.00 0.23 0.19 0.03 0.17 0.38 0.00 0.00

Honokōwai 0.03 0.22 0.27 0.10 0.14 0.23 0.00 0.01

Launiupoko 0.00 0.15 0.18 0.27 0.10 0.29 0.01 0.00

Olowalu 0.00 0.06 0.37 0.24 0.15 0.17 0.01 0.00

Ukumehame 0.00 0.03 0.42 0.36 0.05 0.13 0.01 0.00

Kahului 0.55 0.31 0.02 0.02 0.07 0.01 0.00 0.02

Pā‘ia 0.78 0.10 0.05 0.02 0.04 0.00 0.00 0.01

Makawao 0.07 0.24 0.38 0.12 0.13 0.02 0.04 0.00

Kama‘ole 0.00 0.15 0.33 0.08 0.37 0.02 0.05 0.00

Ha‘ikū 0.02 0.22 0.28 0.01 0.31 0.16 0.00 0.00

Honopou 0.00 0.09 0.14 0.01 0.43 0.32 0.01 0.00

Waikamoi 0.00 0.02 0.04 0.09 0.26 0.59 0.00 0.00

Ke‘anae 0.00 0.01 0.05 0.11 0.15 0.53 0.15 0.00

Kūhiwa 0.00 0.02 0.07 0.03 0.35 0.52 0.01 0.00

Kawaipapa 0.00 0.05 0.29 0.03 0.25 0.33 0.05 0.00

Waiho‘i 0.00 0.02 0.32 0.05 0.17 0.44 0.00 0.00

Kīpahulu 0.01 0.02 0.18 0.10 0.21 0.47 0.01 0.00

Kaupō 0.00 0.01 0.40 0.19 0.02 0.02 0.36 0.00

Nakula 0.00 0.01 0.72 0.10 0.04 0.00 0.13 0.00

Luala‘ilua 0.00 0.02 0.49 0.08 0.12 0.05 0.24 0.00

Island of Maui 

(all aquifer 

systems)

0.09 0.11 0.26 0.09 0.18 0.20 0.06 0.01

Table 2. General types of land cover, as a fraction of aquifer system area, Maui, Hawai‘i.

[See fig.1 for location of aquifer systems. See fig. 4 for a map of land cover. Agriculture–irrigated consists of coffee, diversified agriculture, pineapple, sugarcane, and taro land covers; Other consists of macadamia, reservoir, water body, and wetland land covers]

water-budget model. The water-budget model is designed 

to simulate–on a daily basis–the hydrological processes 

and physical conditions that affect recharge on Maui. 

Hydrological processes simulated by the model include 

rainfall, fog interception, irrigation, runoff, and ET. The 

model represents physical conditions by using parameters 

that include the moisture-storage capacity of soils and 

properties of the vegetation and land cover that affect ET. 

The water-budget model used for this study (as 

described herein) is a modified version of the models used 

for previous U.S. Geological Survey (USGS) recharge studies for west and central Maui (Engott and Vana, 2007), and 

leeward west Maui (Gingerich and Engott, 2012). The structure of the model used for this study is similar to that of the 

earlier models; however the model used for this study differs 

from previous versions by covering the entire Island of Maui 

and uses more recent maps of rainfall, land cover, and reference grass ET. Additionally, the model used for this study (1) 

includes a more robust method for using streamflow records 

to estimate the spatial distribution of direct runoff, and (2) 

quantifies precipitation that is intercepted by and evaporates 

from forest canopies.

The water-budget model used for this study is similar to 

other models that simulate a root-zone water balance and can be 

used to estimate recharge (for example, Leavesley and others, 

1983; Hevesi and others, 2002; Westenbroek and others, 2010). 

However, for this study, the preferred approach was to build on 

previous water-budget models developed by the USGS for the 

Hawaiian Islands because (1) these models are better adapted 

to conditions unique to Maui, such as a persistent fog and cloud 

cover for many locations and a pronounced orographic influence 

on climate, and (2) a high degree of spatial detail is needed 
10 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

for defining the model subareas. The high degree of spatial 

detail allows the model to represent the wide range of climate 

conditions, vegetation, soils, and land uses on Maui.

Conceptual Model 

The water-budget model used here to estimate groundwater 

recharge is a “threshold-type” or “reservoir” model utilizing a 

variation of the Thornthwaite and Mather (1955) mass-balance 

procedure. The two generalized flow diagrams of the waterbudget model—one for nonforest land covers and one for 

forest land covers are displayed in figure 5. The plant-root zone 

reservoir is included in the model for nonforest and forest land 

covers. The forest-canopy reservoir is included in the model for 

forest land covers only. 

The volume of the plant-root zone reservoir is based on the 

estimated root depth of different plant types and the available 

water capacity of different soil types. The model uses a daily 

computational interval to account for water entering, leaving, 

and being stored within the plant-root zone reservoir. At the end 

of a day, if the volume of water entering the system exceeds 

the storage capacity of the plant-root zone reservoir, given the 

antecedent water content and water losses from ET, the reservoir 

overflows. This overflow is counted as groundwater recharge 

by the model. In some areas, recharge includes direct recharge 

from reservoir and cesspool seepage. All water infiltrating the 

substrate beneath the plant-root zone reservoir as overflow or 

direct recharge is considered recharge. 

Direct runoff is the fraction of precipitation (rainfall and 

fog interception) that does not contribute to net moisture gain 

within the plant-root zone reservoir. Direct runoff excludes base 

flow, which is groundwater discharge to streams. Direct runoff 

is assumed either to be diverted to other areas or ultimately 

discharge to the ocean. Re-infiltration of direct runoff is not 

quantified in the model, although it is indirectly accounted for in 

the empirical functions used to compute direct runoff. 

The forest-canopy reservoir is not treated as a true reservoir in the model calculations because precipitation is not 

allowed to be stored in it for more than a day. For each daily 

computational period, precipitation in the forest-canopy reservoir either evaporates as canopy evaporation or reaches the soil 

as part of net precipitation. Net precipitation is computed as the 

sum of fog interception and rainfall minus canopy evaporation, 

and thus represents outflow from the forest-canopy reservoir. 

Net precipitation is partitioned into direct runoff and inflow to 

the plant-root zone. The plant-root zone reservoir and the forestcanopy reservoir are herein referred to as the plant-root zone 

and the forest canopy, respectively.

Model Calculations 

The water-budget model computes groundwater 

recharge for the Island of Maui using input data that quantify 

the spatial and temporal distribution of rainfall, fog interception, irrigation, ET, direct runoff, soil type, land cover, and 

seepage from reservoirs and septic systems. For the model 

calculations, the Island of Maui is subdivided into small 

areas with homogeneous properties, termed subareas. A map 

of subareas is generated using Esri ArcGIS software (www.

esri.com) by intersecting (merging) spatial datasets that 

characterize the spatial distribution of rainfall, fog, irrigation, 

reference ET, direct runoff, soil type, and land cover. Intersecting the spatial datasets resulted in 318,429 subareas–with 

an average area of about 1.5 acres–for the Maui water-budget 

model.

The water-budget model treats each subarea independently. 

Water transfers between subareas are not included in the model 

calculations. The model domain of each subarea extends vertically from the vegetation canopy to the base of the plant-root 

zone, the part of the soil and bedrock containing roots. Properties of the substrate beneath the plant-root zone are not included 

in the model calculations.

For each subarea, the water-budget model calculates 

recharge on a daily basis for the period of the scenario. In this 

study, we used two scenarios: one for average climate conditions and one for drought conditions. Mean annual recharge 

for each scenario is determined for each subarea. Mean annual 

recharge for subareas is also summed over larger areas of interest, including Maui’s 25 aquifer systems.

For each subarea, the model calculates an interim moisture 

storage value at the start of each day. Interim moisture storage 

is the amount of water that enters the plant-root zone for the 

current day plus the amount of water already in the plant-root 

zone from the previous day. For the first day of the simulation, 

a value for the amount of water already in the plant-root zone 

from the previous day (initial soil moisture) is selected. For 

subareas with nonforest land covers, interim moisture storage, 

Xi, is given by the equation 

 Xi = Ri + Fi –Ui + Ii + Li + Wi + Mi-1, (1a)

where

Xi = interim moisture storage for current day 

[L],

Ri = rainfall for current day [L], 

Fi = fog interception for current day [L], 

Ui = direct runoff for current day [L],

Ii = irrigation for current day [L], 

Li = septic-system leachate for current day [L],

Wi = excess water from the impervious fraction 

of the subarea distributed over the 

pervious fraction of the subarea 

[L],

Mi-1 = moisture storage at the end of the previous 

day (i-1) [L], and

i = subscript designating current day.

For subareas with forest land covers, interim moisture 

storage is given by the equation 
Water-Budget Model 11

Figure 5. Generalized water-budget flow diagrams for both forest and nonforest land covers.

PLANT-ROOT ZONE RESERVOIR

FOREST-CANOPY RESERVOIR

FOR FOREST LAND COVERS:

(modified from McJannet and others, 2007)

number indicates relative timing of process represented in the water budget

Canopy 

Evaporation

Groundwater 

Recharge

Direct

Runoff

Total

Evapotranspiration

Transpiration Ground 

Evaporation

Net

Precipitation

Rainfall

Fog 

Interception

Septic-System 

Leachate

Direct

Recharge

2

1 3

3

4

4

2

1

5

1

6

5

5

PLANT-ROOT ZONE RESERVOIR

FOR NONFOREST LAND COVERS:

number indicates relative timing of process represented in the water budget

Total

Evapotranspiration

Irrigation Fog

Interception Rainfall

Groundwater 

Recharge

Direct 

Runoff

Direct

Recharge

Septic-System

Leachate

2

1

1

2

2 3

4

1
12 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

 Xi = (NP)i – Ui + Li + Wi + Mi-1, (1b)

where

(NP)i = net precipitation for current day [L]. 

For subareas with forest land covers, net precipitation is 

computed as precipitation minus canopy evaporation, which is 

the amount of precipitation that is intercepted by and evaporates 

from the leaves, stems, and trunks of a forest. Precipitation is 

the sum of rainfall and fog interception:

 Pi = Ri + Fi (2)

where

Pi = precipitation for current day [L].

Net precipitation is computed as:

 (NP)i = Pi – (CE)i, (3)

where

(CE)i = forest-canopy evaporation for current day 

[L]. 

The water-budget model computes forest-canopy evaporation using an approach that is derived from the rainfall-interception model described by Gash and others (1995), herein referred 

to as the Gash model. Using this approach, canopy evaporation 

for a given day and location depends on precipitation amount, 

forest structure, and mean rates of evaporation and precipitation. 

The Gash model was modified for this study so that (1) precipitation includes rainfall and fog interception, instead of rain 

only, and (2) water cannot be stored on the forest canopy for 

more than a day. The forest structure is characterized in terms of 

canopy cover, canopy capacity, trunk-storage capacity, and the 

proportion of precipitation diverted to stemflow. Canopy cover, 

c, is the fraction of a forested subarea that is covered by leaves, 

stems, and branches of trees. Canopy capacity, S, is the depth 

of water left on the canopy when rainfall and throughfall have 

ceased (Gash and Morton, 1978). Evaporation of water from 

tree trunks is accounted for by the model using the proportion 

of precipitation that is diverted to stemflow, p, and trunk-storage 

capacity, k, which is considered in terms of an equivalent depth 

of precipitation. The last parameter needed for the Gash model 

is the ratio of the mean evaporation rate to the mean precipitation rate during saturated conditions, V.

To compute forest-canopy evaporation, the first step is to 

determine the minimum depth of precipitation necessary to saturate the forest canopy, P’ (Gash and others, 1995). On the basis 

of equation 2 in Gash and others (1995), P’ for subareas with 

forest land covers is computed as 

 P’ = –{S ÷ (c × V)} × ln (1 – V). (4)

where

P’ = precipitation necessary to saturate the 

canopy [L],

S = canopy capacity per unit of ground area [L] 

(a constant),

c = canopy cover per unit of ground area 

[dimensionless], and

V = ratio of mean evaporation rate to mean 

precipitation rate during saturated 

conditions [dimensionless].

On the basis of the revised analytical form of the Gash 

model presented in table 1 of Gash and others (1995), forestcanopy evaporation for a given day, (CE)

i

, was computed for 

three canopy conditions as follows: 

for Pi < P’, 

(CE)i = c × Pi , 

for Pi ≥ P’ and Pi ≤ k ÷ p,

 (CE)i = c × P’+ c × V × (Pi – P’) + p × Pi ,

 

for Pi ≥P’ and Pi > k ÷ p,

 (CE)i = c × P’ + c × V × (Pi – P’) + k, (5)

where

k = trunk-storage capacity [L] (a constant), and

p = proportion of precipitation diverted to 

stemflow [dimensionless].

For each subarea with impervious surfaces, such as paved 

roads and buildings, the interim moisture-storage equations 

include the factor Wi (see equations 1a and 1b), which is a function of the fraction of the subarea that is impervious. For subareas with no impervious surfaces, Wi

 is zero. The fraction of the 

subarea that is impervious, z, is used to separate, from the total 

rain that falls in a subarea, a depth of water that is treated computationally as though it fell on an impervious surface. Based 

on the rainfall-retention capacity of the impervious surface, 

some water is subtracted to account for direct evaporation. The 

remaining water is considered excess water, Wi. For subareas 

without storm-drain systems, Wi is added to the water budget of 

the pervious fraction of the model subarea. In this case, the total 

daily water input for the pervious fraction of a subarea includes 

excess water from the impervious fraction (equations 1a and 

1b). For subareas with storm-drain systems, Wi is assumed to 

be collected by storm-drain systems and is added to runoff or to 

direct recharge, depending on location. 

For subareas with impervious surfaces, excess water, Wi, 

is determined using the following conditions:
Water-Budget Model 13

 X1i = Pi – Ui + Ti-1, (6)

for X1i ≤ N , Wi = 0, and X2i = X1i,

for X1i > N , Wi = (X1i – N) × z ÷ (1 – z), 

 and X2i = N, (7)

where

X1i = first interim moisture storage on the 

surface of impervious area for current day 

[L],

X2i = second interim moisture storage on the 

surface of impervious area for current day 

[L],

Ti–1 = water storage (ponded water) on the 

surface of impervious area at the end of the 

previous day (i –1) [L], 

N = rainfall-retention capacity of the 

impervious surface (maximum amount of 

water storage on the surface of impervious 

area) [L], and 

z = fraction of area that is impervious 

[dimensionless].

The water storage on the surface of the impervious area at 

the end of the current day, Ti, is determined from the equation:

for X2i ≤ Gi , Ti = 0,

for X2i > Gi , Ti = X2i – Gi, and (8)

where

Gi = reference ET for current day [L], and

Ti = water storage (ponded water) on the 

surface of impervious area at the end of 

day [L].

The next step in the water-budget computation is to 

determine the amount of water that will be removed from the 

plant-root zone by ET. Actual ET is a function of potential ET 

and interim moisture, Xi. The plant-root zone loses water to the 

atmosphere at the potential-ET rate if sufficient water is available. At all sites, potential ET, (PE)i

, is computed as the product 

of (1) reference ET, Gi, the potential ET of a grass reference 

surface, and (2) crop coefficient, kc, a factor that depends on 

vegetation and land cover. 

 (PE)i = kc × Gi (9)

where

 

(PE)i = potential-ET rate for the current day [L/T], 

and

kc = crop coefficient of land cover 

[dimensionless].

For moisture storage greater than or equal to a threshold 

value, Ci, the actual-ET rate is assumed to be equal to the 

potential-ET rate. For moisture storage less than Ci, the actualET rate is assumed to occur at a reduced rate that declines 

linearly with soil-moisture content:

for M ≥ Ci, , E = (PE)i , and

for M < Ci and Ci > 0 E = M × (PE)i ÷ Ci , (10)

where

E = instantaneous actual-ET rate [L/T],

M = instantaneous moisture storage [L], and

Ci = threshold moisture storage for the current 

day below which the actual-ET rate is less 

than the potential-ET rate [L].

The threshold moisture storage, Ci, is estimated using 

the model of Allen and others (1998) for soil moisture. In this 

model, a depletion fraction, d, which ranges from 0 to 1, is 

defined as the fraction of maximum moisture storage that can 

be depleted from the plant-root zone before moisture stress 

causes a reduction in the actual-ET rate. Values for d are 

assigned to land-cover classes on the basis of data in Allen and 

others (1998). The threshold moisture, Ci, is estimated from d

by the equation

 Ci = (1 – d) × Mm , (11)

where 

Mm = moisture-storage capacity of the plant-root 

zone [L]. 

d = depletion fraction [dimensionless].

The moisture-storage capacity of the plant-root zone, 

Mm, expressed as a depth of water, is equal to the plant-root 

depth, D, multiplied by the available water capacity of the 

soil, φ. Available water capacity is the difference between the 

volumetric field-capacity moisture content and the volumetric 

wilting-point moisture content: 

 Mm = D × φ , (12)

where

D = plant root depth [L],

φ = available water capacity, θfc – θwp [L3/L3], 

θfc = volumetric field-capacity moisture content 

[L3/L3], and

θwp = volumetric wilting-point moisture content 

[L3/L3].

In the water-budget model, the actual-ET rate from the 

plant-root zone may be (1) equal to the potential-ET rate for 

part of the day and less than the potential-ET rate for the 
14 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

remainder of the day, (2) equal to the potential-ET rate for 

the entire day, or (3) less than the potential-ET rate for the 

entire day. The total ET from the plant-root zone during a day 

is a function of the potential-ET rate, (PE)i, interim moisture 

storage, Xi, and threshold moisture content, Ci. By recognizing 

that E = -dM/dt, the total depth of water removed by ET from 

the plant-root zone during a day, Ei, is determined as follows:

for Xi > Ci and Ci > 0,

 Ei = (PE)it

i + Ci

{1 – exp[–(PE)i(1-t

i

) ÷ Ci]},

for Xi > Ci and Ci = 0,

 

 Ei = (PE)it

i

,

for Xi ≤ Ci and Ci > 0,

 Ei = Xi{1 – exp[–(PE)i ÷ Ci]}, and

for Xi = Ci, and Ci = 0,

 Ei = 0, (13)

where

Ei = evapotranspiration from the plant-root 

zone during the day [L], 

t

i = time during which moisture storage is 

above Ci [T]. It ranges from 0 to 1 day and 

is computed as follows:

for (Xi – Ci) < (PE)i(1 day)

 ti

 = (Xi – Ci) ÷ (PE)i, and

for (Xi – Ci) ≥ (PE)i(1 day),

 t

i

 = 1. (14)

After accounting for runoff (equation 1a or 1b), actual ET 

from the plant-root zone for a given day is subtracted from the 

interim moisture storage, and any moisture remaining above 

the maximum moisture storage is assumed to be recharge. 

The daily rate of direct recharge from anthropogenic sources, 

including seepage from cesspools and reservoirs, is also added 

to daily recharge at this point. Recharge and moisture storage 

at the end of a given day are assigned according to the following conditions:

for Xi – Ei ≤ Mm, Qi = DR, and Mi = Xi – Ei , and

for Xi– Ei > Mm,

 Qi = (Xi

– Ei – Mm) + DR, and Mi = Mm, (15)

where

Qi = groundwater recharge during the day [L], 

and 

Mi = moisture storage at the end of the current 

day (i) [L], and

DR = daily rate of direct recharge [L] (a 

constant).

Model Input

Land Cover

A land-cover map for Maui representative of 2010 

conditions, herein referred to as the 2010 land-cover map, 

was developed by Johnson (2014) for this study. The 2010 

land-cover map identifies 21 types of land cover (fig. 4) and 

was intersected with other spatial datasets when creating 

the map of subareas for the water-budget model. The 2010 

land-cover map was used in the computation of recharge for 

both scenarios of this study: average climate conditions and 

drought conditions. The 2010 land-cover map was created 

by modifying the LANDFIRE Existing Vegetation Type map 

for Maui (U.S. Geological Survey, 2010), herein referred 

to as the Landfire map. Modifications to the Landfire map 

included converting it from a raster dataset to a vector dataset, combining similar land-cover classes, and adding boundaries of golf courses and selected crops. These modifications 

were done using Esri ArcGIS software, as summarized next. 

Additional details are included in the metadata of Johnson 

(2014).

Some land-cover groups of the Landfire map were combined into more general classes for the 2010 land-cover map. 

Landfire groups “Hawaiian Dry Grassland,” “Hawaiian Mesic 

Grassland,” and “Introduced Perennial Grassland and Forbland” 

were combined into “Grassland” for the 2010 land-cover map 

(fig. 4). Landfire groups “Hawaiian Dry Shrubland,” “Hawaiian 

Mesic Shrubland,” and “Introduced Upland Vegetation – Shrub” 

were combined into “Shrubland.” Landfire groups “Hawaiian 

Dry Forest,” “Hawaiian Mesic Forest,” and “Hawaiian Rainforest” were combined into “Native forest.” The Landfire group 

“Introduced Upland Vegetation-Treed” was renamed as “Alien 

forest.” 

Locations of golf courses and selected crops not specified 

in the Landfire map were delineated in the 2010 land-cover map 

to improve estimates of irrigation, ET, and recharge for parcels 

with these types of land cover. We defined the boundaries of 

sugarcane, coffee, pineapple, and taro fields, as well as golf 

courses, in the 2010 land-cover map (fig. 4) by using sources 

other than the Landfire map. We defined boundaries of sugarcane fields by using a 2000–04 land-cover map by Engott and 

Vana (2007) and a plantation-divisions map (Hawaiian Commercial & Sugar Company, 2008). We digitized the boundaries of golf courses and fields of coffee and pineapple by using 

2010–13 satellite imagery in Google Earth (earth.google.com) 

and recent orthoimagery (U.S. Department of Agriculture, 
Model Input 15

2007). Fields of taro were digitized on the basis of those identified in Gingerich and others (2007). 

Parcels classified as agriculture in the Landfire map (other 

than those within fields of sugarcane, pineapple, or coffee) 

were reclassified by the authors in the 2010 land-cover map 

as either macadamia, diversified agriculture, fallow/grassland, 

low-intensity developed, or open-space developed using the 

2000–04 land-cover map of Engott and Vana (2007), recent 

orthoimagery, and 2010–13 satellite imagery. Agriculture 

parcels with groves of trees on the east slope of West Maui 

Mountain were classified as macadamia. All other agriculture 

parcels that appeared in the imagery as being actively cultivated 

were classified as diversified agriculture. Additionally, we 

used recent orthoimagery and satellite imagery to digitize the 

boundaries of diversified-agriculture fields within the Kula 

Agricultural Park (fig. 4) in the 2010 land-cover map. These 

boundaries were digitized because water-supply data for Kula 

Agricultural Park were used to calibrate irrigation rates for 

diversified agriculture. Agriculture parcels that appeared in 

the imagery as uncultivated and mostly covered by grass were 

defined as fallow/grassland. Parcels defined as fallow/grassland 

include former pineapple fields on leeward West Maui Mountain 

that were abandoned when Maui Land & Pineapple Company 

ceased its pineapple operations there at the end of 2009 (Maui 

News, December 24, 2009). The fallow/grassland land-cover 

class likely includes parcels that were used as grazing pastures. 

Parcels defined as Agriculture in the Landfire map and that were 

adjacent to, but not within, the fields of sugarcane, pineapple, 

or coffee were classified in the 2010 land-cover map as either 

grassland, open-space developed, or low-intensity developed on 

the basis of the land cover for nearby parcels.

Impervious Surfaces

Impervious surfaces include paved surfaces and buildings. 

Excess water from the impervious fraction of a subarea, Wi, that 

is distributed to the pervious fraction of the subarea depends on 

the impervious fraction of the subarea, z. The impervious fraction of each subarea was computed from a map of impervious 

surfaces on Maui (National Oceanic and Atmospheric Administration, 2008).

Rainfall

Monthly Rainfall

Gridded maps of rainfall for each of the 360 months 

during 1978–2007 for the Island of Maui were used to define 

the spatial and temporal distribution of rainfall in the waterbudget calculations. These maps are a subset of the 1,056 

maps of rainfall for each month during 1920–2007 for the 

Hawaiian Islands (Frazier, 2012) generated for the Rainfall 

Atlas of Hawai‘i (Giambelluca and others, 2013). The monthly 

rainfall maps for 1978–2007 were used in the water budget 

because they have the same base period as the mean monthly 

and mean annual rainfall datasets of the Rainfall Atlas of 

Hawai‘i. Rainfall maps are formatted 8.1-arcsecond grids, 

where each grid cell is about 14 acres (770 by 820 ft) and has 

an estimated rainfall value for each month during 1978–2007. 

Using Esri ArcGIS software, the monthly rainfall maps were 

converted from a raster grid to polygon format for use in the 

water-budget model. The rainfall polygons were intersected 

with the other spatial datasets when the map of subareas for 

the water-budget model was created.

Daily Rainfall

Estimates of the actual rainfall pattern on Maui for 

each day during 1978–2007 were not available and were not 

developed as part of this study. Although records of daily 

rainfall measurements at gages were available, reconstructing 

the actual daily rainfall pattern was not attempted because 

(1) records for many gages have considerable gaps, (2) the 

spatial interpolation of daily records for gages would have high 

uncertainty, and (3) the monthly rainfall maps of Frazier (2012) 

were considered to be the best dataset available for estimating 

historical rainfall patterns.

The water-budget model synthesized daily rainfall by 

disaggregating the monthly values of the 1978–2007 rainfall 

distribution maps using the method of fragments (for example, 

Oki, 2002). The method of fragments creates a synthetic 

sequence of daily rainfall from monthly rainfall by imposing the 

rainfall pattern from a rain gage with daily data. The synthesized 

daily rainfall data approximate the long-term average character 

of daily rainfall, such as frequency, duration, and intensity, but 

may not reproduce the historical daily rainfall record during 

1978–2007. 

Daily rainfall measurements at 52 rain gages on Maui 

during 1905–2011 were used to disaggregate monthly rainfall 

into daily rainfall for the water-budget model. Rain gages were 

selected on the basis of location and length and completeness 

of daily records. Daily rainfall data for the rain gages’ period of 

record were obtained from the National Climatic Data Center 

(www.ncdc.noaa.gov) and the USGS (http://waterdata.usgs.

gov/hi/nwis/nwis). Thiessen polygons were drawn around each 

of the rain gages, and the daily rainfall pattern within each 

Thiessen polygon was assumed to be the same as the pattern at 

the rain gage within the Thiessen polygon (fig. 3).

For each rain gage, daily rainfall fragments were computed 

by dividing each daily rainfall measurement for a particular 

month by the total rainfall measured at the gage for that month. 

This resulted in a set of fragments for that particular month in 

which the total number of fragments was equal to the number of 

days in the month. Fragment sets were compiled for every gage 

for every month in which complete daily rainfall measurements 

were available. Fragment sets were grouped by month of the 

year and by rain gage. In the water-budget calculation, the 

fragment set to be used for a given gage for a given month was 

selected randomly from among all available sets for that gage 

for that month. Daily rainfall for a given month was synthesized 
16 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

by multiplying total rainfall for that month (from the monthly 

rainfall maps) by each fragment in the set, thereby providing 

daily rainfall, Ri, for equation 1a or 1b.

Owing to insufficient daily records, fragment sets for each 

of the 12 calendar months were not available for rain gages 

with Cooperative Station Network numbers 0790, 6635, 6645, 

and 7066 (fig. 3). These four gages were assigned fragment sets 

from nearby gages with similar amounts of mean rainfall. Gage 

0790 was assigned the gage 1892 fragment set, gage 6635 the 

gage 4887 fragment set, gage 6645 the gage 9315 fragment set, 

and gage 7066 the gage 5404 fragment set.

Fog Interception

In Hawai‘i, fog most frequently occurs where mountain 

slopes are immersed in a persistent layer of clouds. Clouds can 

form when moist air cools and condenses as it is forced upslope 

by trade winds and by thermal circulation systems such as sea 

breezes. As fog flows near the land surface, vegetation may 

intercept some of the fog moisture. Fog moisture that accumulates on vegetation is called “fog interception” or “cloud-water 

interception.” At places where fog is frequent, intercepted fog 

moisture that drips from the vegetation to the ground can be 

a substantial part of the water budget (Ekern, 1964; Juvik and 

Ekern, 1978; Juvik and Nullet, 1995; Heath and Huebert, 1999; 

Scholl and others, 2007; Giambelluca and others, 2011; Takahashi and others, 2011). Areas in Hawai‘i that frequently have 

fog are in the “cloud zone,” which is between altitudes of about 

2,000 and 8,200 ft (fig. 4; DeLay and Giambelluca, 2010). Fog 

can also form in areas above the cloud zone (Juvik and Ekern, 

1978). 

Much effort has been to done to quantify cloud-water 

interception (CWI) for forests in Hawai‘i. According to a summary of fog research in Hawai‘i, CWI estimates range between 

4 and 20 in/yr for most leeward sites within the cloud zone, and 

between 11 and 44 in/yr for most windward sites within the 

cloud zone (DeLay and Giambelluca, 2010). On Maui, Giambelluca and others (2011) and Scholl and others (2007) used 

different approaches to estimate CWI for two forested sites on 

Haleakalā: Auwahi and Waikamoi (fig. 3). Auwahi is near 

4,000 ft altitude on leeward Haleakalā; Waikamoi is near 

6,400 ft altitude on windward Haleakalā. Using a canopy waterbalance approach, Giambelluca and others (2011) determined 

CWI to be about 6.5 in/yr at Auwahi and about 48 in/yr at 

Waikamoi. Using a stable isotopic mixing-model approach, 

Scholl and others (2007) estimated that cloud water was 46 

and 37 percent of total precipitation at Auwahi and Waikamoi, 

respectively. By combining the results of Scholl and others 

(2007) with 1978–2007 mean annual rainfall near Auwahi 

(about 29 in/yr) and Waikamoi (about 82 in/yr), we computed 

CWI to be about 25 in/yr at Auwahi, and 48 in/yr at Waikamoi. 

Fog interception was quantified in the water-budget model 

for four land-cover classes: alien forest, native forest, tree 

plantation, and shrubland. On the basis of previous fog and CWI 

estimates for Hawai‘i, mean annual fog interception for forests 

Altitude range, in

feet above mean sea level Location 

relative to 

cloud zone

Fog-interception rate, in 

inches per year

From To Leeward 

aspect

Windward 

aspect

 0 <2,000 Below 0 0

 2,000 8,200 Within 14 30

 >8,200 <9,000 Above 10 18

 9,000 and higher Above 6 6

Table 3. Mean fog-interception rates used in the water-budget 

calculation for average climate conditions for Maui, Hawai‘i.

[Fog-interception rates in this table were used for forest land covers: alien forest, native forest, and tree plantation. For areas with shrubland land cover, foginterception rates were assumed to be half of the rates in this table. See fig. 4 

for land covers and the cloud zone for Maui. The altitude range of the cloud 

zone is based on areas with frequent cloud contact, as defined by DeLay and 

Giambelluca (2010). See fig. 6 for boundary between windward and leeward 

areas. <, less than; >, greater than]

on Maui was assumed to vary with altitude and aspect for the 

water budget (table 3). For forests below the typical base of the 

cloud zone (2,000 ft altitude), fog interception was assumed to 

be negligible in both leeward and windward areas. For forests 

within the cloud zone (2,000–8,200 ft altitude) mean annual 

fog interception was assumed to be near the midpoints of the 

ranges of the CWI measurements for leeward and windward 

sites in Hawai‘i (table 3). The midpoints of the ranges were 

used because the existing set of fog and CWI measurements in 

Hawai‘i may not be representative of the majority of the cloudforest area across the state (DeLay and Giambelluca, 2010). 

For example, many of the CWI estimates in Hawai‘i are from 

fog-gage measurements in large clearings or along ridges, or are 

based on canopy water-balance measurements at forest edges. 

At such exposed areas and forest edges, wind velocity and 

fog density is much higher than within the canopy of a closed 

canopy forest (DeLay and Giambelluca, 2010). Hence the 

cloud-water input at these measurement sites, including Auwahi 

and Waikamoi, may be greater than cloud-water input for large 

areas of continuous forests (Giambelluca and others, 2011). 

Mean annual fog interception was assumed to decrease 

uniformly with altitude from the values for the cloud zone to 

6 in/yr near the Haleakalā summit (table 3). The assumption 

of decreasing fog interception with altitude above the cloud 

zone is based on the fog-altitude relation shown in figure 

35.3 of and DeLay and Giambelluca (2010). A fog-interception rate of 6 in/yr is based on three fog-interception studies 

conducted at about 11,200 ft altitude on northern Mauna 

Loa, Island of Hawai‘i (Juvik and Perreira, 1974; Juvik and 

Ekern, 1978) and summarized in DeLay and Giambelluca 

(2010, table 35.1). Much of the area above the cloud zone on 

Maui is not forested but is sparsely vegetated or shrubland 

(fig. 4). 

Mean annual fog-interception rates for shrubland subareas were assumed to be half of the rates for forest subareas 
Model Input 17

(table 3). This assumption was also used in recent water 

budgets for Hawai‘i (for example, Engott and Vana, 2007; 

Engott, 2011), and is based on the premise that shrubs are 

usually shorter than trees and consequently have a smaller 

silhouette and less potential to intercept fog water. Fog interception was assumed to be negligible at all altitudes for landcover classes other than forests and shrubland. Coffee and 

macadamia land covers, which have the potential to intercept 

fog moisture, were assumed to have no fog interception for 

this study because they are at altitudes below the cloud zone 

(fig. 4).

Owing to the sparseness of monthly fog data for Maui, 

mean annual fog interception for a given subarea was apportioned equally to each month of the year in the water-budget 

calculations. Fog interception was assumed to occur only on 

days with rainfall. Daily fog interception was computed in the 

water budget as a fraction of daily rainfall equivalent to the ratio 

of mean monthly fog interception and mean monthly rainfall 

during 1978–2007. These resulting fog-to-rainfall ratios were 

used to compute recharge for average climate conditions and for 

drought conditions. 

Estimating fog interception in the water budget requires 

many assumptions because the magnitude, temporal variability, 

and spatial variability of fog interception on Maui are not well 

known. A better understanding of these characteristics would 

improve fog-interception estimates of the water budget. The 

fog-to-rainfall ratios used to compute fog interception for this 

study, however, result in fog-interception values that are within 

the range of most CWI and fog-interception measurements in 

Hawai‘i. Because of the uncertainty in fog-interception rates 

on Maui, alternative fog-interception rates are evaluated in the 

Sensitivity Analysis section of this report.

Irrigation

Irrigation was applied to golf courses and five agricultural 

land covers: coffee, diversified agriculture, pineapple, sugarcane, and taro (fig. 4). Irrigation was also applied to mediumand high-intensity developed subareas in order to simulate the 

watering of urban landscapes. Of these irrigated land-cover 

classes, sugarcane is the most expansive, totaling slightly less 

than 9 percent of the study area. The other irrigated land-cover 

classes total about 3 percent of the study area. 

Irrigation Estimates

We estimated irrigation rates using a demand-based 

approach, similar to the approach used to estimate irrigation 

for the Hawaii Agricultural Water Use and Development 

Plan (Department of Natural Resources and Environmental 

Management, 2008). The estimated irrigation rate is intended 

to replenish the water content in the root zone to reach field 

capacity. Irrigation demand for a given subarea is estimated on the basis of monthly rainfall, runoff, and potential 

evapotranspiration: 

for (PE)m + Um > Rm, 

Im = {(PE)m + Um – Rm} ÷ g

for (PE)m + Um ≤ Rm,

Im = 0 (16)

where

(PE)m = potential evapotranspiration for month m

[L] (varies by location),

Um = amount of runoff for month m [L], 

 Rm = amount of rainfall for month m [L],

 Im = amount of irrigation for month m [L], and

 g = irrigation efficiency [dimensionless] (varies 

by irrigation method).

Irrigation rates also depend on the efficiency, g, of the 

irrigation method. Irrigation efficiency is the ratio of volume of 

water consumed by vegetation to the volume of water applied 

for irrigation. Hawaiian Commercial & Sugar Company 

(HC&S) uses drip irrigation for its sugarcane fields. On the 

basis of information provided by HC&S, the water budget of 

Engott and Vana (2007) computed drip irrigation for HC&S’ 

sugarcane fields using an irrigation efficiency of 0.80. An 

irrigation efficiency of 0.80 was also used to compute sugarcane irrigation in the water budget for this study. The irrigation 

efficiencies used in the water budget for all remaining irrigation 

methods were obtained from table 3.1 of Department of Natural 

Resources and Environmental Management (2008). Diversified 

agriculture and pineapple were assumed to use drip irrigation, 

which has an irrigation efficiency of 0.85. Coffee was assumed 

to use micro-spray irrigation, which has an irrigation efficiency 

of 0.80. Golf course, and medium- and high-intensity developed 

land covers were assumed to use sprinkler irrigation, which has 

an irrigation efficiency of 0.70. 

For all irrigated land covers other than sugarcane, pineapple, and taro, monthly irrigation estimated from equation 

16 was allocated in equal amounts for each day of a given 

month. To simulate irrigation practices for sugarcane on Maui, 

as described in detail by Engott and Vana (2007) and briefly 

summarized here, irrigation rates for sugarcane were varied 

throughout the cultivation cycle. Sugarcane was assumed to 

have a 24-month cultivation cycle. For a given sugarcane field, 

irrigation water was applied during for the first 20 months of 

the cycle. Monthly irrigation during the first 20 months of the 

cycle was computed according to equation 16 and was equally 

apportioned to the field on days 1, 2, 3, 8, 9, 10, 15, 16, 17, 22, 

23, 24, 28 of each month. No irrigation was applied to the field 

during the last 4 months of the 24-month cycle. The sugarcane 

was assumed to be harvested at the end of month 22 of the 

24-month cycle. The field was assumed to be fallow during 

the last 2 months of the cycle. HC&S staggers its cultivation 

cycles of sugarcane fields such that about half the fields will 
18 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

be harvested in any one year. In the water-budget calculation, 

sugarcane fields were randomly divided into two groups such 

that half of each plantation began active cultivation at the start 

of the simulation and the other half after 12 months into the 

cultivation cycle. 

Similar to the approach taken in Engott and Vana (2007) 

for pineapple irrigation, the monthly irrigation volume 

calculated for pineapple using equation 16 was uniformly 

distributed on days 1, 2, 3, 8, 9, 10, 15, 16, 17, 22, 23, 24, 28 

of each month. Simulation of the pineapple cultivation cycle 

was not attempted in this study and irrigation was assumed 

for all months of the simulation period.

Owing to the nature of taro cultivation, the irrigation 

rate for taro was not computed using the demand-based 

approach (equation 16). Instead, the irrigation rate for taro 

was assumed to be 455 in/yr. A constant irrigation rate was 

used because wetland taro, which is grown in flood ponds, 

was assumed to have a constant recharge rate of 455 in/yr. 

A rate of 455 in/yr is the mean of the recharge rates derived 

from four water-use studies for various taro fields in Hawai‘i 

(Miles, 1931; Watson, 1964; La Pena and Melchor, 1984; 

Berg and others, 1997).

Irrigation Calibrations

Irrigation rates may be limited by the supply or availability 

of irrigation water. Irrigation rates in the water-budget model 

for diversified agriculture, and medium- and high-intensity 

developed land covers were calibrated on the basis of historical water-availability data. The calibrated irrigation rates for 

these land covers do not account for spatial variations in water 

availability or irrigation practices. Estimated irrigation rates 

for sugarcane were deemed to be consistent with average water 

availability. Irrigation rates for pineapple, coffee, taro, and tree 

plantations could not be calibrated because complete records of 

historical water availability were not readily available for areas 

with these land covers.

Irrigation rates for diversified agriculture were calibrated 

on the basis of water supplied to Kula Agricultural Park, on 

northwest Haleakalā (fig. 4). Mean annual water supplied to 

Kula Agricultural Park was about 0.56 Mgal/d for 2004–07 

according to data provided by the County of Maui Department of Water Supply. If water losses are assumed to be 10 

percent of the water supply, then about 0.50 Mgal/d is available 

for irrigation if other water uses are ignored. On the basis of 

rainfall during 2004–07 and an irrigation efficiency of 0.85, the 

demand-based water-budget irrigation rates for Kula Agriculture Park need to be multiplied by a calibration factor of 0.41 in 

order to be consistent with water availability. Irrigation practices 

for Kula Agricultural Park were assumed to be representative of 

irrigation practices for other areas with diversified agriculture 

on Maui. Accordingly, irrigation rates computed using equation 16 were multiplied by 0.41 for all subareas with diversified 

agriculture on Maui. 

Irrigation rates for medium- and high-intensity developed 

land covers computed using equation 16 were multiplied by 

0.37. This calibration factor, 0.37, was derived for the Island 

of O‘ahu by the authors using historical water availability for 

irrigation of abundant urbanized areas on the southern part of 

O‘ahu. Better information regarding actual irrigation rates in 

urbanized areas on Maui would improve estimates of urban 

irrigation in the water-budget model. 

Irrigation rates for sugarcane estimated in the waterbudget model for average climate conditions were not adjusted 

because they were deemed consistent with average water 

available for irrigation. HC&S reported that average water 

availability was 7,689 gallons per acre per day [(gal/acre)/d] 

for the majority of its sugarcane fields during 1984–2007 

(State of Hawai‘i, 2010, p. G-3). After accounting for the 

HC&S estimated system losses and industrial uses (State 

of Hawai‘i, 2010, Exhibit G-1), average water available for 

irrigating these sugarcane fields was about 6,720 (gal/acre)/d. 

Average irrigation estimates of the water-budget model for 

these sugarcane fields was about 6,139 (gal/acre)/d when we 

used rainfall for average climate conditions (1978–2007), and 

was about 6,163 (gal/acre)/d when we used rainfall for the 

HC&S long-term period (1984–2007); these irrigation estimates are less than average water availability. 

Septic-System Leaching

Some buildings and premises on Maui use on-site systems to dispose of wastewater. Whittier and El-Kadi (2013) 

compiled an inventory of on-site wastewater-disposal systems 

on Maui. For each tax map key (TMK) parcel, Whittier and 

El-Kadi (2013) specify the number and type (class) of on-site 

disposal systems and the total estimated wastewater effluent 

flux. For the water-budget calculation, all effluent flux for 

each TMK parcel was applied daily as a uniform depth over 

the parcel’s area. Each TMK parcel may contain one or more 

subareas. Hence all subareas, including those that do not have 

developed land cover, within a TMK parcel that contains an 

on-site disposal system can have septic effluent. For TMK 

parcels with cesspools, the effluent flux was considered direct 

recharge (fig. 5). For TMK parcels with other types of septicsystems, the effluent flux was added to the plant-root zone as 

septic-system leachate (fig. 5). 

 Storm-Drain Systems

Some developed areas on Maui have storm-drain systems 

that collect and divert of rainwater that runs off the surface. 

Water collected by storm-drain systems may be diverted into 

streams, gulches, the ocean, infiltration basins, and drywells. 

Hence, storm-drain systems can affect the water budget. The 

County of Maui Department of Public Works (Cary Yamashita, 

oral commun., 2013) provided information to help define the 

areas on Maui that have storm-drain systems and where these 

systems dispose of water.

For this study, a general approach was used to account for 

water collected and disposed by storm-drain systems. In the 
Model Input 19

water-budget calculation, subareas with medium- and highintensity developed land covers were assumed to have stormdrain systems (fig. 4). For these subareas, excess water, Wi

, 

that flows off of impervious surfaces was assumed to be collected by storm-drain systems instead of flowing to adjacent 

pervious surfaces (see equation 7). Excess water collected by 

storm-drain systems was counted in the water budget as either 

direct runoff or direct recharge, depending on location. For 

areas near Waiehu, Wailuku, Kahului, Pā‘ia, and Kīhei, excess 

water collected by storm-drain systems was counted as runoff 

because it was assumed to be diverted into streams, gulches, 

and the ocean (fig. 2). For areas near Lahaina, Kā‘anapali, 

and Nāpili, excess water collected by storm-drain systems 

was halved into runoff and recharge because it was assumed 

to be diverted into streams and gulches as well as infiltration 

basins and dry wells. All other subareas with storm drains 

were assumed to have systems that divert excess water into 

infiltration basins or drywells; excess water collected by these 

systems was counted as recharge. Exceptions to the general 

approach likely exist, but would require additional investigation to identify and this was beyond the scope of the study. 

The effects on recharge owing to counting all excess water 

from medium- and high-intensity developed areas as either 

runoff or recharge are evaluated in the Sensitivity Analysis 

section of this report.

Direct Runoff

Direct runoff is the fraction of rainfall that does not 

contribute to net moisture gain within the plant-root zone 

(fig. 5). Direct runoff of rainfall consists of overland flow and 

subsurface storm flow that rapidly returns infiltrated water to 

the stream (Oki, 2003). In the water-budget calculation, direct 

runoff was estimated as a fraction of rainfall using runoffto-rainfall ratios. This approach was used also in previous 

water-budget studies for Hawai‘i and other Pacific islands 

(for example, Izuka and others, 2005; Engott and Vana, 2007; 

Engott, 2011; Gingerich and Engott, 2012; Johnson, 2012) and 

was shown to provide reasonable estimates of regional average 

direct runoff using a minimal level of complexity.

The spatial variability of runoff-to-rainfall ratios 

depends on numerous factors including geology, climate, soil 

type, topography, and land use. Runoff-to-rainfall ratios are 

expected to be highest where the rainfall amount and intensity 

are high, permeability of the soils and substrate is low, slopes 

are steep, and soil moisture is high (Oki, 2003). The temporal 

variability in runoff-to-rainfall ratios reflects event characteristics, such as antecedent soil moisture and rainfall intensity. In 

Hawai‘i, runoff-to-rainfall ratios generally follow a seasonal 

pattern. Runoff-to-rainfall ratios are highest during the wetseason months and lowest during the dry-season months. 

In the water-budget model, daily direct runoff, Ui, 

was computed by multiplying daily rainfall, Ri, with 

seasonal (wet and dry season) runoff-to-rainfall ratios 

assigned to drainage basins termed catchment zones. The 

runoff-to-rainfall ratio used to compute direct runoff was 

either (1) the observed runoff-to-rainfall ratio if available for 

the time and location of interest, or (2) the mean seasonal 

runoff-to-rainfall ratio, determined from data or regression equations, if the observed ratio was not available. For 

the analysis, May through October was considered the dry 

season; November through April was considered the wet 

season. Catchment zones were delineated by Rea and Skinner (2012) for a stream network developed using a 10-m 

digital elevation model and a flow-accumulation threshold of 

20,000 cells. For our study, catchment zones were grouped 

into windward and leeward regions (fig. 6). The boundaries 

between windward and leeward regions on Maui’s two volcanoes are based on topographic divides. The 20-inch mean 

annual isohyet was used as the boundary between the windward region on West Maui Mountain and the leeward region 

on the isthmus. In general, the boundaries between windward 

and leeward regions are consistent with those determined in 

previous studies (Yamanaga, 1972, Oki and others, 2010). 

Seasonal runoff-to-rainfall ratios assigned to catchment 

zones in the water-budget model were computed from data 

for drainage basins of stream-gaging stations. Catchment 

zones in ungaged areas were assigned mean seasonal runoffto-rainfall ratios derived from regional-regression models. 

For each gaged drainage basin within a single catchment 

zone, observed seasonal and mean seasonal runoff-to-rainfall 

ratios for the gaged basin were assigned to the catchment 

zone. For each gaged drainage basin containing multiple 

catchment zones, observed seasonal and mean seasonal 

runoff-to-rainfall ratios for the gaged drainage basin were 

spatially disaggregated to each catchment zone within the 

gaged basin. 

Computation of Seasonal Runoff-to-Rainfall Ratios

Runoff and rainfall data for drainage basins of 56 streamgaging stations (25 on Maui, 13 on Kaua‘i and 18 on O‘ahu) 

were used to derive seasonal runoff-to-rainfall ratios for the 

water-budget model (table 4). Data for stations on Kaua‘i and 

O‘ahu were included in the analysis to derive broadly applicable 

empirical models for estimating direct runoff in ungaged areas. 

Stream-gaging stations selected for the runoff analysis had (1) 

at least eight complete years of daily mean discharge records 

between 1920 and 2007, (2) a drainage-basin area greater than 

0.2 mi2, and (3) unregulated streamflow or regulated streamflow with complete and reliable records of daily mean diverted 

flow available to reconstruct total streamflow at the gage. For 

drainage basins with more than one stream-gaging station, only 

the station at the lowest altitude was used in the runoff analysis 

because it had the largest drainage basin. Additionally, concurrent streamflow data for two stations within a drainage basin 

was generally absent or insufficient for improving direct runoff 

estimates. The drainage basins of the selected stream-gaging 

stations were delineated using the USGS StreamStats application for Hawai‘i (Rosa and Oki, 2010). Rainfall within each 

drainage basin was computed using gridded maps of monthly 
20 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Figure 6. Mean runoff-to-rainfall ratios for the (a) Dry-season months: May through October and (b) Wet-season months: November through April 

for drainage basins of selected stream-gaging stations and catchment zones on Maui, Hawai‘i. 

0.05

Mean seasonal

runoff-to-rainfall ratio

0.0003

0.15

0.45

0.30

0.60

0.87

EXPLANATION

16636000

Drainage basin of 

stream-gaging station 

USGS stream-gaging 

station and number

Leeward-windward boundary

Catchment zone of 

Rea and Skinner (2012) 

0 3 6 9 KILOMETERS

0 3 6 9 MILES

(a) Dry-season months:

 May through October

(b) Wet-season months:

 November through April

16517000

16518000

16520000

16510000

16508000

16515000

16516000

16566000

16570000

16577000

16586000

16585000

16587000

16557000

16527000

16565000

16552800

16636000

16618000

16620000

16614000

16604500

16660000

16500100

16501200

Leeward Windward

Windward

Leeward

Leeward

Windward

Base modified from Rea and Skinner, 2012.

Universal Transverse Mercator Projection, Zone 4, NAD83 Datum

156°40'

156°30'

156°20'

156°10'

156°00'

21°00'

20°40'

20°50'

156°40'

156°30'

156°20'

156°10'

156°00'

21°00'

20°40'

20°50'
Model Input 21

Table 4. Mean seasonal runoff-to-rainfall ratios for drainage basins of stream-gaging stations on Kaua‘i, O‘ahu and Maui, Hawai‘i.

1Gaging-station 

number

Stream/river/gulch/

valley Island Aspect

Drainagebasin area, 

in square 

miles 

n

Period of record selected 

to compute mean 

runoff-to-rainfall ratios

Mean runoff-to-rainfall ratio Mean annual rainfall 

Start End Number of years Wet season: November through April Dry season: May through October During 1978–2007, in inches

2Difference during 

selected period of 

record, percentage 

of mean during 

1978–2007

16028000 

(16022000, 16014000)

Waimea River Kaua‘i Leeward 43.68 5 1927 1947 21 0.31 0.24 89.0 1.0

16063000 

(16100000, 16062000, 16061000)

North fork Wailua 

River

Kaua‘i Windward 5.34 4 1957 1984 28 0.54 0.39 199.8 4.9

16068000 East bank of north 

fork Wailua River

Kaua‘i Windward 6.24 4 1978 2007 30 0.50 0.27 129.0 0.0

16071500 Left branch 

‘Ōpaeka‘a Stream

Kaua‘i Windward 0.75 4 1978 2007 30 0.22 0.09 100.5 0.0

16089000 

(16088000, 16087000)

Anahola Stream Kaua‘i Windward 5.45 4 1956 1985 30 0.49 0.36 108.5 4.3

16097500 Halaulani Stream Kaua‘i Windward 1.20 4 1978 2007 30 0.62 0.41 115.0 0.016103000 Hanalei River Kaua‘i Windward 18.51 4 1993 2007 15 0.54 0.32 175.4 4.016108000 Wainiha River Kaua‘i Windward 10.44 5 1978 2007 30 0.54 0.39 207.6 0.016114000 Limahuli Stream Kaua‘i Windward 1.37 4 1995 2005 11 0.36 0.21 116.5 3.116115000 Hanakāpī‘ai Stream Kaua‘i Windward 2.75 5 1941 1949 9 0.40 0.27 128.7 4.616116000 Hanakoa Stream Kaua‘i Windward 1.09 5 1939 1947 9 0.51 0.30 105.8 4.516117000 Kalalau Stream Kaua‘i Leeward 1.60 5 1932 1948 17 0.23 0.08 80.5 4.316130000 Nahomalu Valley Kaua‘i Leeward 3.79 E 1964 1971 8 0.04 0.01 36.6 24.616200000 North fork Kaukonahua Stream O‘ahu Leeward 1.38 4 1978 2007 30 0.48 0.49 232.4 0.016201000 Right bank of north 

fork Kaukonahua Stream

O‘ahu Leeward 1.15 4 1940 1951 12 0.33 0.36 213.4 4.6

[See fig. 6 for locations of drainage basins on Maui; n, number of days used for determining minimum flows in the base flow-separation program (Wahl and Wahl, 1995); E, the base flow-separation program (Wahl and Wahl, 1995) was not used to estimate direct runoff because the streamflow measured at station was ephemeral and was assumed to equal direct runoff; mean annual rainfall was estimated from Frazier (2012) and Giambelluca and others (2013)]

1 Data for gaging-station numbers in parentheses were used to reconstruct streamflow at the gaging station whose number is not in parentheses.2 For example, mean annual rainfall on the drainage basin of station 16500100 was 22 percent greater during 1963–1972 than that during 1978–2007.
22 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

1Gaging-station 

number

Stream/river/gulch/

valley Island Aspect

Drainagebasin area, 

in square 

miles 

n

Period of record selected 

to compute mean 

runoff-to-rainfall ratios

Mean runoff-to-rainfall ratio Mean annual rainfall 

Start End Number of years Wet season: November through April Dry season: May through October During 1978–2007, in inches

2Difference during 

selected period of 

record, percentage 

of mean during 

1978–2007

16208000 South fork Kaukonahua Stream O‘ahu Leeward 4.10 5 1978 2007 29 0.32 0.28 168.916211600 Mākaha Stream O‘ahu Leeward 2.12 5 1978 2007 30 0.13 0.04 71.2 0.016212800 Kīpapa Stream O‘ahu Leeward 4.25 4 1978 2004 27 0.19 0.13 178.3 -0.116216000 Waiawa Stream O‘ahu Leeward 25.19 4 1978 2004 27 0.21 0.12 93.7 -0.216226200 North Hālawa 

Stream

O‘ahu Leeward 4.02 5 1983 2007 24 0.19 0.09 111.8 -2.8

16228000 Moanalua Stream O‘ahu Leeward 2.71 4 1949 1978 30 0.15 0.07 117.8 3.616229300 Kalihi Stream O‘ahu Leeward 5.21 4 1978 2003 26 0.21 0.11 97.6 -1.916244000 Pūkele Stream O‘ahu Leeward 1.15 5 1953 1982 30 0.14 0.08 114.6 3.416270500 Kamo‘olali‘i Stream O‘ahu Windward 3.20 3 1967 1976 9 0.18 0.07 82.8 1.916273950 South fork Kapunahala Stream O‘ahu Windward 0.42 3 1988 1997 10 0.13 0.09 78.4 2.916278000 ‘Ioleka‘a Stream O‘ahu Windward 0.32 3 1944 1968 25 0.09 0.07 95.8 0.516283000 Kahalu‘u Stream O‘ahu Windward 0.28 3 1941 1970 30 0.12 0.08 119.2 2.516303000 

(16302000)

Punalu‘u Stream O‘ahu Windward 2.78 4 1978 2006 29 0.21 0.14 186.6 0.2

16304200 Kaluanui Stream O‘ahu Windward 1.09 4 1978 2007 30 0.34 0.25 168.4 0.016325000 Kamananui Stream O‘ahu Leeward 3.15 4 1971 2000 30 0.29 0.15 132.0 0.616345000 ‘Ōpae‘ula Stream O‘ahu Leeward 3.00 4 1978 2007 30 0.37 0.24 164.6 0.016500100 Kepuni Gulch Maui Leeward 1.93

E 1963 1972 10 0.04 0.06 33.1 22.0

16501200 ‘Ohe‘o Gulch Maui Leeward 8.53 4 1990 1997 8 0.38 0.25 249.4 0.616508000 Hanawī Stream Maui Windward 3.29 4 1978 2007 30 0.41 0.23 248.7 0.016510000 Kapā‘ula Gulch Maui Windward 1.35 4 1933 1962 30 0.61 0.38 257.3 1.316515000 Waiohue Gulch Maui Windward 0.74 4 1933 1962 30 0.52 0.32 247.9 1.3

Table 4. Mean seasonal runoff-to-rainfall ratios for drainage basins of stream-gaging stations on Kaua‘i, O‘ahu and Maui, Hawai‘i.—Continued[See fig. 6 for locations of drainage basins on Maui; n, number of days used for determining minimum flows in the base flow-separation program (Wahl and Wahl, 1995); E, the base flow-separation program (Wahl and Wahl, 1995) was not used to estimate direct runoff because the streamflow measured at station was ephemeral and was assumed to equal direct runoff; mean annual rainfall was estimated from Frazier (2012) and Giambelluca and others (2013)]

1 Data for gaging-station numbers in parentheses were used to reconstruct streamflow at the gaging station whose number is not in parentheses.2 For example, mean annual rainfall on the drainage basin of station 16500100 was 22 percent greater during 1963–1972 than that during 1978–2007.
Model Input 23

1Gaging-station 

number

Stream/river/gulch/

valley Island Aspect

Drainagebasin area, 

in square 

miles 

n

Period of record selected 

to compute mean 

runoff-to-rainfall ratios

Mean runoff-to-rainfall ratio Mean annual rainfall 

Start End Number of years Wet season: November through April Dry season: May through October During 1978–2007, in inches

2Difference during 

selected period of 

record, percentage 

of mean during 

1978–2007

16516000 Kopiliula Stream Maui Windward 3.56 4 1938 1957 20 0.43 0.24 197.8 2.816517000 East Wailuaiki 

Stream

Maui Windward 3.14 4 1928 1957 30 0.58 0.35 201.2 4.5

16518000 West Wailuaiki 

Stream

Maui Windward 3.59 4 1978 2007 30 0.64 0.39 183.5 0.0

16520000 East Wailuanui 

Stream

Maui Windward 0.53 4 1928 1957 30 0.75 0.55 228.1 4.4

16527000 Honomanū Stream Maui Windward 2.94 4 1939 1963 25 0.55 0.45 149.8 3.816552800 Waikamoi Strream Maui Windward 2.50 4 1957 1967 11 0.17 0.12 62.9 1.316557000 Alo Stream Maui Windward 0.47 4 1928 1957 30 0.75 0.65 209.3 1.916565000 Ka‘aiea Gulch Maui Windward 0.68 4 1933 1962 30 0.50 0.44 216.5 0.016566000 ‘O‘opuola Stream Maui Windward 0.25 4 1931 1957 27 0.64 0.54 189.4 1.516570000 Na‘ili‘ilihaele 

Stream

Maui Windward 3.69 5 1945 1974 30 0.60 0.50 181.9 1.8

16577000 Kailua Stream Maui Windward 2.41 4 1928 1957 30 0.75 0.53 203.4 1.516585000 Ho‘olawanui Stream Maui Windward 1.34 5 1942 1971 30 0.50 0.32 174.9 0.616586000 Ho‘olawali‘ili‘i 

Stream

Maui Windward 0.62 4 1931 1957 27 0.43 0.33 160.2 4.2

16587000 Honopou Stream Maui Windward 0.59 4 1978 2007 30 0.57 0.38 145.6 0.016604500 ‘Īao Stream Maui Windward 6.13 4 1983 2007 24 0.49 0.40 178.1 -3.816614000 Waihe‘e River Maui Windward 4.27 3 1984 2007 24 0.49 0.45 184.6 -3.016618000 Kahakuloa Stream Maui Windward 3.40 4 1978 2007 30 0.43 0.33 127.0 0.016620000 Honokōhau Stream Maui Windward 4.18 4 1978 2007 28 0.35 0.31 200.3 -2.916636000 Kanahā Stream Maui Leeward 1.57 3 1920 1931 11 0.17 0.16 141.8 3.016660000 Kūlanihāko‘i Gulch Maui Leeward 15.03 E 1963 1970 8 0.01 0.00 22.4 35.6

1 Data for gaging-station numbers in parentheses were used to reconstruct streamflow at the gaging station whose number is not in parentheses.2 For example, mean annual rainfall on the drainage basin of station 16500100 was 22 percent greater during 1963–1972 than that during 1978–2007.

Table 4. Mean seasonal runoff-to-rainfall ratios for drainage basins of stream-gaging stations on Kaua‘i, O‘ahu and Maui, Hawai‘i.—Continued[See fig. 6 for locations of drainage basins on Maui; n, number of days used for determining minimum flows in the base flow-separation program (Wahl and Wahl, 1995); E, the base flow-separation program (Wahl and Wahl, 1995) was not used to estimate direct runoff because the streamflow measured at station was ephemeral and was assumed to equal direct runoff; mean annual rainfall was estimated from Frazier (2012) and Giambelluca and others (2013)]
24 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

rainfall (Frazier, 2012). Because streamflow measured at most 

gaging stations consists of direct runoff and base flow, the baseflow component was estimated and subtracted from the total 

streamflow. Streamflow at stations 16500100 and 16660000 

on Maui was ephemeral and was assumed to have no base 

flow. Base flow at the other gaged basins was estimated using 

a computerized base-flow separation method (Wahl and Wahl, 

1995). This method has been used in numerous other studies 

in Hawai‘i (for example, Izuka and others, 2005, Engott and 

Vana, 2007, Engott, 2011) and provides a reasonable estimate 

of base flow for perennial streams in Hawai‘i. The method 

defines local streamflow minimums within consecutive, nonoverlapping n-day periods and requires two parameters (1) f, the 

turning-point test factor and (2) n, the number of days in a test 

window. In this study, the f value used for all stations was 0.9. 

The n values were determined for each station using the method 

described by Wahl and Wahl (1995) and ranged from 3 to 5 days 

(table 4). Daily base flow was subtracted from daily streamflow 

to determine daily direct runoff.

Observed seasonal and mean seasonal runoff-to-rainfall 

ratios were computed for drainage basins of stream-gaging stations. Observed seasonal runoff-to-rainfall ratios were computed for gaged drainage basins of stream-gaging stations on 

Maui that were operational during 1978–2007 (table 4). Each 

observed seasonal runoff-to-rainfall ratio for the gaged drainage basins was computed as the quotient of cumulative direct 

runoff and cumulative rainfall during the season. For example, 

the observed runoff-to-rainfall ratio for a gaged basin during the 

dry season of 2001 was computed as the quotient of cumulative 

direct runoff and cumulative rainfall during May–October 2001. 

For each of the 56 selected gaged drainage basins, mean seasonal runoff-to-rainfall ratios were computed for a period with 

rainfall and runoff conditions generally representative of those 

during 1978–2007 (table 4). Each mean seasonal runoff-torainfall ratio for the gaged drainage basins was computed as the 

quotient of cumulative direct runoff and cumulative rainfall during the appropriate season of the selected period. For example, 

the mean dry season runoff-to-rainfall ratio for stream-gaging 

station 16500100 was computed as the quotient of cumulative direct runoff and cumulative rainfall during May–October 

between 1963 and 1972. 

Criteria for selecting periods of record were determined 

by examining temporal variations in seasonal runoff-to-rainfall ratios for stream-gaging stations that were operational 

during 1978–2007. For these stations, mean seasonal runoffto-rainfall ratios computed for 1978–2007 were compared 

with those computed for smaller, subset periods during 

1920–2007. Based on these comparisons, the difference 

between mean seasonal runoff-to-rainfall ratios computed for 

1978–2007 and those computed for subset periods decreases 

with increasing record length of the subset period and with 

decreasing differences between mean annual rainfall during 

1978–2007 and that of the subset period. Accordingly, the 

period of record selected to compute mean seasonal runoffto-rainfall ratios was (1) the entire period during 1978–2007 

for stream-gaging stations with at least 24 complete years of 

record during 1978–2007, (2) the longest contiguous period 

during 1920–2007 that had less than 5-percent difference in 

mean annual rainfall, relative to rainfall for 1978–2007, for 

all remaining stream-gaging stations on perennial streams 

with base flow, and (3) the entire period of record for three 

stream-gaging stations on ephemeral streams, including two 

stations on Maui. Despite the relatively high rainfall during their periods of record (table 4), the latter three stations 

were included in the analysis owing to the sparseness of 

runoff data for areas in the Hawaiian Islands with ephemeral 

streams.

Runoff-to-Rainfall Ratios Assigned to Ungaged 

Catchment Zones 

Ungaged catchment zones are outside of the drainage 

basins of the 25 selected stream-gaging stations on Maui 

(fig. 6). Direct runoff for ungaged catchment zones was computed using mean seasonal runoff-to-rainfall ratios derived from 

the four regional-regression equations in table 5. Separate windward and leeward regression equations were derived for the wet 

and dry seasons. The regional-regression equations relate mean 

seasonal runoff-to-rainfall ratios and basin characteristics. 

The regional-regression equations were derived 

from mean seasonal runoff-to-rainfall ratios and basin 

Region Season Regression used to compute 

runoff-to-rainfall ratio

RMSE Percentage bias 

Overall Maui Overall Maui

Leeward Wet 0.00294 [mean wet season rainfall] 0.07 0.05 -2.1 27.6

Leeward Dry 0.00175 [mean annual rainfall] 0.07 0.04 1.9 6.5

Windward Wet [% HSG C]0.177 [% HSG D]0.261 [mean wet season ETo](–0.751) 0.12 0.11 -5.4 -3.9

Windward Dry [% HSG C]0.177 [% HSG D]0.348 [mean dry season ETo](–0.942) 0.10 0.10 -6.8 -8.4

[See fig. 6 for locations of leeward and windward regions on Maui. RMSE, prediction root mean square error; Overall RMSE and percentage bias are for the 

gaged basins on the islands of Kaua‘i, O‘ahu, and Maui used to developed the regional-regression equations; Wet season is November through April; Dry season 

is May through October; mean wet season rainfall and mean annual rainfall are from Giambelluca and others (2013); mean wet and dry season ETo, reference 

evapotranspiration, are from Giambelluca and others (2014); % HSG C is the percentage of a catchment zone’s area that contains hydrologic soil group C; % 

HSG D is the percentage of catchment zone’s area that contains hydrologic soil group D; Areas of hydrologic soil groups were derived from Natural Resources 

Conservation Service (2006a,b,c)] 

Table 5. Regional-regression equations used to compute mean seasonal runoff-to-rainfall ratios for catchment zones in ungaged areas on Maui 

Hawai‘i.
Model Input 25

characteristics for the 56 stream-gaging stations in table 

4. Thirty basin characteristics–including those related to 

climate, soil, and vegetation– were evaluated as possible 

explanatory variables (not shown). The regional regressions in table 5 were selected because they had the lowest 

residual sum of squares and met the following criteria. First, 

all regression coefficients were statistically significant at a 

5-percent significance level. Second, the sign and magnitude 

of the fitted coefficients were physically meaningful. Finally, 

the cross-validated results indicated less than 10 percent 

bias. For leeward catchment zones, wet season runoff-torainfall ratios were estimated on the basis of mean wet 

season rainfall during 1978–2007 (Giambelluca and others, 

2013); dry season runoff-to-rainfall ratios were estimated 

on the basis of mean annual rainfall during 1978–2007. For 

windward catchment zones, wet season runoff-to-rainfall 

ratios were estimated on the basis of mean wet season reference ET (Giambelluca and others, 2014) and the percentage 

of the catchment zone’s area with hydrologic soil groups C 

and D (Natural Resources Conservation Service, 2006a,b,c); 

dry season runoff-to-rainfall ratios were estimated on the 

basis of mean dry season reference ET and the percentage 

of the zone’s area with hydrologic soil groups C and D. The 

performance statistics of the regression equations in table 5 

are used to examine the sensitivity of recharge to runoff-torainfall ratios (see Sensitivity Analysis).

Runoff-to-Rainfall Ratios Assigned to Gaged Basins 

within a Single Catchment Zone

Twelve of the selected stream-gaging stations on Maui 

have drainage basins with a single catchment zone (fig. 6). 

Nine of these stations (16500100, 16520000, 16557000, 

16565000, 16566000, 16570000, 16585000, 16586000, and 

16636000) were not operational during 1978–2007 (table 4). 

For the drainage basins of these nine stations, the model computed direct runoff by using mean seasonal runoff-to-rainfall 

ratios. For the drainage basins of the three remaining stations 

(16587000, 16618000, and 16620000), the model computed 

direct runoff by using (1) observed seasonal runoff-to-rainfall 

ratios, for seasons when observed ratios were available, and 

(2) mean seasonal runoff-to-rainfall ratios for seasons when 

observed ratios were not available.

Runoff-to-Rainfall Ratios Assigned to Gaged Basins 

with Multiple Catchment Zones

Thirteen of the selected stream-gaging stations on Maui 

(16501200, 16508000, 16510000, 16515000, 16516000, 

16517000, 16518000, 16527000, 16552800, 16577000, 

16604500, 16614000, and 16600000) have drainage basins 

containing multiple catchment zones (fig. 6). The model computed direct runoff for each of the catchment zones within these 

gaged basins by using adjusted seasonal runoff-to-rainfall ratios 

determined from equation 17:

 (17)

where:

 = adjusted runoff-to-rainfall ratio for catchment 

zone a during season t

= mean runoff-to-rainfall ratio for catchment zone

a estimated using the appropriate regionalregression equation (table 5)

 

 = number of catchment zones in the gaged basin b

 

 = mean runoff-to-rainfall ratio for catchment zone i 

 estimated using the regional-regression 

 equations

 = rainfall for catchment zone i during season t

 = observed runoff-to-rainfall ratio for the gaged 

basin b during season t. If the observed 

runoff-to-rainfall ratio for gaged basin b

is not available during season t, the mean 

seasonal runoff-to-rainfall ratio is used 

(table 4).

 = rainfall for gaged basin b during season t.

Equation 17 uses the regional-regression models and 

seasonal rainfall to spatially disaggregate observed seasonal and mean seasonal runoff-to-rainfall ratios of a gaged 

basin for each catchment zone within a gaged basin. Hence, 

seasonal runoff-to-rainfall ratios were allowed to be spatially 

variable, instead of spatially uniform, across the catchment 

zones within gaged drainage basins containing multiple 

catchment zones.

Evapotranspiration

Evapotranspiration (ET) is the sum of all water that is 

evaporated or transpired from the forest canopy and plantroot zone. Evapotranspiration can be divided into three main 

evaporative processes (1) canopy evaporation, which is 

evaporation of intercepted rain and fog from the surface of 

vegetation; (2) ground evaporation, which is evaporation of 

water from the soil surface and overlying litter and mulch 

layers; and (3) transpiration, the process by which soil moisture taken up by vegetation is eventually evaporated through 

plant pores (Viessman and Lewis, 2003). Because these three 

processes are difficult to quantify individually, they are typically combined in water budgets. 

Canopy evaporation in forested areas can substantially 

reduce the rainfall that reaches the ground beneath a forest 

rr RR rr rf

RR rf a t, a

b t b t

i i t i

j

, ,

, ∑ 1 × = × ×

=

 



rr

a t,

RR a

j

RRi

rfi t,

rrb t,

rfb t,
26 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

canopy (Gaskill, 2004; DeLay, 2005; McJannet and others, 

2007; Giambelluca and others, 2011; Safeeq and Fares, 2014). 

Owing to the height of trees and their canopy structure, turbulent diffusion is much more efficient at removing intercepted 

water from forest canopies than from shorter vegetation. Moreover, canopy evaporation in forests tends to operate on much 

shorter time scales (hours) than transpiration (weeks or longer) 

(Savenije, 2004). This enhanced rate of evaporation from a wet 

forest canopy makes realistic estimates of ET from forests possible only if transpiration and canopy evaporation are evaluated 

separately (Shuttleworth, 1993). 

For this study, total ET from subareas with forest land covers is computed by separately estimating forest-canopy evaporation and combined ground evaporation and transpiration from 

the plant-root zone (fig. 5). Evaporation from the forest canopy 

and evapotranspiration from plant-root zone are added together 

to yield total ET. For subareas with nonforest land covers, ET 

is computed using a more traditional approach in which canopy 

evaporation, ground evaporation, and transpiration are not separately estimated (fig. 5). The concept of potential ET, combined 

with empirical models when soil moisture is limited, is used 

to estimate ground evaporation and transpiration in forests and 

total ET for all other land covers. 

Forest-Canopy Evaporation and Net Precipitation

As rain falls on a vegetated surface, a fraction of the 

droplets will accumulate on the leaves, trunks, or stems of the 

vegetation. Additional moisture from fog interception may 

supplement the amount of water that accumulates on vegetation. 

Canopy evaporation is the part of precipitation that accumulates on and then evaporates from the vegetation (Gerrits and 

Savenije, 2011). Net precipitation is the part of precipitation that 

reaches the forest floor (fig. 5). 

In this study, the Gash model was used to compute forestcanopy evaporation for the following reasons. First, the Gash 

method accounts for gaps in the forest canopy, and this allows 

for a sparse canopy to be differentiated from a dense canopy. 

Second, canopy evaporation during a period of precipitation is 

dependent on the amount of precipitation during that period. 

Third, the Gash model has the capacity to account for spatial 

differences in climate, including climate differences between 

windward and leeward forests. One disadvantage of the Gash 

model, however, is that it is theoretical. Therefore, one of the 

parameters of the Gash model used to compute canopy evaporation in the water-budget model was calibrated to the wet 

canopy evaporation maps of Giambelluca and others (2014) as 

described below. Use of the Gash model, instead of the mean 

wet-canopy evaporation maps, in the water-budget model 

allows forest-canopy evaporation to be computed on a daily 

basis in response to daily variations in precipitation.

Forest-canopy evaporation is computed in the water 

budget according to equations 4 and 5, which require rainfall 

and the following parameters (1) canopy cover, (2) canopy 

capacity, (3) trunk-storage capacity, (4) proportion of precipitation diverted to stemflow, and (5) the ratio of the mean 

evaporation rate to mean precipitation rate during saturated 

conditions, V. For the water-budget calculations, the values 

assigned to parameters 1–4 were derived from published data 

for areas in Hawai‘i. The values assigned to V were derived 

using maps of mean wet canopy evaporation for the Islands 

of Kaua‘i, O‘ahu, and Maui (Giambelluca and others, 2014). 

An analysis of the sensitivity of the water-budget results to 

selected Gash-model parameters is included in the Sensitivity Analysis section of this report.

Canopy cover of forest land covers varies spatially 

across Maui. The canopy cover of each subarea with forest 

land cover in the water-budget model was estimated from a 

map of mean annual vegetation cover fraction (Giambelluca 

and others, 2014). This map quantifies the vegetation cover 

fraction at a spatial resolution of about 14 acres. A canopy 

cover of 0 implies an absence of canopy cover, whereas a 

value of 1 implies a dense canopy with no gaps. The estimated canopy-cover values for subareas with forest land 

covers in the water budget range from 0.03 to 1. 

Canopy capacity, trunk-storage capacity, and the 

proportion of precipitation diverted to stemflow were 

assumed to be the same for all forests (table 6). Canopy 

capacity was set at 0.05 inches, the mean of the average 

values reported for six forested sites in Hawai‘i (DeLay, 

2005, p. 42; Takahashi and others, 2011, Safeeq and Fares, 

2014). Trunk-storage capacity was set at 0.01 inches, 

the mean of the values reported for four forest sites in 

Hawai‘i (DeLay, 2005, p. 42; Safeeq and Fares, 2014). 

The proportion of precipitation diverted to stemflow was 

assumed to be 0.04, the mean of the values reported for 

eight forest sites in Hawai‘i (Gaskill, 2004; DeLay, 2005, 

p. 42; Takahashi and others, 2011; Safeeq and Fares, 2014). 

Forest sites of Takahashi and others (2011) and Safeeq and 

Fares (2014) with an abundance of Psidium cattleianum 

(strawberry guava) had relatively high stemflow estimates 

ranging from 29 to 37 percent of rainfall. The relatively high 

stemflow values from these sites were excluded from the 

computed stemflow mean of 0.04 used in the water budget. 

The effect on recharge estimates owing to the use of a 

stemflow value of 0.37 for areas mapped as alien forests is 

examined in the Sensitivity Analysis section. 

A map of mean annual wet canopy evaporation for the 

Hawaiian Islands (Giambelluca and others, 2014) was used 

to develop a regression model for estimating the spatial 

variability of V. This regression model was developed 

because estimates of V for sites in Hawai‘i are rare, and 

because canopy-evaporation estimates from the water-budget 

model are sensitive to V. The wet canopy evaporation map 

has mean annual wet canopy evaporation estimates at a 

spatial resolution of about 14 acres. To derive a regression 

model for estimating V, we first selected grid cells from the 

islands of Kaua‘i, O‘ahu, and Maui that consisted of forest 

land-cover classes only, based on the LANDFIRE Existing 

Vegetation Type map (U.S. Geological Survey, 2010). Data 

from Kaua‘i and O‘ahu were included in the analysis to 

supplement the data from Maui and provide the basis for 
Model Input 27

a broadly applicable V value for Hawai‘i. Next, for each 

of the selected grid cells, we determined the “calibrated V

value” needed for mean annual canopy evaporation from 

the Gash model for Kaua‘i, O‘ahu, and Maui to equal wet 

canopy evaporation from Giambelluca and others (2014). 

The water budgets were computed with various V values 

to determine the calibrated V values for the selected grid 

cells. Last, we examined associations between the calibrated 

V values and various other parameters of the selected grid 

cells including estimates of mean annual rainfall, reference 

ET, Penman-Monteith ET from Giambelluca and others 

(2014), and estimates of mean annual wind speed from 

AWS Truewind, LLC (2004). Estimates of V from a linearregression model (equation 18) relating V to the quotient of 

mean annual wind speed and mean annual rainfall resulted 

in the best agreement, in terms of bias and root mean square 

error, between estimates of canopy evaporation from the wet 

canopy evaporation maps (Giambelluca and others, 2014) 

and the estimates from the Gash model for Kaua‘i, O‘ahu, 

and Maui. The spatial distribution of V was estimated as 

follows:

for w < 0.009,

 

 V = 0.01, 

 

for 0.009 ≤ w ≤ 0.192,

 

 V = 2.677 × (w) – 0.014, and 

 

for w > 0.192,

 V = 0.50, (18)

where

w = mean annual wind speed divided by mean 

annual rainfall [(meters/second)/inch)]

The variable w is the quotient of (1) mean annual wind 

speed, in meters per second, which was derived from a map 

of mean annual wind speed at height of about 100 ft above 

the land surface (AWS Truewind, 2004), and (2) 1978–2007 

mean annual rainfall, in inches (Giambelluca and others, 

2013). In general, estimates of V are less for wet areas than 

for dry areas. Grid cells that had calibrated V values that 

were outside the range of 0.01–0.50 were excluded from 

the derivation of equation 18. A range of 0.01–0.50 was 

established for this analysis on the basis of the range of 

published estimates of V. The low end of the range, 0.01, 

was determined by Hutjes and others (1990) for a humid, 

tropical forest site in the African Ivory Coast. The high end 

of the range, 0.50, is the mean value reported by Safeeq and 

Fares (2014) for a forest site on leeward O‘ahu. A similar 

range of mean V estimates (about 0.03–0.40) was determined 

for 54 sites located in various climate zones across Australia 

(Wallace and others, 2013, fig. 6c).

Potential Evapotranspiration

Potential evapotranspiration (ET) is the maximum rate 

that water can be removed from the plant-root zone by ET if 

soil moisture is nonlimiting (Giambelluca, 1983). The actualET rate is a function of potential ET, soil-moisture content, and 

threshold-moisture content (see equation 13). The actual-ET 

rate becomes less than the potential rate with the onset of soilmoisture stress. As the soil dries, capillary and adsorptive forces 

bind the remaining water to the soil matrix, reducing water flow 

to roots. Soil-moisture stress occurs when the decreasing flow 

of water to the root system induces a response in the plant to 

slow down transpiration and prevent desiccation. The thresholdmoisture content at which a plant begins to react to soil drying 

varies with the type of plant. 

Potential ET is controlled by atmospheric conditions, 

topography, and land-cover characteristics (Giambelluca, 1983). 

Maps of mean monthly reference ET produced by Giambelluca and others (2014) were used in the water-budget model 

to estimate the influence of atmospheric conditions (radiation, 

air temperature, air humidity, and wind speed) on potential ET. 

Crop coefficients were used to estimate the integrated effects 

of land-cover and vegetation characteristics on potential ET. 

Potential ET for each subarea was computed in the water-budget 

model as the product of mean monthly reference ET and the 

crop coefficient assigned to the land cover (see equation 9).

Reference Evapotranspiration 

Reference ET is the potential ET of a hypothetical grass 

surface with specific characteristics and optimum soil-water 

conditions for given climatic conditions (Allen and others, 

1998). Reference ET is similar to pan evaporation, which 

has been used in previous water budgets for Maui and other 

Hawaiian Islands. Both pan evaporation and reference ET 

provide an index of the energy that is available for ET for a 

given area. 

Maps of mean monthly reference ET (Giambelluca and 

others, 2014) for Maui were used in the water budget. These 

maps have the same grid resolution (about 14 acres) as the 

monthly rainfall maps produced by Frazier (2012). Mean 

annual reference ET ranges from about 28 to 113 inches on 

Maui (fig. 7). In general, mean annual reference ET is highest in dry lowlands, and is lowest in wet uplands within the 

cloud zone. In the water-budget calculation, monthly reference ET was not varied from year to year, and was assumed 

to equal mean monthly reference ET. Reference ET was 

assumed to be the same each day of a given month.

Crop Coefficients 

A crop coefficient is an empirically derived ratio of the 

potential ET of a certain type of land cover and reference ET. 

Crop coefficients provide an index of the integrated effect of 

vegetation characteristics (reflectance, roughness, and plant 

physiology) on potential ET. Crop coefficients were assigned to 

each land-cover class (table 6). Crop coefficients were assumed 
28 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i 40 Mean annual reference evapotranspiration, in inches 2850607080 100 11390 Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum. 156°00' 156°10' 156°20' 156°30' 156°40' 21°00' 20°50' 20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

EXPLANATION

Figure 7. Mean annual reference evapotranspiration for Maui, Hawai‘i (modified from Giambelluca and others, 2014).
Model Input 29

Land-cover description Root depth, 

in inches Depletion fraction Cropcoefficient

Canopy capacity, 

in inches

Trunk-storage 

capacity, 

in inches

Forest land covers

Alien forest 60 0.50 a 0.33, b 0.44 0.05 0.01

Native forest 30 0.50 0.30 0.05 0.01

Tree plantation 60 0.50 a 0.33, b 0.44 0.05 0.01

Nonforest land covers

Coffee 48 0.40 0.91 0 0

Diversified agriculture 10 0.35 1.00 0 0

Fallow/grassland 39 0.60 0.95 0 0

Macadamia 60 0.50 0.91 0 0

Pineapple 18 0.50 0.30 0 0

Sugarcane 24 0.65 c 0.31–1.25 0 0

Taro 10 1.05 0.95 0 0

Developed, open space 12 0.50 1.18 0 0

Developed, low-intensity 12 0.50 1.18 0 0

Developed, medium-intensity 12 0.50 1.18 0 0

Developed, high-intensity 12 0.50 1.18 0 0

Golf course 30 0.50 0.85 0 0

Grassland 39 0.60 0.95 0 0

Shrubland 12 0.50 1.00 0 0

Sparsely vegetated 5 0.50 1.18 0 0

Water body and Reservoir 1 1.00 1.05 0 0

Wetland 39 0.50 1.18 0 0

Table 6. Land-cover parameters used in water-budget calculations for Maui, Hawai‘i.

[Crop coefficients for forests are used to compute the sum of transpiration and ground evaporation; canopy evaporation is computed separately. Crop coefficients 

for nonforests are used to compute the sum of all evaporative components]

to be temporally constant for all classes other than sugarcane. 

For nonforest land-cover classes, crop coefficients integrated 

the effects of transpiration, ground evaporation, and canopy 

evaporation. For forested land-cover classes, crop coefficients 

integrated the effects of transpiration and ground evaporation; 

canopy evaporation was accounted for separately (fig. 5). 

Crop coefficients for nonforest land covers (table 6) were 

obtained from published values or were derived from pan 

coefficients used for the same or similar land-cover classes in 

previous water budgets for Hawai‘i. A pan coefficient for a 

given land cover is the ratio of potential ET to pan evaporation. 

Hence, pan coefficients are analogous to crop coefficients. Pan 

coefficients for land-covers other than sugarcane were converted 

to crop coefficients by dividing the pan coefficients by 0.85, a 

factor that Engott (2011) used to convert crop coefficients to pan 

coefficients. Crop coefficients for fallow/grassland, grassland, 

pineapple, taro, wetland, water body, and reservoir land covers 

were obtained from Allen and others (1998). The midpoint of 

the range of crop coefficients for grazing pasture (rotated grazing) was used for fallow/grassland and grassland. The mean of 

the crop coefficients for wetlands with no frost was used for 

wetland. The crop coefficient for open water in the tropics was 

used for taro, water body, and reservoir. Crop coefficients for 

coffee and macadamia were obtained from Fares (2008). Crop 

coefficients for golf course and the developed land covers were 

derived from the pan coefficient used by Engott (2011) for 

developed land covers. Crop coefficients for diversified agriculture, shrubland, and sparsely vegetated were also derived from 

pan coefficients used by Engott (2011). 

The crop coefficient for sugarcane was varied with time 

according to the growth stages of sugarcane. The stages of 

sugarcane growth used in previous water budgets for Kaua‘i 

(Izuka and others, 2005) and Maui (Engott and Vana, 2007) 

were also used for this study. The pan coefficients for the 

different growth stages of the sugarcane range between 0.25 

and 1.0 (Engott and Vana, 2007, fig. 10) and were converted 

to crop coefficients by dividing them by 0.80. The factor 

0.80 is the ratio of the pan coefficient, 1.0, and the crop coefficient, 1.25 (Fares, 2008), for the middle-growth stage of 

sugarcane.

a

 Value used for forests inside the cloud zone, which is between altitudes of 2,000 and 8,200 feet.

b

 Value used for forests outside the cloud zone.

c

 Sugarcane crop coefficients vary with time according to the growth stages of the sugarcane and are between 0.31 and 1.25.
30 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Maps of mean soil (ground) evaporation and transpiration (Giambelluca and others, 2014) were used to derive crop 

coefficients for forest land covers. First, we computed water 

budgets for Kaua‘i, O‘ahu, and Maui using a range of crop 

coefficients for forest land covers. Data from Kaua‘i and O‘ahu 

were included in the analysis to develop regionally applicable 

crop coefficients. Next, the sum of ground evaporation and transpiration was determined for subareas on Kaua‘i, O‘ahu, and 

Maui with (1) native forest land cover inside the cloud zone, (2) 

native forest land cover outside the cloud zone, (3) alien forest 

land cover inside the cloud zone, and (4) alien forest land cover 

outside the cloud zone. The crop coefficients listed in table 6 

resulted in the best match, in terms of ground evaporation plus 

transpiration, between the maps of Giambelluca and others 

(2014) and subareas with forest land covers in water budgets of 

Kaua‘i, O‘ahu, and Maui. The derived crop coefficient for alien 

forests in the cloud zone is less than that for alien forests outside 

the cloud zone. This difference could be related to spatial differences in tree species or reduced transpiration with the presence 

of fog. The crop coefficients for alien forests are also used for 

tree plantations. For native forests, one crop coefficient is used 

because the crop coefficient estimated for areas in the cloud 

zone is nearly identical to that for areas outside the cloud zone. 

Moisture-Storage Capacity of the Plant-Root Zone 

The moisture-storage capacity of the plant-root zone 

(fig. 8) was computed as the product of available water 

capacity and root depth (equation 12). The Natural Resources 

Conservation Service (2006a) soil map and corresponding 

tables of available water capacities were used to quantify the 

available water capacity of the soils on Maui. For each soil 

unit, the tables list the minimum and maximum available water 

capacities for various ranges of depth. A depth-weighted mean 

available water capacity was computed for each soil type 

in the water-budget model. All depths of the soil unit “rock 

outcrop” have zero available water capacity according to tables 

of Natural Resources Conservation Service (2006a). The rock 

outcrop soil unit covers steep parts of southwest West Maui 

Mountain and steep ravines west of Kaupō Gap (fig. 2). For this 

study, zero available water capacity for the rock outcrop soil 

unit was considered too low because many areas with this soil 

unit on Maui were mapped as grassland or shrubland. Therefore, 

for the water-budget calculations, available water-capacity 

values for the rock land soil unit were used for all subareas with 

the rock outcrop soil unit designation. The rock land soil unit 

was selected because it was mapped near rock outcrop soils 

and because its available water capacity exceeds zero in the top 

8 inches of soil. Subareas with water body and reservoir land 

covers have zero available water capacity at all soil depths and 

therefore have zero soil-moisture storage capacity (fig. 8).

Root depths for each land-cover class were assigned 

values on the basis of published values and root depths used in 

previous water budgets in the Hawaiian Islands (table 6). The 

root depth used for sugarcane is the same as that used by Engott 

and Vana (2007). The root depth used for pineapple is the 

middle of the range of pineapple root depths reported in Fares 

(2008) and Allen and others (1998). The root depth used for 

diversified agriculture is near the middle of the range reported 

in Fares (2008) for typical diversified agriculture crops in the 

Hawaiian Islands. The root depth used for taro is the middle 

of the range reported in Fares (2008). The root depth used for 

wetland is the same as that used for grassland. For all other 

land-cover classes, the roots depths used are the same as those 

used by Engott (2011) in the water budget for the Island of 

Hawai‘i. 

Direct Recharge

For this study direct recharge was defined as water 

that passes directly to the groundwater system, completely 

bypassing the plant-root zone (equation 15). Hence, direct 

recharge was not subject to direct runoff or ET processes. 

Direct recharge was estimated for subareas with cesspools, 

and water body and reservoir land covers (fig. 4). For 

subareas within TMK parcels with cesspools, the wastewater 

effluent fluxes estimated by Whittier and El-Kadi (2013) 

were applied as direct recharge (see Septic-System Leaching 

section). The direct recharge rate from subareas with 

water body land covers was set at 0 in/yr because these 

were mapped near the coast and were assumed to have no 

net recharge. Hawaiian Commercial & Sugar Company 

estimated that the total seepage rate was between about 23 

and 31 Mgal/d for 31 of its easternmost reservoirs (State of 

Hawaii, 2010, p. C-2). To reproduce the middle of this range 

(27 Mgal/d) for these reservoirs in the water-budget model, a 

direct recharge rate of 1,268 in/yr was needed. Accordingly, 

a direct recharge rate of 1,268 in/yr was used in the water 

budget for subareas with reservoir land cover within the 

Pā‘ia, Kahului, Waikapū, and ‘Īao aquifer systems (figs. 1 

and 4). For all remaining subareas with reservoir land cover 

on Maui, the direct recharge was set at a more conservative 

rate of 528 in/yr, which also was used by Engott and Vana 

(2007).

Other Input

In addition to the water-budget inputs already listed, 

several other parameter inputs were required. The initial 

moisture storage for pervious fraction of subareas was set at 

50 percent of the soil moisture-storage capacity. The rainfallretention capacity for impervious surfaces was assumed to 

be 0.25 inch. The initial moisture storage for the impervious 

fraction of subareas was set at 0.125 inch, 50 percent of the 

rainfall-retention capacity. These values were also used for 

other recent Hawai‘i water budgets (Izuka and others, 2005; 

Engott and Vana, 2007; Engott, 2011). The effects of these 

inputs on regional-scale mean annual recharge generally were 

minor because they either pertained to only a small area or were 

applicable during only a small fraction of time.
Model Input 31

0.01 

Moisture-storage capacity, in inches

02.55.07.510.20

EXPLANATION

Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum.

156°00'

156°10'

156°20'

156°30'

156°40'

21°00'

20°50'

20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

Figure 8. Calculated moisture-storage capacity of the plant-root zone for Maui.
32 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Model Exclusions and Limitations

The water-budget model has several limitations. As 

described below, estimates of daily rainfall and runoff are the 

chief limitations of the model. We synthesized, rather than 

reconstructed, daily rainfall patterns during 1978–2007 from 

monthly rainfall maps using the method of fragments. Daily 

runoff was estimated from seasonal runoff-to-rainfall ratios. 

Additionally, some parameters and conditions excluded from 

the water-budget model were assumed to be inherent in other 

parameters used in the model, and could not be quantified 

owing to insufficient data, or were considered insignificant with 

respect to the study objectives. 

Owing to sparseness of data and the use of synthesized 

daily rainfall, the water-budget model did not reconstruct the 

actual distributions of the water-budget components (rainfall, 

fog interception, irrigation, septic-system leachate, runoff, 

evapotranspiration, and recharge) on Maui each day during 

1978–2007. The sets of rainfall fragments were assumed to 

represent the statistical properties of daily rainfall during 

1978–2007 even though they may not have been from this 

period. The reconstruction of actual daily runoff was also 

prevented by the use of seasonal runoff-to-rainfall ratios and 

the assumption that runoff occurs in a single day in response 

to rainfall. Additionally, runoff may have been overestimated 

by the model on days with light rain and underestimated on 

days with intense rain. The water-budget model did, however, 

reproduce the spatial distribution of (1) monthly rainfall on 

Maui during 1978–2007 that was estimated by Frazier (2012), 

and (2) mean monthly and mean annual rainfall on Maui 

during 1978–2007 that was estimated by Giambelluca and 

others (2013). For gaged drainage basins, the water-budget 

model reproduced seasonal runoff estimates that were based 

on streamflow data from stream-gaging stations. Datasets for 

calibrating recharge estimates of the model, however, were not 

available.

To estimate runoff, recent water budgets for Maui (Engott 

and Vana, 2007; Gingerich and Engott, 2012) used mean 

monthly runoff-to-rainfall ratios, whereas this study used 

seasonal and mean seasonal runoff-to-rainfall ratios. Monthly 

and mean monthly runoff-to-rainfall ratios, although available 

for gaged drainage basins, were not used in this study for the 

following reasons. First, the regional-regression models 

(table 5) computed mean seasonal (not monthly) runoff-torainfall ratios. Assigning seasonal runoff-to-rainfall ratios 

to all areas was considered a more consistent approach than 

assigning monthly runoff-to-rainfall ratios to gaged drainage 

basins and seasonal runoff-to-rainfall ratios to ungaged 

drainage basins. Second, analysis of the variability of monthly 

runoff-to-rainfall ratios within a season indicated that a mean 

seasonal ratio was sufficient to predict monthly direct runoff. 

Observed monthly runoff for gaged basins, based on streamflow 

data, was compared with monthly runoff estimated using (1) 

mean monthly runoff-to-rainfall ratios and (2) mean seasonal 

runoff-to-rainfall ratios. Based on these comparisons, runoff 

estimated from mean monthly runoff-to-rainfall ratios was not 

significantly better than runoff estimated from mean seasonal 

runoff-to-rainfall ratios. Last, compared to methods used in 

previous water budgets for Maui, the regional-regression 

models (table 5) were considered a more robust method for 

estimating runoff in ungaged areas. 

Some parameters and conditions not included in the 

calculations of the water-budget model were inherent in other 

parameters used in the model. For example, the permeability 

and vertical hydraulic conductivity of the plant-root zone 

and underlying substrate were not included in the model 

calculations. Their effect on direct runoff, however, would 

be inherent in streamflow records used to derive runoff-torainfall ratios that were used to compute direct runoff in the 

model. Also assumed to be inherent in streamflow records 

were reductions in runoff owing to ET of interflow in the 

subsurface. The reduction in potential ET of the plant-root 

zone in response to evaporation from the forest canopy was 

not included in the model calculations; it was, however, 

likely accounted for in the forest crop coefficients, which 

were calibrated to maps of mean transpiration and ground 

evaporation produced by Giambelluca and others (2014).

Re-infiltration of direct runoff, water that runs off one 

subarea and then infiltrates the plant-root zone of a different 

subarea, was not explicitly considered in the water-budget 

model. Re-infiltration of direct runoff within gaged basins was 

assumed to be inherent in streamflow records used to derive 

runoff-to-rainfall ratios. Spatial variations in the re-infiltration 

of direct runoff within a drainage basin, however, were not 

accounted for in the model. For example, if re-infiltration 

of direct runoff was considerable within streambeds, then 

recharge may have been underestimated in intra-channel 

(streambed) areas and overestimated in inter-channel (nonstreambed) areas. Surface water diversion systems, however, 

can reduce or prevent streambed seepage in lower reaches of 

many streams on Maui.

Some processes and conditions were excluded from 

the water-budget calculations. Interannual variations in 

potential ET were not accounted for in the model. Potential 

ET was also assumed to be uniform each day of the month 

and consequently may have been overestimated on cloudy 

days and underestimated on clear days. The errors caused by 

assuming uniform daily potential ET may be minor because 

the variation in daily potential ET for a given area is likely 

small compared to the variation in daily rainfall. For example, estimates of daily potential ET ranged from about 0.02 

to 0.26 inches for three climate stations at altitudes between 

2,500 and 5,770 ft on West Maui Mountain during November 

2003 through November 2005 (Engott and Vana, 2007). In 

contrast, daily rainfall on Maui ranges from zero to tens of 

inches. For subareas with impervious surfaces, potential ET 

was not reduced to account for evaporation from impervious 

surfaces. Adjustments to reference ET at scales less than the 

spatial resolution of the reference-ET maps, such as those 

related to shading in rugged terrain, were not accounted for 

in the model. Other conditions not considered in the model 

include the variability of soil moisture with depth within 
Water-Budget and Groundwater-Recharge Estimates 33

Figure 9. The average absolute percentage change in recharge of all 

the water-budget subareas with each successive model simulation.

0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50

Average absolute change in recharge 

from previous simulation, in percent

Number of simulations

0.1 percent

the plant-root zone, the effect of soil texture on irrigation 

demand, and removal of water from saturated groundwater 

zones by transpiration. Additionally, daily irrigation estimates were based on monthly variations in rainfall, runoff, 

and potential ET instead of daily variations. The daily irrigation estimates of the water-budget model may also differ 

from actual irrigation rates because, in addition to the model 

not reproducing actual daily rainfall, the model does not 

account for other factors—such as field observations, nonirrigation water needs, and water availability—that may be 

considered by irrigation managers each day.

The water-budget model did not account for water 

inflows from injection wells or leakage from water-transmission systems. Major water-transmission systems on Maui 

include the MDWS system and several surface-water diversion systems. Annual water losses from the MDWS system 

on Maui were, on average, between about 3.7 and 5.4 Mgal/d 

for fiscal years 2007–11 (written commun., Tui Anderson, 

MDWS, 2012). The largest surface-water diversion system is 

the East Maui Irrigation (EMI) system on northeast Haleakalā. 

On the basis of discharge measurements, Cheng (2012) 

determined net seepage losses and gains for 26 reaches whose 

combined length was 15 mi. Cheng (2012) estimated that the 

total water lost from the losing reaches of the EMI diversion 

system was about 3.4 Mgal/d, and the total water gained from 

gaining reaches was about 6.2 Mgal/d. Although the losses 

may be locally important, the estimated water losses from the 

MDWS and EMI systems are minor compared to island-wide 

precipitation. Therefore losses from the MDWS and EMI systems and other water-transmission systems were not accounted 

for in the water-budget model.

Many limitations of the water-budget model are a consequence of insufficient data. As more data become available, 

these limitations can be addressed and recharge estimates can 

be improved.

Model Randomness

The selection of monthly rainfall fragment sets (see 

Rainfall section) introduces randomness into the water-budget 

model. To account for this randomness, the water-budget model 

was run for multiple simulations, and the results of the simulations were averaged. For example, if the model was run for ten 

simulations, then mean recharge would be the average of ten 

recharge values–one value for each simulation. To determine 

the appropriate number of simulations to use for this study, 

the water-budget model for Maui was run for 50 simulations. 

Next, the absolute percentage change in mean recharge between 

successive simulations was determined for each of the 318,429 

subareas. Last, the absolute percentage change in mean recharge 

between successive simulations for all subareas was averaged 

for each number of simulations (fig. 9). After 25 simulations, the 

average percentage change did not exceed 0.1 percent, which 

was considered adequate for this study. Therefore, for each of 

the scenarios of this study, the water-budget model was run 

for 25 simulations, and the results of the 25 simulations were 

averaged. The water-budget model was run separately for each 

scenario because annual results were not included in the model 

output; only the time-averaged results for the entire simulation 

period were generated.

Water-Budget and GroundwaterRecharge Estimates

The water-budget model was used to compute recharge 

for two scenarios: average climate conditions and drought 

conditions. Monthly rainfall during 1978–2007 was used for 

the average-climate-conditions scenario. Monthly rainfall 

during 1998–2002 was used for the drought-conditions 

scenario. Both scenarios used 2010 land cover and mean 

monthly reference ET. Water-budget estimates for average 

climate conditions are compared with previous estimates.

Water-Budget and Recharge Estimates for 

Average Climate Conditions

For the Island of Maui, mean annual recharge is 

about 1,340 million gallons per day (Mgal/d) for average 

climate conditions (table 7). Recharge is about 45 percent of 

precipitation (rainfall and fog interception) and is about 

40 percent of total inflow (sum of precipitation, irrigation, 

septic leachate, and direct recharge). Direct recharge, which 

consists of seepage from reservoirs and cesspools, accounts 

for about 4 percent of overall recharge. Expressed as a depth 

of water uniformly distributed over the island, mean annual 

recharge is about 39 inches.

For Maui’s six aquifer sectors (fig. 1), mean annual 

recharge ranges from about 88 to 411 Mgal/d for average 

climate conditions (table 7). Of Maui’s total mean recharge for 

average climate conditions, 60 percent occurs in the Ko‘olau 

and Hāna aquifer sectors on windward Haleakalā, 20 percent 
34 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Table 7. Mean annual water-budget components from this study for aquifer sectors and all of Maui, Hawai‘i.

Location

Area, in 

square 

miles

Condition/ 

Scenario

Water-budget component, in million gallons per day

Rain Fog Irr Septic Direct 

Rech Runoff Canopy Evap Total ET Recharge

Island of 

Maui 727.93

Average climate 2,806.02 179.93 288.13 3.75 54.58 824.29 232.45 1,172.52 1,340.12

Drought 2,282.93 149.87 301.29 3.75 54.58 683.99 208.73 1,080.42 1,034.63

Wailuku 

aquifer 

sector

65.81

Average climate 247.46 19.61 11.91 0.30 7.45 97.39 24.08 90.79 98.90

Drought 188.77 15.51 12.54 0.30 7.45 73.69 20.46 80.03 71.51

Lahaina 

aquifer 

sector

95.88

Average climate 319.96 17.86 9.03 0.37 4.57 56.23 35.12 131.14 164.89

Drought 260.33 15.17 9.84 0.37 4.57 47.41 31.14 117.54 125.85

Central 

aquifer 

sector

229.68

Average climate 296.67 15.00 264.37 1.96 36.80 19.06 18.93 419.69 180.47

Drought 206.60 10.87 275.90 1.96 36.80 13.31 14.07 390.19 135.29

Ko‘olau 

aquifer 

sector

134.18

Average climate 945.13 78.48 2.46 0.96 5.32 366.27 91.52 255.04 410.55

Drought 798.67 66.76 2.60 0.96 5.32 307.29 84.85 240.52 324.96

Hāna 

aquifer 

sector

89.41

Average climate 800.57 42.91 0.36 0.16 0.41 272.21 57.93 174.86 397.13

Drought 682.11 36.97 0.41 0.16 0.41 232.40 54.55 165.41 321.50

Kahikinui 

aquifer 

sector

112.97

Average climate 196.23 6.07 0.00 0.00 0.03 13.13 4.87 101.00 88.18

Drought 146.45 4.59 0.00 0.00 0.03 9.89 3.66 86.73 55.52

[See fig. 1 for locations of aquifer sectors; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation, 

Total ET, total evapotranspiration which includes canopy evaporation. Average climate conditions for this study are 1978–2007 rainfall and 2010 land cover; 

drought conditions for this study are 1998–2002 rainfall and 2010 land cover. Components may not balance because of rounding and direct recharge from cesspools and reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may 

discharge to streams as base flow]

occurs in the Lahaina and Wailuku aquifer sectors on West Maui 

Mountain, and 20 percent occurs in the Kahikinui and Central 

aquifer sectors on leeward Haleakalā and the isthmus. About 

20 percent of the recharge for the Central aquifer sector comes 

from direct recharge, most of which is from reservoir seepage. 

Irrigation in the Central aquifer sector is also substantial and is 

about 43 percent of total inflow. 

For Maui’s 25 aquifer systems (fig. 1), mean annual 

recharge ranges from about 13 to 222 Mgal/d for average 

climate conditions (table 8, see Water-budget area description 

A). Total mean annual recharge is about 150 Mgal/d for the 

Makawao, Ha‘ikū, and Honopou aquifers systems of upcountry 

Maui. In terms of recharge expressed as an average depth 

of water, the aquifer systems with the greatest recharge are 

Kūhiwa, Kīpahulu, and Waiho‘i, which are on windward 

Haleakalā; the aquifer systems with the least recharge are 

Kama‘ole, Luala‘ilua, and Makawao, which are on leeward 

Haleakalā. Irrigation is less than 10 percent of inflow for 

all aquifer systems other than Waikapū, Kahului, and Pā‘ia; 

irrigation in the latter two aquifer systems exceeds rainfall. 

Septic leachate is no more than 1 percent of total water inflow 

for Maui’s aquifer systems. Direct recharge is less than 10 

percent of recharge for all aquifer systems other than ‘Īao, 

Kahului, and Pā‘ia.

The spatial pattern of mean annual recharge on Maui for 

average climate conditions (fig. 10) resembles the pattern of 

mean annual rainfall (fig. 3), but also reflects spatial variations 

in vegetation and soils, irrigation in agricultural and developed 

areas, and persistent cloud layers where fog interception 

supplements rainfall. Wet, upland parts of the island typically 

have more recharge than dry, coastal areas. Areas with the 

highest recharge include the mid-altitude slopes of windward 

Haleakalā and the uplands of West Maui Mountain. Areas with 

the lowest recharge include the leeward slopes of Haleakalā and 

coastal lowlands of West Maui Mountain. West Maui Mountain 

has steep gradients in mean recharge distribution: recharge 

exceeds 200 in/yr near the summit, but is less than 10 in/yr for 

most coastal areas. Half of Maui has mean recharge rates that 

are less than 20 in/yr. Only about 10 percent of the island has 

mean recharge rates that exceed 100 in/yr. In general, areas that 
Water-Budget and Groundwater-Recharge Estimates 35

Aquifer 

sector

Aquifer 

system

Source of 

estimate

Waterbudget-area 

description

Area, in 

square 

miles

Condition/ 

Scenario

Water-budget component, in million gallons per day

Rain Fog Irr Septic Direct Rech Runoff Canopy Evap Total ET Recharge

Wailuku Waikapü This study A 17.31 Average 

climate

42.44 4.27 8.92 0.12 1.02 14.50 3.15 22.72 19.59

This study A 17.31 Drought 27.80 2.97 9.35 0.12 1.02 9.57 2.27 19.85 11.98This study B 15.06 Average 

climate

40.47 4.27 2.73 0.01 0.94 14.37 3.11 16.23 17.84

This study B 15.06 Drought 26.63 2.97 2.93 0.01 0.94 9.49 2.24 13.60 10.49WRPP C 13.53 Natural 27.70 n/a n/a n/a n/a 1.90 n/a 20.00 6

Wailuku ‘Īao This study A 25.28 Average 

climate

98.52 7.44 2.81 0.11 6.37 41.39 8.61 33.52 40.64

This study A 25.28 Drought 74.84 5.86 3.01 0.11 6.37 30.96 7.35 29.29 30.43This study B 18.21 Average 

climate

89.97 7.44 0.18 0.04 1.13 38.69 8.30 25.75 34.33

This study B 18.21 Drought 69.00 5.86 0.20 0.04 1.13 29.22 7.12 22.59 24.53WRPP C 17.81 Natural 82.30 n/a n/a n/a n/a 33.10 n/a 33.90 15 (27)

Wailuku Waihe‘e This study A 12.44 Average 

climate

61.46 4.36 0.11 0.07 0.04 24.73 4.98 17.37 23.95

This study A 12.44 Drought 49.54 3.65 0.11 0.07 0.04 20.05 4.40 15.57 17.82This study B 12.17 Average 

climate

61.04 4.36 0.09 0.06 0.03 24.56 4.95 17.11 23.91

This study B 12.17 Drought 49.23 3.65 0.09 0.06 0.03 19.93 4.37 15.36 17.79WRPP C 11.87 Natural 62.70 n/a n/a n/a n/a 28.30 n/a 22.60 12 (16)

Table 8. Mean annual water-budget components from this study for average climate and drought conditions and components from the Water Resources Protection Plan (State of Hawai‘i, 1990; 2008) for aquifer systems of Maui, Hawai‘i.[See fig. 1 for locations of aquifer systems; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation; Total ET, total evapotranspiration, which includes canopy evaporation. WRPP, Water Resources Protection Plan (State of Hawai‘i, 1990; 2008). Water-budget-area descriptions: A, entire aquifer-system area; B, entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Wailuku Basalt (see fig. 1); C, entire aquifer-system area excluding caprock areas as defined in the WRPP; D entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Honomanü Basalt/Kula Volcanics (see fig. 1). Average climate conditions for this study are 1978–2007 rainfall and 2010 land cover. Drought conditions for this study are 1998–2002 rainfall and 2010 land cover. Natural conditions for WRPP are 1916–83 mean rainfall and a uniform, unirrigated land cover. All WRPP components not in parentheses are from the 1990 WRPP. WRPP aquifer-system recharge values that were updated in the 2008 WRPP, shown in parentheses, were provided by the State of Hawai‘i Commission on Water Resource Management. n/a, not accounted for or not reported. Components may not balance because of rounding and direct recharge from cesspools and reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may discharge to streams as base flow]
36 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i Aquifer sector Aquifer system Source of estimate Water- budget-area description Area, in square miles Condition/ Scenario Water-budget component, in million gallons per day Rain Fog Irr Septic Direct Rech Runoff Canopy Evap Total ET Recharge Wailuku Kahakuloa This study

A 10.78 Average 

climate

45.04 3.54 0.07 0.00 0.02 16.77 7.34 17.18 14.72

This study

A 10.78 Drought 36.59 3.03 0.07 0.00 0.02 13.11 6.44 15.32 11.28

This study

B 10.78 Average 

climate

45.04 3.54 0.07 0.00 0.02 16.77 7.34 17.18 14.72

This study

B 10.78 Drought 36.59 3.03 0.07 0.00 0.02 13.11 6.44 15.32 11.28

WRPP

C 10.22 Natural 46.70 n/a n/a n/a n/a 16.10 n/a 19.50 11

Lahaina Honokōhau This study

A 13.87 Average 

climate

73.34 5.72 0.11 0.00 0.01 22.92 8.96 23.81 32.44

This study

A 13.87 Drought 61.65 5.03 0.11 0.00 0.01 19.98 8.11 21.44 25.35

This study

B 13.80 Average 

climate

73.21 5.72 0.11 0.00 0.01 22.91 8.95 23.71 32.41

This study

B 13.80 Drought 61.55 5.03 0.11 0.00 0.01 19.97 8.11 21.35 25.34

WRPP

C 13.23 Natural 80.60 n/a n/a n/a n/a 38.40 n/a 25.20 17

Lahaina Honolua This study

A 17.77 Average 

climate

67.18 2.17 2.31 0.06 0.85 9.54 10.08 35.79 27.26

This study

A 17.77 Drought 55.67 1.92 2.59 0.06 0.85 7.91 9.01 32.59 20.59

This study

B 16.94 Average 

climate

65.68 2.17 1.71 0.06 0.77 9.44 10.07 34.12 26.85

This study

B 16.94 Drought 54.53 1.92 1.93 0.06 0.77 7.83 9.00 31.07 20.29

WRPP

C 17.61 Natural 72.90 n/a n/a n/a n/a 20.10 n/a 33.50 19

Lahaina Honokōwai This study

A 23.54 Average 

climate

70.40 3.50 5.88 0.05 2.99 10.34 7.45 36.07 36.75

This study

A 23.54 Drought 57.63 3.04 6.38 0.05 2.99 8.68 6.67 32.51 29.33

This study

B 22.70 Average 

climate

69.60 3.50 5.05 0.05 2.94 10.28 7.45 34.65 36.43

This study

B 22.70 Drought 57.09 3.04 5.50 0.05 2.94 8.65 6.66 31.18 29.09

WRPP

C 22.67 Natural 68.00 n/a n/a n/a n/a 11.90 n/a 43.20 13

Table 8. Mean annual water-budget components from this study for average climate and drought conditions and components from the Water Resources Protection Plan (State of Hawai‘i, 1990; 2008) for aquifer systems of Maui, Hawai‘i.—Continued[See fig. 1 for locations of aquifer systems; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation; Total ET, total evapotranspiration, which includes canopy evaporation. WRPP, Water Resources Protection Plan (State of Hawai‘i, 1990; 2008). Water-budget-area descriptions: A, entire aquifer-system area; B, entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Wailuku Basalt (see fig. 1); C, entire aquifer-system area excluding caprock areas as defined in the WRPP; D entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Honomanü Basalt/Kula Volcanics (see fig. 1). Average climate conditions for this study are 1978–2007 rainfall and 2010 land cover. Drought conditions for this study are 1998–2002 rainfall and 2010 land cover. Natural conditions for WRPP are 1916–83 mean rainfall and a uniform, unirrigated land cover. All WRPP components not in parentheses are from the 1990 WRPP. WRPP aquifer-system recharge values that were updated in the 2008 WRPP, shown in parentheses, were provided by the State of Hawai‘i Commission on Water Resource Management. n/a, not accounted for or not reported. Components may not balance because of rounding and direct recharge from cesspools and reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may discharge to streams as base flow]
Water-Budget and Groundwater-Recharge Estimates 37

Aquifer 

sector

Aquifer 

system

Source of 

estimate

Waterbudget-area 

description

Area, in 

square 

miles

Condition/ 

Scenario

Water-budget component, in million gallons per day

Rain Fog Irr Septic Direct Rech Runoff Canopy Evap Total ET Recharge

Lahaina Launiupoko This study A 20.75 Average climate 64.06 4.16 0.70 0.24 0.63 8.94 5.65 19.58 41.34

This study A 20.75 Drought 52.46 3.45 0.73 0.24 0.63 7.50 4.97 17.36 32.71This study B 18.72 Average 

climate

62.85 4.16 0.29 0.24 0.62 8.83 5.57 18.35 41.01

This study B 18.72 Drought 51.70 3.45 0.31 0.24 0.62 7.44 4.92 16.33 32.55WRPP C 18.29 Natural 65.30 n/a n/a n/a n/a 14.80 n/a 34.80 16

Lahaina Olowalu This study A 8.12 Average 

climate

21.34 1.11 0.03 0.02 0.09 2.35 1.79 7.09 13.19

This study A 8.12 Drought 16.43 0.87 0.03 0.02 0.09 1.83 1.48 6.19 9.45This study B 7.12 Average 

climate

20.78 1.11 0.00 0.01 0.00 2.32 1.77 6.60 12.98

This study B 7.12 Drought 16.07 0.87 0.00 0.01 0.00 1.81 1.47 5.81 9.32WRPP C 6.81 Natural 20.40 n/a n/a n/a n/a 3.60 n/a 13.00 4

Lahaina Ukumehame This study A 11.83 Average climate 23.64 1.20 0.00 0.00 0.00 2.14 1.19 8.80 13.91

This study A 11.83 Drought 16.49 0.86 0.00 0.00 0.00 1.51 0.90 7.45 8.42This study B 10.78 Average 

climate

22.99 1.20 0.00 0.00 0.00 2.11 1.16 8.36 13.72

This study B 10.78 Drought 16.08 0.86 0.00 0.00 0.00 1.50 0.88 7.13 8.34WRPP C 10.61 Natural 25.30 n/a n/a n/a n/a 3.50 n/a 18.20 4

Central Kahului This study A 27.50 Average 

climate

20.19 0.00 76.58 0.09 5.88 1.68 0.59 79.06 25.17

This study A 27.50 Drought 12.70 0.00 78.85 0.09 5.88 0.96 0.40 77.00 23.09This study D 23.05 Average 

climate

17.28 0.00 70.49 0.07 5.85 1.27 0.40 69.28 23.57

This study D 23.05 Drought 10.85 0.00 72.62 0.07 5.85 0.73 0.28 67.57 21.72WRPP C 9.54 Natural 9.10 n/a n/a n/a n/a 0.50 n/a 6.80 2

Table 8. Mean annual water-budget components from this study for average climate and drought conditions and components from the Water Resources Protection Plan (State of Hawai‘i, 1990; 2008) for aquifer systems of Maui, Hawai‘i.—Continued[See fig. 1 for locations of aquifer systems; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation; Total ET, total evapotranspiration, which includes canopy evaporation. WRPP, Water Resources Protection Plan (State of Hawai‘i, 1990; 2008). Water-budget-area descriptions: A, entire aquifer-system area; B, entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Wailuku Basalt (see fig. 1); C, entire aquifer-system area excluding caprock areas as defined in the WRPP; D entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Honomanü Basalt/Kula Volcanics (see fig. 1). Average climate conditions for this study are 1978–2007 rainfall and 2010 land cover. Drought conditions for this study are 1998–2002 rainfall and 2010 land cover. Natural conditions for WRPP are 1916–83 mean rainfall and a uniform, unirrigated land cover. All WRPP components not in parentheses are from the 1990 WRPP. WRPP aquifer-system recharge values that were updated in the 2008 WRPP, shown in parentheses, were provided by the State of Hawai‘i Commission on Water Resource Management. n/a, not accounted for or not reported. Components may not balance because of rounding and direct recharge from cesspools and reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may discharge to streams as base flow]
38 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i Aquifer sector Aquifer system Source of estimate Water- budget-area description Area, in square miles Condition/ Scenario Water-budget component, in million gallons per day Rain Fog Irr Septic Direct Rech Runoff Canopy Evap Total ET Recharge Central Pā‘ia This study

A 59.04 Average 

climate

70.55 0.00 176.96 0.11 26.20 4.10 1.04 196.45 74.60

This study

A 59.04 Drought 50.38 0.00 185.09 0.11 26.20 2.98 0.76 192.97 67.70

WRPP

C 60.73 Natural 78.10 n/a n/a n/a n/a 2.90 n/a 54.90 17

Central Makawao This study

A 51.78 Average 

climate

107.85 5.57 3.54 1.22 3.18 9.67 4.84 64.78 46.83

This study

A 51.78 Drought 78.54 4.20 4.25 1.22 3.18 7.11 3.83 56.66 27.91

WRPP

C 52.93 Natural 95.80 n/a n/a n/a n/a 7.60 n/a 70.60 15

Central Kama‘ole This study

A 91.36 Average 

climate

98.08 9.43 7.29 0.54 1.54 3.61 12.46 79.40 33.87

This study

A 91.36 Drought 64.98 6.67 7.71 0.54 1.54 2.26 9.08 63.56 16.59

WRPP

C 89.22 Natural 118.90 n/a n/a n/a n/a 8.50 n/a 85.00 25

Ko‘olau Ha‘ikū This study

A 35.20 Average 

climate

161.52 12.80 0.68 0.84 4.13 39.54 17.55 75.52 64.74

This study

A 35.20 Drought 130.93 10.55 0.81 0.84 4.13 31.79 15.47 69.07 46.26

WRPP

C 35.71 Natural 163.20 n/a n/a n/a n/a 34.00 n/a 68.00 61

Ko‘olau Honopou This study

A 17.70 Average 

climate

118.38 6.71 0.01 0.11 0.55 45.54 15.13 41.95 38.21

This study

A 17.70 Drought 101.37 5.79 0.02 0.11 0.55 38.52 13.89 39.63 29.42

WRPP

C 18.87 Natural 129.40 n/a n/a n/a n/a 32.30 n/a 35.90 57

Ko‘olau Waikamoi This study

A 26.06 Average 

climate

210.90 18.49 0.00 0.00 0.55 95.48 22.76 48.41 85.91

This study

A 26.06 Drought 184.91 16.19 0.00 0.00 0.55 83.45 21.36 46.45 71.27

WRPP

C 26.08 Natural 223.50 n/a n/a n/a n/a 83.20 n/a 49.70 91

Ko‘olau Ke‘anae This study

A 55.22 Average 

climate

454.33 40.48 1.77 0.01 0.09 185.71 36.08 89.16 221.69

This study

A 55.22 Drought 381.46 34.23 1.77 0.01 0.09 153.53 34.13 85.37 178.01

WRPP

C 55.56 Natural 489.40 n/a n/a n/a n/a 195.80 n/a 105.80 188

Table 8. Mean annual water-budget components from this study for average climate and drought conditions and components from the Water Resources Protection Plan (State of Hawai‘i, 1990; 2008) for aquifer systems of Maui, Hawai‘i.—Continued[See fig. 1 for locations of aquifer systems; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation; Total ET, total evapotranspiration, which includes canopy evaporation. WRPP, Water Resources Protection Plan (State of Hawai‘i, 1990; 2008). Water-budget-area descriptions: A, entire aquifer-system area; B, entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Wailuku Basalt (see fig. 1); C, entire aquifer-system area excluding caprock areas as defined in the WRPP; D entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Honomanü Basalt/Kula Volcanics (see fig. 1). Average climate conditions for this study are 1978–2007 rainfall and 2010 land cover. Drought conditions for this study are 1998–2002 rainfall and 2010 land cover. Natural conditions for WRPP are 1916–83 mean rainfall and a uniform, unirrigated land cover. All WRPP components not in parentheses are from the 1990 WRPP. WRPP aquifer-system recharge values that were updated in the 2008 WRPP, shown in parentheses, were provided by the State of Hawai‘i Commission on Water Resource Management. n/a, not accounted for or not reported. Components may not balance because of rounding and direct recharge from cesspools and reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may discharge to streams as base flow]
Water-Budget and Groundwater-Recharge Estimates 39

Aquifer 

sector

Aquifer 

system

Source of 

estimate

Waterbudget-area 

description

Area, in 

square 

miles

Condition/ 

Scenario

Water-budget component, in million gallons per day

Rain Fog Irr Septic Direct Rech Runoff Canopy Evap Total ET Recharge

Hāna Kūhiwa This study A 13.59 Average 

climate

175.88 10.28 0.00 0.01 0.02 66.17 12.04 27.31 92.67

This study A 13.59 Drought 150.15 8.88 0.00 0.01 0.02 56.24 11.55 26.60 75.97WRPP C 13.14 Natural 160.80 n/a n/a n/a n/a 104.50 n/a 25.00 31

Hāna Kawaipapa This study A 31.75 Average 

climate

255.24 12.52 0.17 0.12 0.32 87.48 18.39 66.38 114.44

This study A 31.75 Drought 216.84 10.91 0.19 0.12 0.32 74.45 17.33 62.38 91.33WRPP C 32.60 Natural 263.90 n/a n/a n/a n/a 93.10 n/a 62.10 109

Hāna Waiho‘i This study A 14.40 Average 

climate

132.19 8.35 0.02 0.02 0.04 53.46 8.95 28.35 58.77

This study A 14.40 Drought 113.44 7.24 0.02 0.02 0.04 45.98 8.42 26.78 47.95WRPP C 15.18 Natural 114.20 n/a n/a n/a n/a 36.90 n/a 28.90 40

Hāna Kīpahulu This study A 29.67 Average 

climate

237.26 11.76 0.17 0.01 0.03 65.10 18.55 52.82 131.25

This study A 29.67 Drought 201.68 9.94 0.20 0.01 0.03 55.73 17.25 49.65 106.25WRPP C 30.22 Natural 213.00 n/a n/a n/a n/a 59.00 n/a 57.60 96

Kahikinui Kaupō This study A 20.31 Average 

climate

58.19 1.60 0.00 0.00 0.02 5.87 0.44 22.65 31.28

This study A 20.31 Drought 46.06 1.25 0.00 0.00 0.02 4.65 0.38 20.52 22.24WRPP C 20.73 Natural 87.80 n/a n/a n/a n/a 12.80 n/a 39.50 36

Kahikinui Nakula This study A 30.21 Average 

climate

62.41 0.99 0.00 0.00 0.00 4.26 0.52 30.15 29.00

This study A 30.21 Drought 47.67 0.76 0.00 0.00 0.00 3.21 0.42 26.28 19.25WRPP C 30.94 Natural 69.20 n/a n/a n/a n/a 2.90 n/a 50.10 16

Table 8. Mean annual water-budget components from this study for average climate and drought conditions and components from the Water Resources Protection Plan (State of Hawai‘i, 1990; 2008) for aquifer systems of Maui, Hawai‘i.—Continued[See fig. 1 for locations of aquifer systems; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation; Total ET, total evapotranspiration, which includes canopy evaporation. WRPP, Water Resources Protection Plan (State of Hawai‘i, 1990; 2008). Water-budget-area descriptions: A, entire aquifer-system area; B, entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Wailuku Basalt (see fig. 1); C, entire aquifer-system area excluding caprock areas as defined in the WRPP; D entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Honomanü Basalt/Kula Volcanics (see fig. 1). Average climate conditions for this study are 1978–2007 rainfall and 2010 land cover. Drought conditions for this study are 1998–2002 rainfall and 2010 land cover. Natural conditions for WRPP are 1916–83 mean rainfall and a uniform, unirrigated land cover. All WRPP components not in parentheses are from the 1990 WRPP. WRPP aquifer-system recharge values that were updated in the 2008 WRPP, shown in parentheses, were provided by the State of Hawai‘i Commission on Water Resource Management. n/a, not accounted for or not reported. Components may not balance because of rounding and direct recharge from cesspools and reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may discharge to streams as base flow]
40 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i Aquifer sector Aquifer system Source of estimate Water- budget-area description Area, in square miles Condition/ Scenario Water-budget component, in million gallons per day Rain Fog Irr Septic Direct Rech Runoff Canopy Evap Total ET Recharge Kahikinui Luala‘ilua This study

A 62.45 Average 

climate

75.63 3.48 0.00 0.00 0.01 3.00 3.91 48.20 27.90

This study

A 62.45 Drought 52.72 2.58 0.00 0.00 0.01 2.03 2.86 39.93 14.03

WRPP

C 68.11 Natural 123.20 n/a n/a n/a n/a 6.50 n/a 90.80 26

Table 8. Mean annual water-budget components from this study for average climate and drought conditions and components from the Water Resources Protection Plan (State of Hawai‘i, 1990; 2008) for aquifer systems of Maui, Hawai‘i.—Continued[See fig. 1 for locations of aquifer systems; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation; Total ET, total evapotranspiration, which includes canopy evaporation. WRPP, Water Resources Protection Plan (State of Hawai‘i, 1990; 2008). Water-budget-area descriptions: A, entire aquifer-system area; B, entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Wailuku Basalt (see fig. 1); C, entire aquifer-system area excluding caprock areas as defined in the WRPP; D entire aquifer-system area excluding areas seaward of the 0-ft elevation contour of the top of Honomanü Basalt/Kula Volcanics (see fig. 1). Average climate conditions for this study are 1978–2007 rainfall and 2010 land cover. Drought conditions for this study are 1998–2002 rainfall and 2010 land cover. Natural conditions for WRPP are 1916–83 mean rainfall and a uniform, unirrigated land cover. All WRPP components not in parentheses are from the 1990 WRPP. WRPP aquifer-system recharge values that were updated in the 2008 WRPP, shown in parentheses, were provided by the State of Hawai‘i Commission on Water Resource Management. n/a, not accounted for or not reported. Components may not balance because of rounding and direct recharge from cesspools and reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may discharge to streams as base flow]
Water-Budget and Groundwater-Recharge Estimates 41 10 20 0 30 40 100 200 150 300 50 Mean annual groundwater recharge, in inches Areas where mean annual recharge is between 455 and 1,894 inches − includes taro fields and reservoirs, which are difficult to see at this scale Boundaries of named aquifer systems (modified from State of Hawai‘i, 2008) EXPLANATION Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum.

Pā‘ia

Kama‘ole

‘Īao

Ha‘ikū

Makawao

Kahului

Waikapū

Honokōwai

Honolua

Honopou

Launiupoko

Waihe‘e

Honokōhau

Ukumehame

Kahakuloa

Olowalu

Waikamoi

Ke‘anae

Luala‘ilua

Nakula Kaupō

Kīpahulu

Waiho‘i

Kawaipapa

Kūhiwa

156°00'

156°10'

156°20'

156°30'

156°40'

21°00'

20°50'

20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

Figure 10. The distribution of mean annual groundwater recharge for average climate conditions (1978–2007), Island of Maui, Hawai‘i, calculated using the water-budget model.
42 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

have water inflows from irrigation, septic-system leachate, and 

direct recharge have higher recharge than nearby areas without 

these supplemental water inflows. For example, in the isthmus, 

reservoirs and irrigated fields of sugarcane have higher recharge 

rates than nearby areas. 

Mean annual recharge ranges spatially from 0 to 99 percent 

of total water inflow (fig. 11). Most of Maui has recharge rates 

that are less than 60 percent of water inflow. The areas with the 

highest fraction, greater than 80 percent, are mainly reservoirs. 

The areas with the lowest fraction, less than 20 percent are 

chiefly dry, lowland areas.

Comparison with Water Resources Protection Plan

The 1990 Water Resources Protection Plan (WRPP) 

included recharge estimates, in million gallons per day, for 

all aquifer systems on Maui for conditions without irrigation 

(State of Hawaii, 1990, p. B-7). Recharge estimates reported in 

the WRPP were based on a water budget that considered mean 

annual rainfall, runoff, and evapotranspiration. Estimates of precipitation, runoff, and ET reported in the WRPP were converted 

from units of inches per year to million gallons per day (table 

8) on the basis of the aquifer-system areas provided by the State 

of Hawai‘i Commission on Water Resource Management (Roy 

Hardy, written commun., 2014). 

Caprock areas were excluded from the calculation of aquifer-system recharge in the WRPP. Caprock consists primarily 

of sedimentary deposits that collectively have low permeability 

and overlie volcanic rocks with relatively high permeability. 

The spatial extent of caprock areas on Maui, however, was not 

defined in the WRRP. To facilitate comparisons between results 

of the WRPP and the results of this study, the spatial extent of 

caprock was estimated. Caprock was assumed to (1) consist 

of areas seaward of the 0-ft altitude contour of the top of the 

Wailuku Basalt for aquifer systems of the Lahaina and Wailuku 

aquifer sectors, (2) consist of areas seaward of the 0-ft altitude 

contour of the top of the Kula Volcanics/ Honomanū Basalt for 

the Kahului aquifer system, and (3) be negligible for all remaining aquifer systems (fig. 1). Water-budget components estimated 

for parts of aquifers inland from estimated caprock areas are 

shown in table 8, water-budget area descriptions B and D. Areas 

defined as caprock for this study were excluded from comparisons with the results of the WRPP, as described next. 

Recharge estimates for average climate conditions from 

this study are greater than estimates of the WRPP for 22 of the 

25 aquifer systems (table 8). The part of the Kahului aquifer system that was not considered as caprock for this study, 

however, is about 1.5 times greater than that reported in the 

WRPP. Area inconsistencies aside, the reasons for differences in 

the recharge estimates are related to methods and datasets used 

to estimate individual water-budget components. For most of 

these aquifer systems, total ET estimates of this study are less 

than those of the WRPP. The 1990 WRPP states that ET was 

estimated as (1) 40 in/yr for areas where mean rainfall was at 

least 55 in/yr and (2) 73 percent of rainfall for areas where mean 

rainfall was less than 55 in/yr. In the water budget for this study, 

ET is computed daily on the basis of available mean monthly 

potential ET, soil moisture, vegetative cover, and soil type. Runoff estimates from this study are less than those of the WRPP 

for some aquifer systems. The runoff-rainfall relations used to 

estimate runoff in the WRPP were developed from streamflow 

measurements without subtracting base flow. In contrast, base 

flow was subtracted from streamflow measurements when 

developing the runoff-rainfall relations for this study. Owing to 

the inclusion of fog interception, some precipitation estimates of 

this study are greater than those of the WRPP, which omit fog 

interception. Some differences in precipitation are also related 

to the source of rainfall data. Rainfall estimates of the WRPP 

are based on 1916–83 mean isohyets (Giambelluca and others, 

1986), whereas estimates of this study are based on 1978–2007 

monthly rainfall maps (Frazier, 2012). Additionally, the WRPP 

did not account for septic leachate, irrigation, and direct 

recharge from reservoirs and cesspools. Irrigation is a considerable source of recharge, especially in the isthmus. Even if direct 

recharge is omitted, however, recharge estimates from this study 

exceed the estimates of the WRPP by at least 5 percent for 21 

aquifer systems. 

Recharge estimates of this study for average climate conditions are less than estimates of the WRPP for the Honopou, 

Waikamoi, and Kaupō aquifer systems (table 8). Runoff estimates from this study for the Honopou and Waikamoi aquifer 

systems are considerably greater than those of the WRPP. The 

total ET estimate for the Honopou aquifer system is also much 

greater than that of the WRPP. Precipitation estimates of this 

study for parts of leeward Haleakalā, including the Kaupō aquifer system, are much lower than those of the WRPP. 

The 1990 WRPP was updated in 2008. Recharge estimates 

for aquifer systems were not reported in the 2008 WRPP (State 

of Hawaii, 2008) but were provided by the State of Hawai‘i 

Commission on Water Resource Management (Roy Hardy, written commun., 2014). Recharge values used in the 2008 WRPP 

for Maui’s aquifer systems, other than ‘Īao and Waihe‘e, were 

the same as those presented in the 1990 WRPP. For the ‘Īao and 

Waihe‘e aquifer systems, recharge estimates for average climate 

conditions from this study are greater than the estimates used in 

the 2008 WRPP and those reported in the 1990 WRPP (table 8).

Comparison with Previous Studies for the ‘Īao and 

Waihe‘e aquifer systems

The ‘Īao and Waihe‘e aquifer systems, on the windward 

side of West Maui Mountain (fig. 1), are the principal sources 

of domestic water supply for the Island of Maui. Recharge 

estimates of this study for these aquifer systems are compared with recent estimates by Shade (1997) and Engott and 

Vana (2007). The recharge estimate of Shade (1997) for the 

‘Īao aquifer system (table 9) is based on 1916–83 rainfall 

and 1986–95 land cover, which included irrigated sugarcane, 

macadamia nut trees, and pineapple. The recharge estimates 

of Engott and Vana (2007) for the ‘Īao and Waihe‘e aquifer 

systems are for a scenario referred to as land use II, which 

used 1926–2004 rainfall and 2000–04 land cover. 
Water-Budget and Groundwater-Recharge Estimates 43 2000.1 40608099 EXPLANATION Fraction of water inflow (rainfall, fog interception, irrigation, septic-system leachate, and direct recharge) that becomes recharge, in percent Boundaries of named aquifer systems (modified from State of Hawai‘i, 2008) Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum.

Pā‘ia

Kama‘ole

‘Īao

Ha‘ikū

Makawao

Kahului

Waikapū

Honokōwai

Honolua

Honopou

Launiupoko

Waihe‘e

Honokōhau

Ukumehame

Kahakuloa

Olowalu

Waikamoi

Ke‘anae

Luala‘ilua

Nakula Kaupō

Kīpahulu

Waiho‘i

Kawaipapa

Kūhiwa

156°00'

156°10'

156°20'

156°30'

156°40'

21°00'

20°50'

20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

Figure 11. Fraction of total water inflow that becomes groundwater recharge in the water-budget calculations for average climate conditions (1978–2007) Island of Maui, Hawai‘i. 
44 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Table 9. Comparison of mean annual water-budget components from this study with components from previous studies for ‘Īao and Waihe‘e aquifer 

systems of Maui, Hawai‘i.

Location Source of 

estimate

Condition/

Scenario

Area, in 

square 

miles

Water-budget component, in million gallons per day

Rain Fog Irr Septic Direct 

Rech Runoff Canopy Evap Total ET Recharge

‘Īao 

aquifer 

system

This 

study

Average 

climate

25.28 98.52 7.44 2.81 0.11 6.37 41.39 8.61 33.52 40.64

Engott 

and 

Vana 

(2007)

Land use 

II

25.30 97.76 9.90 1.90 n/a n/a 31.53 n/a 31.28 45.96

Shade 

(1997)

1986–95 24.69 100.00 n/a 3.00 n/a n/a 41.00 n/a 26.00 36.00

Waihe‘e 

aquifer 

system

This 

study

Average 

climate

12.44 61.46 4.36 0.11 0.07 0.04 24.73 4.98 17.37 23.95

Engott 

and 

Vana 

(2007)

Land use 

II

12.45 56.66 6.24 0.02 n/a n/a 16.07 n/a 17.11 29.72

[See fig. 1 for locations of aquifer systems; Irr, irrigation; Septic, septic-system leachate; Direct Rech, direct recharge; Canopy Evap, forest-canopy evaporation; Total ET, total evapotranspiration, which includes canopy evaporation.; Average climate conditions for this study are 1978–2007 rainfall and 2010 land 

cover; Land use II conditions for Engott and Vana (2007) are 1926–2004 rainfall and 2000–04 land cover. 1986–95 conditions for Shade (1997) are 1916–83 

rainfall and 1986–95 landcover. n/a, not accounted for or not reported. Components may not balance because of rounding and direct recharge from cesspools and 

reservoirs. The source of water for direct recharge may be from external sources, including groundwater and imported streamflow. Some recharge may discharge 

to streams as base flow]

Recharge estimated in this study for the ‘Īao aquifer 

system (table 9) is greater than recharge estimated by Shade 

(1997). The rainfall and runoff estimates of Shade (1997) 

are similar to those of this study even though they are based 

on different input datasets and computational methods. Fog 

interception was not accounted for by Shade (1997). The 

difference in total ET estimates is about the same magnitude 

as fog interception. Direct recharge from reservoirs and cesspools was also not accounted for by Shade (1997). Recharge 

is about 5 percent less than that estimated by Shade (1997) if 

direct recharge is not considered.

Recharge estimates of this study for the ‘Īao and 

Waihe‘e aquifer systems (table 9) are less than those of 

Engott and Vana (2007). One reason for these differences is 

that runoff estimates from this study are greater than those of 

Engott and Vana (2007). In general, the mean runoff-to-rainfall ratios for catchment zones within these aquifer systems 

(figs. 1 and 6) are greater than those of Engott and Vana 

(2007, p. 21–23), especially in the wet, inland areas during 

the wet season. Additionally, runoff estimates from this study 

for the drainage basins of stream-gaging stations 16604500 

(on ‘Īao Stream) and 16614000 (on Waihe‘e River) (fig. 

6), are based on observed seasonal runoff-to-rainfall ratios 

(when available). In contrast, runoff estimates from Engott 

and Vana (2007) are based solely on mean monthly runoffto-rainfall ratios. 

Comparison with Evapotranspiration Estimates of 

Giambelluca and others (2014)

For the Island of Maui, mean annual total ET of the water 

budget for average climate conditions is about 1,173 Mgal/d, 

which is within 15 percent of the mean annual ET derived from 

Giambelluca and others (2014). Compared to ET estimates of 

Giambelluca and others (2014), the mean annual total ET estimates of the Maui water budget are (1) about 17 percent more 

for subareas mapped as grassland, (2) about 11 percent more 

for subareas mapped as shrubland, (3) about 2 percent more for 

subareas mapped as native forest, alien forest, or tree plantation, (4) about 7 percent less for subareas mapped as developed, 

and (5) about 10 percent less for subareas mapped as sparsely 

vegetated. ET estimates are also less than estimates of Giambelluca and others (2014) for subareas with fallow/grassland land 

cover. The similarity in ET estimates for forests was expected 

because forest crop coefficients and canopy evaporation were 

calibrated to ET datasets of Giambelluca and others (2014). 

The ET estimate of the water budget for subareas mapped as 

sugarcane is about 57 percent more than that of Giambelluca 

and others (2014). 

The differences in mean annual ET estimates may be 

related to differences in base periods, computational methods, and soil-moisture estimates. For example, in areas 

where ET estimates of the water budget are much greater 
Water-Budget and Groundwater-Recharge Estimates 45

than those of Giambelluca and others (2014)–including 

sugarcane fields– soil-moisture estimates of the water budget 

may be greater than those of Giambelluca and others (2014). 

On the other hand, in areas where ET estimates of the water 

budget are less than those of Giambelluca and others (2014)–

including abandoned agriculture fields mapped as fallow/

grassland– soil-moisture estimates of the water budget may 

be less. 

Recharge for Drought Conditions

Maui has experienced drought conditions in recent 

years. For example, based on the 1978–2007 monthly 

rainfall datasets (Frazier, 2012), annual rainfall for the 

island of Maui was below average 8 out of the last 10 years 

(1998–2007). To assess the impact of drought conditions on 

recharge, rainfall during 1998–2002 was selected to represent drought conditions on Maui because this period had 

the lowest 5-yr mean rainfall during 1978–2007. Other than 

rainfall, all water-budget inputs used to compute recharge for 

drought conditions were the same as those used for average 

climate conditions. Because the fog-to-rainfall ratios used for 

average climate conditions were also used for drought conditions, fog-interception rates for drought conditions were less 

than those for average climate conditions. 

For the Island of Maui, recharge for drought conditions, about 1,035 Mgal/d (table 7), is 23 percent less than 

recharge for average climate conditions. For Maui’s aquifer 

systems, water-budget components estimated for drought 

conditions are shown in table 8. Aquifer-system precipitation 

for drought conditions ranges from 12 to 37 percent less than 

corresponding precipitation for average climate conditions. 

Aquifer-system recharge for drought conditions is about 8 

to 51 percent less than corresponding recharge for average 

climate conditions. 

The spatial pattern of mean annual recharge for drought 

conditions on the Island of Maui (not shown) resembles that 

for average climate conditions. Recharge for most areas, 

however, is lower for drought conditions (fig. 12). Hence, 

for most areas on the Island of Maui, recharge estimates are 

sensitive to precipitation. Compared with recharge for average climate conditions, recharge decreases between 10 and 

50 percent for most upland areas on West Maui Mountain 

and windward Haleakalā; recharge decreases by more than 

50 percent in most unirrigated lowland areas on West Maui 

Mountain and on leeward Haleakalā. Recharge decreases 

by 10 percent or less for some irrigated areas, including the 

sugarcane fields in the isthmus. Subareas mapped as golf 

courses had slightly more recharge for drought conditions 

owing to relatively more sprinkler irrigation. In some areas, 

runoff may have been overestimated for drought conditions owing to use of mean seasonal runoff-to-rainfall ratios 

representative of 1978–2007. Overestimating runoff would 

result in underestimating recharge. On the other hand, the 

assumption of constant reference ET may have resulted in 

underestimating ET for drought conditions, which would 

result in overestimating recharge.

Sensitivity Analysis

Uncertainty exists in many of the water-budget inputs 

used in this study. The inputs used in the water-budget 

calculations were considered to be those most reasonable. To 

analyze the effect that uncertainty in water-budget inputs has 

on estimated recharge, the water budget was rerun, changing 

one input value at a time within a reasonable range. The 

parameters tested were (1) available water capacity, (2) foginterception rates, (3) root depth, (4) runoff-to-rainfall ratios, 

(5) crop coefficient, (6) canopy capacity, (7) ratio of mean 

evaporation rate to mean precipitation rate during saturated 

canopy conditions, and (8) the fate of excess water collected 

by storm-drain systems. Changes to trunk-storage capacity 

and the proportion of precipitation diverted to stemflow were 

also tested for subareas with forest land covers. For available 

water capacity, the low and high values published in Natural 

Resources Conservation Service (2006a) were tested. For 

fog-interception rates and root depths, the “baseline” values 

used in water-budget calculations for average climate 

and drought conditions were increased by 50 percent and 

decreased by 50 percent. Runoff-to-rainfall ratios were 

adjusted on the basis of bias and root-mean-square-error 

statistics (for Maui) of the region-regression models 

(table 5). For crop coefficients, baseline values were 

increased by 20 percent and decreased by 20 percent. 

Canopy-storage capacity values of 0.08 and 0.02 were 

tested. Values for V were increased by 0.10 units and were 

decreased by 0.10 units. For the latter case, however, V was 

assumed to be no less 0.01. Finally the effects of routing 

all water collected by storm-drain systems to runoff and to 

recharge were tested.

The recharge estimates for the sensitivity analysis were 

compared with recharge estimates for average climate conditions for Honokōwai, ‘Īao, Kahului, Kūhiwa, and Luala‘ilua 

aquifer systems and the Island of Maui (table 10). These 

five aquifer systems cover a range of climates on Maui. The 

Honokōwai and ‘Īao aquifer systems are on leeward and 

windward West Maui Mountain, respectively, and have steep 

rainfall gradients. The wet uplands of the Honokōwai aquifer 

system are mostly native forest; drier lowlands include alien 

forest, coffee, developed areas, and large areas of abandoned 

pineapple fields that are now fallow grasslands. Within the 

‘Īao aquifer system, uplands are wet, steep, and mostly forest 

and shrubland; lowlands are drier and are a mixture of alien 

forest, macadamia, grassland, and developed areas. The area 

within the Kahului aquifer system, on the isthmus, is very 

dry but has substantial irrigation for sugarcane. The area 

within the Kūhiwa aquifer system, on windward Haleakalā, 

is very wet and is mostly forested. The area within the 

Luala‘ilua aquifer system, on southwest Haleakalā, is dry 

and is mostly grassland and sparsely vegetated (table 2).
46 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i Difference in mean annual recharge for drought conditions, relative to that for average climate conditions, in percent

40

–10–25–50–75

–100

Boundaries of named aquifer systems (modified from State of Hawai‘i, 2008)

Base modified from U.S. Geological Survey National Hydrography Dataset, Universal Transverse Mercator projection, zone 4, NAD83 datum.

Pā‘ia

Kama‘ole

‘Īao

Ha‘ikū

Makawao

Kahului

Waikapū

Honokōwai

Honolua

Honopou

Launiupoko

Waihe‘e

Honokōhau

Ukumehame

Kahakuloa

Olowalu

Waikamoi

Ke‘anae

Luala‘ilua

Nakula Kaupō

Kīpahulu

Waiho‘i

Kawaipapa

Kūhiwa

156°00'

156°10'

156°20'

156°30'

156°40'

21°00'

20°50'

20°40'

0 3 6 9 KILOMETERS0 3 6 9 MILES

EXPLANATION

Figure 12. Percentage difference in mean annual groundwater recharge for drought conditions (1998–2002), relative to that for average climate conditions (1978–2007), Island of Maui, Hawai‘i calculated using the water-budget model.
Water-Budget and Groundwater-Recharge Estimates 47

Parameter Adjusted parameter value

Percentage difference in recharge 

relative to scenario for average climate conditions

for aquifer systems and the Island of Maui

Honokōwai ‘Īao Kahului Kūhiwa Luala‘ilua Island of 

Maui

Available water capacity Low reported value1 0.8 0.5 6.1 0.1 6.9 1.2

High reported value1 -0.7 -0.4 -3.1 -0.1 -3.9 -0.8

Fog-interception rates 150% of baseline 4.3 8.6 0.0 5.5 4.6 6.2

50% of baseline -4.3 -8.5 0.0 -5.5 -4.4 -6.2

Root depth 150% of baseline -2.2 -1.3 -7.7 -0.1 -6.8 -1.8

50% of baseline 4.5 2.8 28.8 0.3 14.8 4.6

Runoff-to-rainfall ratios Regression bias adjustment2 5.8 -1.4 0.2 -3.8 2.0 -0.5

Increase by regression 

RMSE3

-7.0 -8.1 -1.2 -21.6 -8.2 -12.9

Decrease by regression 

RMSE3

7.0 8.9 0.7 21.5 7.1 12.9

Crop coefficients 120% of baseline -3.3 -4.3 20.2 -2.8 -6.9 -3.1

80% of baseline 4.3 5.1 -15.4 3.0 9.0 4.2

Canopy capacity Increase by 0.03 unit -3.6 -3.9 0.0 -4.6 -0.4 -3.8

Decrease by 0.03 unit 4.2 4.5 0.0 4.9 0.4 4.2

Ratio of the mean 

evaporation rate to 

mean precipitation rate 

during saturated conditions, V

Increase by 0.1 unit -8.1 -11.9 -0.1 -17.6 -1.5 -12.4

Decrease by 0.1 unit 6.2 5.5 0.1 1.4 1.6 4.6

Fate of excess water collected by storm-drain 

systems

All collected water goes to 

runoff

-0.3 -0.3 -0.2 0.0 0.0 -0.1

All collected water goes to 

recharge

0.3 1.6 4.5 0.0 0.0 0.2

Table 10. Results of sensitivity testing for selected water-budget parameters performed for selected aquifer systems and the Island of Maui, 

Hawai‘i.

[See fig.1 for locations of aquifer systems; baseline parameters are those used for average climate conditions; RMSE, prediction root mean square error of 

regional-runoff regression; see table 5 for RMSE and bias for Maui; %, percent]

1

Low and high values reported in Natural Resources Conservation Service (2006a).

2

For leeward areas, runoff-to-rainfall ratios were decreased by 27.6 and 6.5 percent for the wet and dry seasons, respectively. For windward areas, runoff-torainfall ratios were increased by 3.9 and 8.4 percent for the wet and dry seasons, respectively.

3

For leeward areas, runoff-to-rainfall ratios were adjusted by 0.05 and 0.04 unit for the wet and dry seasons, respectively. For windward areas, runoff-to-rainfall ratios were adjusted by 0.11 and 0.10 unit for the wet and dry seasons, respectively.
48 Spatially Distributed Groundwater Recharge Estimated Using a Water-Budget Model for the Island of Maui, Hawai‘i

Adjusting the fate of water collected by storm-drain 

systems had a minor effect on the recharge estimates for the 

five aquifer systems examined in this analysis (table 10). If all 

water collected by storm-drain systems is assumed to be runoff, 

then aquifer-system recharge decreases by less than 1 percent. 

If all water collected by storm-drain systems is assumed to be 

recharge, then aquifer-system recharge increases by less than 

5 percent. Although considerable areas are developed in the 

‘Īao and Honokōwai aquifer systems, these areas typically have 

much less rainfall than the forested areas farther inland. Similarly, for the Kahului aquifer system, rainfall for the developed 

land covers is much less than the sum of irrigation and direct 

recharge from reservoirs. Adjustments to canopy capacity 

changes recharge by less than 5 percent for each aquifer system 

examined. 

Recharge estimates for windward-facing aquifer systems 

(‘Īao and Kūhiwa) were most sensitive to adjustments to foginterception rates, runoff-to-rainfall ratios, and V. This indicates 

the importance of fog, runoff, and canopy evaporation in the 

water budget for wet, forested areas. Adjustments to the remaining parameters tested affected recharge by no more than 

6 percent. Recharge estimates for the Honokōwai aquifer 

system were also sensitive to runoff-to-rainfall ratios, and V. 

Adjustments to fog-interception rates, however, had less impact 

on recharge for this leeward-facing aquifer system relative to 

the windward-facing aquifer systems.

Recharge estimates for the dry aquifer systems on leeward Haleakalā (Kahului and Luala‘ilua) were more sensitive 

to available water capacity, root depth, and crop coefficients 

than the wetter aquifer systems examined. This indicates that 

moisture-storage capacity and ET, which are related to these 

three parameters, are important water-budget components in dry 

areas. Adjustments to fog-interception rates also had a noticeable effect on recharge for the Luala‘ilua aquifer system, which 

has patches of forest and shrubland in the cloud zone. Unlike 

recharge for the other aquifer systems examined, recharge for 

the Kahului aquifer system (1) was not sensitive to adjustments 

in runoff-to-rainfall ratios, and (2) increased when crop coefficients were increased. The reason for the latter difference is 

related to the chief source of recharge. Irrigation is the chief 

source of recharge for the Kahului aquifer system, whereas 

precipitation is the chief source of recharge in most unirrigated 

areas on Maui. In the water budget, increasing the crop coefficients for irrigated land covers increases potential ET and 

irrigation rates (see equation 16), which results in increased 

recharge rates. 

For areas mapped as forests (native, alien, and tree 

plantation), recharge changes by 5 percent or less in response 

to selected adjustments to the baseline values used for trunkstorage capacity (0.01 inch), and the proportion of precipitation diverted to stemflow (0.04). Recharge for forested areas 

decreases by about 5 percent when the model uses a relatively 

high trunk-storage capacity value of 0.14 inch, the storage 

capacity determined by DeLay (2005) for epiphytes in a native 

cloud forest on the Island of Hawai‘i. Recharge for forested 

areas increases by about 2 percent when the model uses a 

relatively low trunk-storage capacity value of 0.001 inch and 

decreases by 0.2 percent when the model uses a relatively 

high stemflow value of 0.10. For areas mapped as alien forest, 

recharge decreases by 0.4 percent when the model uses a stemflow value of 0.37, determined by Safeeq and Fares (2014) for a 

site with an abundance of Psidium cattleianum. 

Suggestions for Future Study and 

Additional Data Collection

The results of this study indicate that precipitation is a critical dataset for estimating recharge. Future studies that estimate 

recharge would benefit from additional sites where precipitation 

is measured. A large part of Maui is in the cloud zone and thus 

has the potential to intercept fog moisture. Recharge estimates 

for areas in the cloud zone are sensitive to fog-interception 

estimates. Fog and recharge estimates of this study could be 

improved with a better understanding of the spatial and daily 

variability of fog on Maui and fog-interception rates for different types of vegetation. 

Streamflow data from stream-gaging stations is essential for determining direct runoff and base flow. Additionally, 

recharge estimates for most areas on Maui are sensitive to 

runoff-to-rainfall ratios. More stream-gaging stations on Maui 

would improve runoff and recharge estimates.

Irrigation is substantial on Maui, particularly for sugarcane 

on the isthmus. Records of water availability can be used to 

constrain irrigation rates of the water budget. Records of water 

applied to irrigated areas would help with replicating actual 

irrigation practices on Maui.

The gridded maps of ET-related parameters produced by 

Giambelluca and others (2014) were a valuable resource for 

estimating ET in the water budget of this study, although uncertainty in these parameters will result in uncertainty in recharge 

estimates. Recharge estimates could be further improved with 

estimates of the interannual and daily variations of reference ET 

across Maui. For example, ET measurements for different types 

of the dominant alien and native plants could be used to develop 

crop coefficients for land-cover types that are more specific than 

the general land-cover classes used here. Also helpful would be 

spatial estimates of V, which is the ratio of the mean evaporation rate to the mean precipitation rate during saturated canopy 

conditions. Additional canopy evaporation measurements on 

Maui could be used to confirm or calibrate the canopy evaporation estimates of the water-budget model. 

Summary and Conclusions

The main objective of this study was to determine the 

spatial distribution of mean annual groundwater recharge for the 

Island of Maui. Recharge was computed with a water-budget 

model that used a daily computational interval. Hydrological 
References Cited 49

processes and physical conditions that affect recharge on Maui 

were simulated in the water-budget model using the most current datasets available– including maps of 2010 land cover, 

1978–2007 monthly rainfall, and mean monthly reference 

evapotranspiration. The model disaggregated monthly rainfall 

into daily rainfall using the method of fragments and daily rainfall records from 52 rain gages. The water-budget model and the 

most current datasets available were used to estimate the spatial 

distribution of mean annual groundwater recharge for two 

scenarios: (1) average climate conditions, based on 2010 land 

cover and rainfall during 1978–2007 and (2) drought conditions, 

based on 2010 land cover and rainfall during 1998–2002.

For the Island of Maui, mean annual recharge is about 

1,340 Mgal/d for average climate conditions. Recharge is about 

45 percent of precipitation (sum of rainfall and fog interception) 

and is about 40 percent of total inflow (sum of precipitation, 

irrigation, septic-system leachate, and direct recharge). 

The spatial pattern of mean annual recharge on Maui for 

average climate conditions resembles that of mean annual rainfall, but it also reflects spatial variations in vegetation and soils, 

irrigation in agricultural and developed areas, and persistent 

cloud layers that envelop upland areas. Wet, upland parts of 

the island typically have more recharge than dry, coastal areas. 

Areas with the highest recharge include the mid-altitude slopes 

of windward Haleakalā and the uplands of West Maui Mountain. In addition to having high rainfall, these locations have 

considerable fog interception and low reference ET owing to 

persistent cloud cover. In general, areas that have water inflows 

from irrigation, septic-system leaching, and direct recharge have 

more total recharge than nearby areas without these supplemental water inflows. Of Maui’s total mean recharge for average 

climate conditions, about 60 percent occurs in the Ko‘olau and 

Hāna aquifer sectors on windward Haleakalā. For Maui’s 25 

aquifer systems, mean annual recharge ranges from about 13 

to 222 Mgal/d for average climate conditions. Compared to the 

aquifer-system recharge estimates of the 1990 State of Hawai‘i 

Water Resources Protection Plan, recharge estimates from this 

study are (1) lower for the Honopou, Waikamoi, and Kaupō 

aquifer systems, and (2) higher for the remaining 22 aquifer 

systems on Maui. Compared to ET estimates of Giambelluca 

and others (2014), ET estimates of this study are (1) within 17 

percent for areas mapped as forest, grassland, developed, shrubland, and sparsely vegetated and (2) about 57 percent greater 

for areas mapped as sugarcane. 

Mean annual recharge on Maui for drought conditions, 

about 1,035 Mgal/d, is 23 percent less than recharge for average 

climate conditions. Aquifer-system precipitation for drought 

conditions ranges from 12 to 37 percent less than corresponding precipitation for average climate conditions. Aquifer-system 

recharge for drought conditions ranges from 8 to 51 percent 

less than corresponding recharge for average climate conditions. Compared with recharge for average climate conditions, 

recharge decreases between 10 and 50 percent for most upland 

areas on West Maui Mountain and on windward Haleakalā. 

Recharge decreases by more than 50 percent in most lowland 

areas on West Maui Mountain and on leeward Haleakalā. 

Recharge decreases by 10 percent or less for some irrigated 

areas, including the sugarcane fields on the isthmus.

The spatial distribution of rainfall is the primary factor 

determining spatially distributed recharge estimates for most 

areas on Maui. Recharge estimates of the Maui water-budget 

model are also sensitive to runoff-to-rainfall ratios except in 

areas such as the isthmus, where irrigation exceeds precipitation. For wet areas, recharge estimates are also sensitive to 

fog-interception rates and forest-canopy evaporation. Recharge 

estimates in drier areas are more sensitive to irrigated crop areas 

and parameters that are related to ET: available water capacity, 

root depth, and crop coefficient. The assumption that all water 

collected by storm-drain systems in urban areas becomes either 

runoff or recharge has a minor effect on regional-scale recharge. 

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