FOR ONLINE PUBLICATION ONLY
Appendix 1: Surface Water and Forest Quality
Here we establish the relationship between surface runoff and forest plantings. This is important
to demonstrate that the plantings met their conservation purpose and to establish a timeline of
their effectiveness. This also clarifies the connections between groundwater recharge and forest
cover.
Table A1.1 shows the estimated level of stream flow for three major streams supplying surface
water on O`ahu over the 20th century as a function of precipitation and lagged replanting. We
choose a lag of 25 years to reflect the growth rates of the dominant species replanted (Eucalyptus
species, averaging 22 years to maximum mean annual increment (MMAI) (Delgado-Matas and
Pukkala 2011, and Acacia species, 30 years to MMAI (Elevitch and others 2006).
Figure A1.1 maps the plantations and long run monitored streams1.
Unsurprisingly, early
monitoring of the streams coincided with planting strategy. Figure A1.2 graphs the deviations
from means for precipitation and stream flow over time. In Figure A1.2, one can see that,
although stream flow patterns mimic rainfall closely throughout the century, at the beginning of
the time period, the fluctuations in stream flow follow the rainfall pattern much more closely
than they do starting in the 1950s. This is when the considerable planting efforts of the 1920s
(shown in Table 1) began to show their effects.
The regression results in Table A1.1 indicate that the grown plantings capture and regulate the
surface runoff, in that they show a negative, significant relationship between precipitation and
stream discharge, as expected. Thus the replanting conservation effort worked to reduce and
even out stream flow, with the strongest effect on the Nu`uanu valley. We can then say that
replanting regulates stream flow (Figure A1.2) and increases recharge and/or evapo-transpiration
at the expense of runoff (Table A1.1). Planters investing in reforestation had approximately a 30
year wait for the greatest of the surface water returns on their investment; this investment would
1
Hillshading (shadowing to highlight the varied terrain of the land) provides the visual 3-D relief.
not have been undertaken without the concentration of land ownership and unity of goals held by
the sugar industrialists.
Appendix 2: Forest Quality and Water Balance
Table A2.1 shows means and standard errors for data by the individual spatial cells on O`ahu
grouped by land use classification. Thus, for example, there are 8,000 cells with that fall into
“evergreen forest” land cover, which receive on average 2,550 mm/y of precipitation (with s.d.
1417), 1,054 mm/y of which are recharge and 1,496 of which are runoff + Evapotranspiration.
These characteristics affecting runoff and recharge vary significantly by characteristics such as
elevation, slope, and soil quality and this requires incorporation into the model. The final row
shows the raw percentages of runoff plus evapotranspiration for each land use. OLS estimates of
how these characteristics affect runoff (and therefore in reflection, recharge) are presented in
Table A2.2 (both tables from Kaiser and others 20082). In specification I (column I, Table A2.2)
coefficients other than the constant do not vary with land type (all observations across land types
are included); specifications II-V generate separate coefficients for each main land type as in a
fully interacted model. These tables can then be used to determine what recharge should become
under changes in land use.
Under specification I, we see that compared to (omitted) evergreen forest, scrub/shrub appears to
have slightly lower runoff and evapotranspiration (-30 mm/y), whereas cultivated land, low
intensity developed, and high intensity developed have increasingly higher runoff respectively.
These results are similar to the raw percentages of runoff plus evapo-transpiration (Table A2.1,
final row) with the exception of scrub/shrub, which has a slightly higher runoff percentage (62%)
than evergreen forest (59%). This reversal potentially highlights one of the foibles of early
conservation efforts: the choice of species for planting was not made so much for its long run
2
Kaiser and others (2008) calculates the effect of watershed conservation on near-shore resources. We now use
portions of this data to investigate the current status of historical investments. We estimate effects on runoff +
evapotranspiration (Table A2.2) rather than recharge directly because recharge is estimated as a residual already.
recharge capabilities but for quick soil cover and stabilization, and the returns are not as high as
they would have been with different planting incentives and/or greater scientific knowledge.
We calculate how runoff plus evapotranspiration (ET) would change if the existing land use/land
cover changed to another land use/land cover. Figures reported in Table A2.3 (from Kaiser and
others 2008) are determined from the coefficients presented in Table A2.2 evaluated at the
means of the other land use (Table A2.1). Means for each land cover category are used to
generate estimated runoff/ET using coefficients from each regression (II-V), where parameters
are allowed to vary. Columns use the characteristics of each land use/ land cover in conjunction
with the regression estimates for another land use/ land cover (rows), illustrating what runoff
would be if the land use/land cover were to change.
Table A1.1: Stream Discharge as a Function of Precipitation and Replanting Efforts
Stream
Precipitation
25 y lagged
constant
N. obs.
R2-Adj
93
0.27
79
0.16
84
0.10
plantings (1000s,
(start date)
cumulative)
Kahili
0.107
-0.258
5.108
(1915-2007)
p=0.000
p=0.000
p=0.000
Nuuanu
0.156
-2.36
5.27
p=0.001
p=0.018
P=0.000
0.133
-0.0025
13.95
p=0.003
p=0.192
p=0.000
(1915-1995)
Wahiawa
(1914-2007,
with breaks)
Notes: OLS regression. Observations are annual following years under stream name. p-values
under parameter estimates. The Nuuanu parameter on lagged plantings is a dummy variable that
is equal to 0 until 1936 and 1 afterward, signaling the lag for an unknown but significant level of
planting that occured in 1911. As no other major plantings occur in the valley in other years, the
dummy variable captures the potential structural effects of the 1911 plantings on stream flow.
Table A2.1: Land Use Classification and Water Balance
Evergreen
Scrub/Shrub Low
Forest
Intensity
High
Cultivated
Intensity
Land
Developed Developed
Rain
2550
1913
1066
864
1041
(1417)
(1353)
(440)
(293)
(284)
907
973
266
159
536
(565)
(668)
(278)
(175)
(342)
26.5
36.1
7.1
3.4
6.9
(22)
(25)
(12)
(9)
(9)
0.01
0
0.06
0.04
0
(0.1)
(0)
(0.23)
(0.20)
(0)
0.57
0.47
0.48
0.31
0.83
(0.49)
(0.50)
(0.50)
(0.46)
(0.38)
0.42
0.52
0.34
0.47
0.09
(0.49)
(0.50)
(0.47)
(0.50)
(0.29)
Pineapple
0
0
0.04
0.01
0.34
(portion)
(0)
(0)
(0.19)
(0.11)
(0.47)
Recharge
1054
734
243
104
312
(mm/yr)
Elevation
(ft)
Slope
(degrees)
Class A
Soils
(portion)
Class B
Soils
(portion)
Class D
Soils
(portion)
(mm/yr)
(857)
(807)
(282)
(191)
(289)
Proximity
2833
2242
421
296
708
(2001)
(1717)
(566)
(396)
(545)
3249
4889
6154
5925
8512
(2575)
(4445)
(4074)
262
619
970
1256
700
(474)
(1078)
(1347)
(1388)
(744)
366
381
419
438
423
(52)
(51)
(47)
(43)
(30)
1496
1179
823
760
729
(675)
(669)
(355)
(232)
(399)
N. Obs.
8000
15924
4013
2094
2488
Runoff +
59%
62%
77%
92%
70%
to Road
(m)
Proximity
to trail (m)
Proximity
to stream
(m)
Solar
radiation
(cal)
Runoff +
ET
(mm/yr)
ET as % of
rain
(3593)
(3047)
Table A2.2: Regression of Runoff + Evapotranspiration on Land Characteristics
Variable
Elevation
I (all)
II
III (Scrub/
IV (low
V (high
VI
(evergreen shrub)
intensity
intensity
(cultivated
forest)
developed
developed
land)
)
)
0.15*
0.44*
0.04*
0.07*
0.09*
0.31*
(0.006)
(.01)
(0.008)
(0.02)
(0.03)
(0.03)
-3.00*
-2.44*
-2.63*
-1.84*
-1.38*
-2.7*
(0.11)
(0.20)
(0.14)
(0.38)
(0.47)
(0.60)
Solar Rad -3.03*
-2.84*
-3.59*
-1.50*
-0.79*
-2.12*
(0.06)
(0.11)
(0.09)
(0.14)
(0.16)
(0.23)
125*
241*
83*
39*
-4.2
195*
(4.6)
(9.4)
(6.7)
(9.4)
(7.4)
(19)
269.5*
75.0*
293.0*
-9.7
-69
-46
(6.9)
(11.7)
(10.8)
(27)
(44)
(136)
20.8
43
73.5*
-210.7*
-393.8*
28.2*
(12.9)
(64)
(34.2)
(22)
(32.8)
(17.1)
0.09*
0.05*
0.12*
0.11*
0.06*
-0.08*
(0.002)
(0.003)
(0.003)
(0.008)
(0.009)
(0.01)
-0.01*
-0.03*
-0.006*
0.001
0.00
0.001
(0.001)
(0.002)
(0.0009)
(0.002)
(0.00)
(0.003)
Slope
D Soils
Ko`olau
Pineapple
Road
Trail
Stream
Ever-
-0.01*
-0.01
-0.02*
-0.02*
-0.02*
0.13*
(0.002)
(0.009)
(0.003)
(0.004)
(0.004)
(0.008)
--
green
Scrub/
-30*
shrub
(5.5)
Low dev
108*
(8.6)
High dev
128*
(11)
Cultivate
d
23*
(10)
Fog
127.0*
560.2*
54.8*
drip
(12.03)
(26.4)
(14.7)
East (by
2.44*
4.98*
2.30*
2.81*
2.77*
3.81*
(0.05)
(0.16)
(0.07)
(0.14)
(0.19)
(0.20)
-2.58*
-2.85*
-2.56*
-3.29*
-3.43*
-7.67*
(0.06)
(0.12)
(0.08)
(0.15)
(0.22)
(0.19)
1974.0*
1445.2*
2210.0*
1471.7*
1233.0*
1886.2*
(28.6)
(65.9)
(42.6)
(71.4)
(87.0)
(114)
.68
0.70
0.67
0.49
0.56
0.57
cell)
South (by
cell)
Const
R sq. adj.
N. Obs.
32610
8000
15924
4013
2094
2488
Percent
100
24.5
48.8
12.3
6.4
7.6
tot
Original Regressions from Kaiser and others (2008). Data sources, Original map year (and last
included digital update year if different) are respectively: State of Hawaii Office of Planning
(Elevation & Slope, 1997; Important Agricultural lands, 2007; Roads, 2008; Trails, 2000, 2002;
Streams, 1983, 2005; Land Cover, 2005), NRCS (Soils, 1972, 1997).
Table A2.3: Predicted Runoff + Evapo-Transpiration from Land Cover Change
coeff:
Predictions (at means)
low intensity
high intensity
evergreen
scrub/shrub
developed
developed
evergreen
62.6
64.3
76.8
82.7
scrub/shrub
65.4
68.1
80.6
86.4
66.8
59.8
79.4
86.3
59.4
56.8
80.5
89.9
low intensity
developed
high intensity
developed
Figure A1.1: Replantings and long run monitored streams
Figure A1.2: Stream flow and precipitation co-variation