tr27 cover2...title tr27 cover2.p65 author user created date 191030529115831

Research Disciplines: Ecology ~ Geology ~ Geomorphology ~ Hydrology ~ Pedology ~ Silviculture Systems ~ Wildlife

Using Combined Snowpack andRainfall Interception Components to

Assess Hydrologic Recovery of aTimber-Harvested Site:

Working Toward an Operational Method

ByRobert Hudson

Research Hydrologist

TR-027 Hydrology March 2003

Technical ReportForest Research

Vancouver Forest Region2100 Labieux Road, Nanaimo, BC, Canada, V9T 6E9, 250-751-7001

Robert Hudson, P.Geo., Ph.D.

Research HydrologistVancouver Forest RegionBC Ministry of Forests2100 Labieux RoadNanaimo, British Columbia V9T 6E9250-751-7001

[email protected]

Hudson, R.O. 2003. Using Combined Snowpack and Rainfall Interception Components to AssessHydrologic Recovery of a Timber-Harvested Site: Working Toward an Operational Method.Research Section, Vancouver Forest Region, BC Ministry of Forests. Nanaimo, BC. TechnicalReport TR-027. 38 pp.

http://www.for.gov.bc.ca/vancouvr/research/research_index.htm

Cover photo : North aspect slope in the Gray Creek study area.

Technical Report TR-027 March 2003 Research Section, Vancouver Forest Region, BCMOF

1

Research Disciplines: Ecology ~ Geology ~ Geomorphology ~ Hydrology ~ Pedology ~ Silviculture ~ Wildlife

CONTENTS

ABSTRACT .......................................................................................................................................................................................... 3KEYWORDS ....................................................................................................................................................................................... 3ACKNOWLEDGEMENTS ................................................................................................................................................................ 3

INTRODUCTION ............................................................................................................................................................................... 5

METHODS ........................................................................................................................................................................................... 5Data Collection ............................................................................................................................................................................. 5Data Analysis ................................................................................................................................................................................ 6

RESULTS ........................................................................................................................................................................................... 10Data Treatment ........................................................................................................................................................................... 10Development of Recovery Curves ............................................................................................................................................. 15Summary of the Approach ......................................................................................................................................................... 22Alternative Approach ................................................................................................................................................................. 23Determination of Hydrologic Recovery for Rain-on-Snow .................................................................................................... 23Implementation ........................................................................................................................................................................... 24

DISCUSSION ..................................................................................................................................................................................... 25

LIMITATIONS .................................................................................................................................................................................. 27

CONCLUSION .................................................................................................................................................................................. 28

REFERENCES .................................................................................................................................................................................. 28

APPENDIX A AUTHOR’S NOTES: IMPLEMENTATION OF HYDROLOGICAL RECOVERY METHODS ................ 30

APPENDIX B NORMALIZING INTERCEPTION DATA ........................................................................................................ 32

2



TABLESTable 1. Rainfall interception and stand variables: summary. ............................................................................................................... 7Table 2a. Storm rainfall, throughfall, and stemflow (mm) at Gray Creek. ............................................................................................ 8Table 2b. Interception losses calculated for storms in Table 2a, and average values. ........................................................................... 9Table 2c. Calculation of mean rainfall recovery using Method 2: summary. ........................................................................................ 9Table 3. Interception under mature Douglas-fir, 1951-53: Nanaimo River, and other sites. ............................................................... 12Table 4a. Mean recovery for all storms at or above a given threshold: the effects of varying the storm size threshold

on rainfall recovery at Gray Creek. .............................................................................................................................................. 15Table 4b. Comparison of recovery data calculated in three different ways. ........................................................................................ 15Table 5a. Rainfall interception data and associated stand data: summary. .......................................................................................... 17Table 5b. Rainfall interception recovery: effects of season and choice of reference stand on rainfall interception recovery. ............ 18Table 6. Coefficients of recovery curves for Equation 9, and some site factors that might account for the different curves. ............. 19

Table B1. Long-term mean precipitation data, by season for three Vancouver Island sites: summary. ............................................... 34Table B2. Seasonal rainfall at Campbell River, for specific time periods. .......................................................................................... 34Table B3. Determination of p’ as a function of p

R using measured interception data at Gray Creek and at

Campbell River, by the Direct Method. ........................................................................................................................................ 34Table B4. Determination of p as a function of p

R using measured interception data at Gray Creek and at

Campbell River, by the Working Method. .................................................................................................................................... 35Table B5. McMinn data as reported and after seasonal adjustment: reported data along with adjusted interception volumes. ......... 35Table B6. McMinn data as reported and after seasonal adjustment: adjusted interception calculated by the

Working Method and the Direct Method. ..................................................................................................................................... 35

FIGURESFigure 1. Map of Vancouver Island and the Sunshine Coast showing locations of the study sites,

and an air photo mosaic of the study area at Gray Creek with a contour map overlay. .................................................................. 6Figure 2. Relationships between interception loss and storm size for mature and immature forest stands at upper Gray Creek. ....... 10Figure 3. Relationships between stemflow and storm rainfall for mature and immature forest stands at upper Gray Creek. ............. 10Figure 4. Statistical distribution of recovery data for individual storms at Gray Creek. ..................................................................... 11Figure 5. Summary of measured interception loss under old growth Douglas-fir stands. ................................................................... 13Figure 6. Rainfall interception recovery as a function of stand height and canopy density. ................................................................ 14Figure 7. Adjustment of stand height to compensate for canopy density outliers. .............................................................................. 14Figure 8. The effects of varying the storm size threshold on recovery of immature stands, and the

effects of using second growth as a reference stand. .................................................................................................................... 16Figure 9. Mean interception as a function of storm size for three stands at Gray Creek. .................................................................... 16Figure 10. Seasonal recovery components at Gray Creek. .................................................................................................................. 20Figure 11. Two possible solutions for a recovery curve based on the same data set. .......................................................................... 20Figure 12. Results of varying the reference stand for Douglas-fir and its effect on the recovery curve. ............................................. 21Figure 13. Effects of reference stand criteria on the recovery curve. .................................................................................................. 21Figure 14. A set of recovery curves that together form the basis of a method to determine full hydrologic recovery,

under a range of management scenarios. ...................................................................................................................................... 22Figure 15. Alternative rainfall interception recovery curves based on lumping together all rainfall recovery data,

with upper and lower bounding curves. ........................................................................................................................................ 23Figure 16. The components of streamflow that occurred as a result of snowmelt and rain-on-snow events,

as determined by the UBC watershed model. ............................................................................................................................... 24Figure 17. The proportion of snowmelt in daily runoff during rain-on-snow at upper Gray Creek

is a function of mean daily rainfall. .............................................................................................................................................. 24

Figure A1. Spring and rain-on-snow melt recovery, with separate and combined curve fits. ............................................................. 31Figure A2. Combined spring and rain-on-snow melt recovery curve based on averaged recovery data. ............................................ 31Figure B1. Relationships to convert seasonal rainfall ratio and to derive interception ratios from rainfall ratios. ............................. 36Figure B2. Relationships between interception under mature Douglas-fir and stand height for Nanaimo River. ............................... 36


3


ABSTRACT

This report describes the second of several steps beingtaken toward developing a science-based, comprehen-sive, integrated approach for assessing full hydrologicrecovery of timber-harvested sites in coastal British Co-lumbia watersheds. It builds on an earlier report, BritishColumbia Ministry of Forests� Assessing Snowpack Recov-er y of Watersheds in the Vancouver Forest Region, thatdescribes a regional approach for applying the snow-melt recovery method.

The results of a rainfall interception study conducted onthe mainland coast of British Columbia are describedand then combined with results from other rainfall inter-ception studies to form rainfall interception recoverycurves. These curves are combined with a previouslyreported snowmelt recovery curve to form a unifiedmethod of determining hydrologic recovery due to snow-melt, rain-on-snow, and rainfall.

KEYWORDS

forestry, forest harvesting, forest management, hydrol-ogy, hydrologic recovery, peak flow, rainfall intercep-tion, clearcuts, snowpack, snowpack recovery, snowmelt,watershed, watershed assessment, Vancouver Forest Re-gion, British Columbia

ACKNOWLEDGEMENTS

This report relied heavily on data collected and pub-lished by Dave Spittlehouse (Research Climatologist, Ecol-ogy and Earth Sciences Section, Research Branch, BCMinistry of Forests), and so Dave�s input on the interpre-tation of his data was an essential part of the data analy-sis and writing of the report. Review comments weregratefully received from Glynnis Horel, (OstapowichEngineering), Warren Cooper (WLAP) and Don Dobson(Dobson Engineering Ltd.). I would also like to thankBob Willington and Shelley Higman, for their support.The original snow hydrology research that formed thebasis of the snowpack recovery methods was funded byMinistry of Forests Operations Division base funds. TheGray Creek rainfall interception data collection was notspecifically funded, but was carried out on funds bor-rowed from various sources with equipment that we had�on hand�, with assistance from John Fraser.


5


INTRODUCTION

Full hydrologic recovery of a site to pre-timber-harvestingconditions is based on three components: snowpack re-covery, rainfall interception recovery and evapotranspira-tion recovery. Snowpack recovery has been well docu-mented for coastal sites in British Columbia (Hudson 2000a,b), based on changes in snow interception and melt ratethat occur when forests are harvested and subsequentlyregenerate. The evapotranspiration component is relativelyminor as it applies primarily to summer low flows. Thus, apractical method of assessing hydrologic recovery shouldconsist of both snowpack and rainfall recovery compo-nents. At lower elevations (<300 m) the rainfall compo-nent is dominant, and in the 300�800-m elevation band,also known as the rain-on-snow (ROS) zone, runoff isgenerated by both rainfall and snowmelt in proportionsthat vary according to storm size (Hudson 2000b).

Previous studies of rainfall interception were conductedby Spittlehouse and Black (1981), and by Giles (1983).In the former, study areas were set up near Courtenay(Figure 1). Giles� study plots were laid out on a hillsidetransect at Cowichan Lake, from valley bottom to ridgetop, 72 years after fire destroyed the original forest. Inthese studies, the interception losses were calculated overthe course of one year. Spittlehouse�s and Giles� data formature forest suggest that potential interception is about25�35%. More recently, Spittlehouse (1998) has studiedrainfall interception under immature as well as maturecanopies at Carnation Creek (on the west coast of Van-couver Island, near Tofino) and near Campbell River (40km north of Courtenay), thereby providing the basis forassessing rainfall interception recovery. Hudson (2000b)has described the development and application of amethod to determine snowmelt recovery.

This technical report discusses a rainfall interception studyconducted at Upper Gray Creek (on the Sunshine Coast,~40 km northwest of Vancouver). Then, the results ofthis and other rainfall interception studies will be com-bined to form rainfall interception recovery curves. Thesecurves will be combined with the previously reportedsnowmelt recovery curve to form a unified method ofdetermining hydrologic recovery due to snowmelt, rain-on-snow, and rainfall.

METHODS

Data CollectionThe site of the rainfall interception study being reportedhere is Upper Gray Creek. This is the same site that wasused to develop the snowmelt recovery curves, and hasbeen described in detail by Hudson (2000a, b). To studysnow accumulation and melt under different regenerat-ing stands, ten snow courses were established. The cur-rent study used plots within four of those snow coursesto study rainfall interception using the methods described

by Spittlehouse (1998). Data were combined withSpittlehouse�s data to form a rainfall interception recov-ery curve for lower and upper elevations.

The study area at Upper Gray Creek was progressively har-vested between the late 1950s and the mid-1980s, leaving apatchy forest cover and stands of various ages (Figure 1).

The species mix is dominated by subalpine fir (Abieslasiocarpa (Hook.) Nutt.) throughout the study area, withwestern hemlock (Tsuga heterophylla (Raf.) Sarg.) andwestern redcedar (Thuja plicata Donn) at lower eleva-tions, merging to mountain hemlock (Tsuga mertensiana(Bong.) Carr.) and yellow-cedar (Chamaec yparisnootkatensis (D. Don) Spach) at higher elevations. West-ern white pine (Pinus monticola) also occurs sporadi-cally, either as individual trees or in clumps.

Rainfall interception was measured in 0.01-ha plots inwhich throughfall and stemflow were measured directly.One study plot was established in an old-growth stand (atSite F), and three study plots that represented regenerat-ing stands of different ages and stand structures (at SitesC, D, and E) (Table 1). Rainfall and total precipitation aremeasured at two meteorological sites in the study area.

Following the methods described by Spittlehouse (1998),throughfall was measured with a series of troughs thatwere laid out under the forest canopy. They fed into acentral drain system that channeled the water into a tip-ping bucket calibrated to tip after receiving 750 ml ofwater. The area of troughs at each plot was about 1.8 m2.Stemflow was collected with a garden hose, cut in halflengthwise and cemented around a tree in a helix pat-tern, and measured by feeding the hose into a standardtipping bucket rain gauge. To date, rainfall interceptionhas been measured for 26 storms between October 2000and November 2001 (Table 2). Fall measurements werecollected until the end of the storm on 01 December2000, after which snow and ice conditions in the troughsand tipping buckets prevented further monitoring. Mea-surement resumed after the system was reactivated inMay 2001.

This monitoring was intended to supplement data col-lected by Spittlehouse in order to determine if intercep-tion characteristics were noticeably different betweenhigh-elevation and low-elevation stands. While the trougharea used to measure throughfall was comparable to thatused by Spittlehouse, it was not possible to measure thecatch of each trough separately. Thus the variability be-tween troughs could not be assessed.

Also, there was only one measurement of stemflow ateach site, whereas Spittlehouse collected five measure-ments per study plot. These facts indicate that the datapresented here are potentially less precise than data pre-sented by Spittlehouse; however, it is felt that the dataare sufficiently accurate for this study.

6



Data Analysis

Throughfall was calculated by dividing the volume ofwater collected in the trough system by the area of thetroughs, and expressed in millimetres. Stemflow was cal-culated for the study plot by dividing the volume of flowmeasured for individual trees by the area of the plot andmultiplied by the number of trees in the plot. Becauserainfall measurements are collected in the open, they arenot affected by canopy interception. Therefore, intercep-tion loss at the forested sites for a given storm is calcu-lated as the storm rainfall in the open, minus the total ofthe throughfall and stemflow for the plot. Interceptionloss is expressed both in terms of millimetres of rainfallintercepted by the canopy, and as a percentage of stormrainfall (Table 2b). Mathematically, the interception canbe formulated as follows:

Figure 1. Above: Map of Vancouver Island and the Sun-shine Coast showing locations of the study sites.Left: An air photo mosaic of the study area at Gray Creekwith a contour map overlay. The patchiness of the regen-eration provided a range of stands of different ages andheights while the small area of the watershed (100 ha)helped to control the climatic variability.


7


STPPI −−= (1)

where,

I, PP, T, and S are interception, rainfall, throughfall,and stemflow, all in millimetres.

Expressed as a percentage,

100(%) ∗−−=PP

STPPI (2)

According to the concept of hydrologic recovery, a har-vested site is considered fully recovered when its poten-tial influence on runoff becomes indistinguishable fromthat of a mature reference stand that represents the hy-drologic characteristics of the stand prior to harvesting.To calculate rainfall interception recovery for an imma-ture stand, the interception loss of that stand is dividedby the interception loss of the reference stand:

100∗=Ref

dStanStand I

IR (3)

where,R

Stand and I

Stand are the recovery and interception ratios

of the stand in question (expressed as percentages),andI

Ref is the interception for the reference stand.

Hence, recovery can be calculated over any time period,from an individual storm to a season or a year.

For each of 26 storms measured between October 2000and November 2001, the total rainfall at each meteoro-logical site�along with the total throughfall and stemflowat Sites C, D, E, and F�were calculated (Table 2a). Thesedata were then used to calculate the interception lossduring each storm at each one of the measurement sites(Table 2b). The recovery factor was not calculated forSite F because this site is in old growth, which by defini-tion represents 100% recovery. For each of Sites C, D,and E, the recovery factor was calculated using Equation3. The data are grouped into three distinct time periods:

1. Fall of 2000, including 6 storms

2. June 2001, 3 storms, and

3. Fall of 2001, represented by 17 storms.

Rainfall interception was calculated on a storm-by-stormbasis. Values for rainfall interception recovery were cal-culated for each storm by dividing the interception lossfor a second-growth stand by the interception loss of theold-growth stand for that particular storm. The recoveryvalues for immature stands were calculated in two ways:as an average of the storm recovery values for the periodduring which each plot was operating (Table 2b), and asan overall recovery ratio for all storms lumped together(Table 2c).

Method 1 can be formulated as:

n

RR

n

ii

X

∑== 1

1(4)

Table 1. Rainfall interception and stand variables: summary.

ecruosataD epytyponaC seicepstnanimoD ytisnedgnikcotS)ah/smets.on(

yponaCytisned

)%(thgiehdnatS

)m(launnA

)%(noitpecretniesuohelttipS erutaM kcolmehnretseW 084 0.58 53 0.03

erutammI rif-salguoD 0501 0.58 51 22-12erutammI ecurpsaktiS 0051 0.57 6 0.41erutammI rif-salguoD 0901 0.07 31 71-11erutammI rif-salguoD 0501 0.58 33 22-02

keerCyarGFetiS htworgdlO seibA

kcolmehniatnuoMradec-wolleY

007 09 23 1.03

EetiS erutammI seibAkcolmehnretseW

005 88 81 4.92

DetiS erutammI seibAkcolmehnretseW

0012 66 8.8 5.91

CetiS erutammI seibAkcolmehnretseW

radec-wolleY

0812 74 5.5 0.9

8



where,

RX1

is the average recovery ratio of plot X averagedover n storms.

Method 2 can be formulated as:

100*2

=

∑∑

∑∑

PP

IPP

I

RRef

X

X(5)

In this case, the time periods of PP and IRef

must matchthe period during which the immature plot was operat-ing. For example, Plot C operated only in the fall of 2001,and therefore its recovery ratio is calculated relative to

old-growth interception for that same period (Table 2c).Using Method 1, the calculated mean recovery is inde-pendent of the time period over which the plot is oper-ated but may be biased toward smaller storms. UsingMethod 2 probably gives more weight to the larger storms,but may be partly dependent on annual climatic trends,and interception conditions as noted above. An attemptwas made to adjust for these differences by adjusting theinterception ratios for Plots C and D by the ratios of in-terception losses at Plot E for the specific period relativeto that for the whole period. There is a 6% differencebetween the two methods in the Gray Creek data. Forthe remaining analysis, the recovery ratios calculated byMethod 2 are used, because the data reported for theCarnation Creek sites were calculated from annual storm-based interception data reported by Spittlehouse (1998).

Finally, to develop a recovery curve, the calculated re-

Table 2a. Storm rainfall, throughfall, and stemflow (mm) at Gray Creek.

etadmrotSllafniaR FetiS a EetiS b DetiS CetiS

1yarG)mm(

2yarG)mm(

llaf-hguorhT)mm(

wolf-metS)mm(

llaf-hguorhT)mm(

wolf-metS)mm(

llaf-hguorhT)mm(

wolf-metS)mm(

llaf-hguorhT)mm(

wolf-metS)mm(

00-tcO-81 2.06 0.66 1.84 842.0 3.53 246.0 0.35 460.1 AN AN

00-tcO-12 6.97 0.48 0.75 016.0 8.44 515.1 7.56 966.2 AN AN

00-tcO-92 2.06 0.86 1.95 243.0 5.63 246.0 5.45 460.1 AN AN

00-tcO-13 5.6 0.7 3.3 000.0 3.3 200.0 6.4 000.0 AN AN

00-voN-40 2.71 0.81 3.21 401.0 9.11 020.0 8.31 610.0 AN AN

00-ceD-10 2.47 0.58 5.81 441.0 9.21 417.0 9.12 591.1 AN AN

10-nuJ-10 4.12 0.42 7.2 012.0 0.6 401.0 0.51 971.0 AN AN

10-nuJ-01 0.7 0.7 0.0 750.0 3.2 300.0 0.5 000.0 AN AN

10-nuJ-11 3.9 0.9 5.0 680.0 5.2 050.0 5.5 80.0 AN AN

10-peS-82 4.01 0.11 1.9 500.0 4.7 800.0 2.5 500.0 AN AN

10-peS-92 0.5 0.5 3.2 500.0 0.3 200.0 2.5 000.0 AN AN

10-tcO-70 4.01 0.11 9.6 500.0 5.6 800.0 7.1 500.0 5.8 700.0

10-tcO-90 1.12 0.32 7.81 920.0 4.71 740.0 9.31 740.0 6.91 290.0

10-tcO-12 9.41 0.61 9.11 410.0 0.31 020.0 deggulP 510.0 3.31 920.0

10-tcO-22 2.52 0.72 0.12 670.0 0.91 440.0 deggulP 580.0 1.22 080.0

10-tcO-42 9.5 0.6 7.2 010.0 6.5 300.0 deggulP 100.0 deggulP 100.0

10-tcO-52 3.91 0.12 5.51 340.0 0.61 830.0 deggulP 530.0 deggulP

10-tcO-62 1.31 0.41 5.01 250.0 8.11 510.0 deggulP 010.0 deggulP

10-tcO-03 9.41 0.61 7.8 910.0 8.8 020.0 72.9 510.0 deggulP

10-voN-10 1.31 0.41 7.8 920.0 8.9 510.0 23.2 010.0 3.21 910.0

10-voN-40 7.61 0.81 3.31 920.0 3.51 550.0 40.31 080.0 4.51 751.0

10-voN-11 1.93 0.34 4.33 170.0 9.62 391.0 08.13 542.0 8.23 184.0

10-voN-31 2.55 0.16 6.25 504.0 4.74 985.0 08.64 519.0 1.94 397.1

10-voN-41 9.84 0.45 4.34 337.0 7.92 134.0 93.14 056.0 5.44 372.1

10-voN-91 9.47 0.38 6.86 907.0 0.95 904.1 80.76 363.2 8.56 236.4

10-voN-02 7.33 0.73 6.03 672.0 8.43 741.0 09.92 681.0 7.72 463.0a .)1erugiF()2G(2yarGetistallafniarotdecnerefersi)htworgdlo(FetiSb .1yarGotdecnerefersiEetiS


9


Table 2b. Interception losses calculated for storms in Table 2a, and average values.

ecnerefeR

htworgdlO EetiS DetiS CetiS

NIARnoitpecretnI

ssolnoitpecretnI

ssol RnoitpecretnI

ssol RnoitpecretnI

ssol R)mm( )%( )mm( )%( )%( )mm( )%( )%( )mm( )%( )%( )mm(

otfeR etiS E 5.242 6.03 7.232 4.92 0.69 9.71 6.85 1.31 0.34 7.297otfeR etiS D 5.022 0.13 1.122 1.13 3.001 0.531 0.91 2.16 9.31 5.14 5.117otfeR etiS C 4.57 4.02 6.47 2.02 9.89 9.25 3.21 4.06 4.33 0.9 3.44 1.073

snaemdetsujdA 3.72 9.62 4.89 4.61 1.06 0.21 9.24

Table 2c. Calculation of mean rainfall recovery using Method 2: summary.

etadmrotS

FetiS EetiS DetiS CetiSnoitpecretnI

ssolnoitpecretnI

ssol RnoitpecretnI

ssol RnoitpecretnI

ssol R

)mm( )%( )mm( )%( )%( )mm( )%( )%( )mm( )%( )%(00-tcO-81 7.71 7.62 3.42 3.04 7.001 2.6 2.01 2.83 AN AN AN

00-tcO-12 4.62 4.13 3.42 5.03 2.79 2.11 1.41 9.44 AN AN AN

00-tcO-92 6.8 6.21 1.32 3.83 7.59 6.4 7.7 9.06 AN AN AN

00-tcO-13 7.3 4.35 2.3 2.94 1.29 8.1 6.82 5.35 AN AN AN

00-voN-40 5.5 8.03 2.5 5.03 9.89 4.3 6.91 4.36 AN AN AN

00-ceD-10 0.54 2.67 7.63 1.66 gnizeerF 4.23 4.85 gnizeerF AN AN AN

10-nuJ-10 0.12 7.78 4.51 6.17 7.18 8.8 6.63 7.14 AN AN AN

10-nuJ-01 9.6 2.99 7.4 8.66 3.76 0.2 6.82 8.82 AN AN AN

10-nuJ-11 5.8 0.49 7.6 1.27 7.67 7.3 9.93 5.24 AN AN AN

10-peS-82 8.1 6.92 9.2 3.82 7.59 6.2 3.51 6.15 AN AN AN

10-peS-92 7.2 1.45 0.2 6.93 AN 2.0- AN AN AN AN AN

10-tcO-70 1.4 8.93 9.3 3.73 6.39 6.2 0.62 3.56 9.1 3.81 9.54

10-tcO-90 2.4 4.81 7.3 4.71 7.49 6.5 4.62 7.76 4.1 7.6 7.63

10-tcO-12 1.4 9.42 8.1 4.21 8.76 deggulP deggulP AN 6.1 6.01 5.24

10-tcO-22 9.5 3.92 2.6 4.42 2.38 deggulP deggulP AN 1.3 1.21 4.14

10-tcO-42 2.3 1.23 3.0 4.31 AN deggulP deggulP AN deggulP deggulP deggulP

10-tcO-52 4.5 3.72 3.3 9.31 9.05 deggulP deggulP AN deggulP deggulP deggulP

10-tcO-62 4.3 5.42 suoiverphtiwdenibmoC deggulP deggulP AN deggulP deggulP deggulP

10-tcO-03 3.7 6.54 0.6 5.04 9.88 6.5 5.73 3.28 deggulP deggulP deggulP

10-voN-10 3.5 7.73 3.3 3.52 0.76 7.01 AN AN 8.0 9.31 7.63

10-voN-40 7.4 2.62 3.1 7.32 6.09 5.3 3.12 2.18 1.1 5.41 3.55

10-voN-11 5.9 2.22 9.11 6.03 6.731 0.7 0.81 AN 8.5 9.11 6.35

10-voN-31 0.8 1.71 2.7 1.31 4.831 5.7 6.31 2.97 4.4 6.3 2.12

10-voN-41 8.9 1.02 8.81 4.83 denibmoC 9.6 1.41 1.07 2.3 5.6 3.23

10-voN-91 7.31 3.81 5.41 4.91 1.601 5.5 3.7 0.04 5.4 0.6 8.23

10-voN-02 1.6 1.81 0.2 5.5 6.3 7.01 2.95 6.5 2.51 0.48

)1dohteM(segarevA 19 75 44

10



covery factors are plotted against stand structure vari-ables (i.e., stand height or canopy density) and a curveis fitted to the data points. A Chapman-Richards curvedescribed by Sit and Poulin-Costello (1994) was foundto fit the data well; this is an asymptotic, exponentialfunction of the same type as that which forms the snow-melt recovery curve described by Hudson (2000a, b).

RESULTS

Data Treatment

The Nature of Rainfall Interception Data

The storms for which rainfall interception was measuredranged in size from 5 to 85 mm of rain. There was arelationship between storm size and interception loss;although not well defined, the interception loss expressedas a percentage declined with increasing storm volumeat each sampling plot. Interception loss was also muchhigher for summer storms than for fall and winter storms(Figure 2). Although only three summer storms weremeasured, this was substantiated by Spittlehouse (1998),who found the same seasonal trend. Superimposed onthis relationship, Figure 2 also shows that interceptionloss at the old-growth and advanced second-growth sites(Sites F and E) is greater than that at Sites C and D.

Stemflow is affected primarily by the development oftree bark, such that it is more significant in younger stands,presumably because there is less bark to absorb wateron young trees than on mature trees. There is a relation-ship between stemflow and storm rainfall that is expo-nential for small storms, and which becomes linear forstorms >40 mm (Figure 3). The relationship is well de-fined for immature/second-growth trees, and less well

defined for old-growth trees. Stemflow is also higher foryounger trees at the stand level. This is partly due to barkdevelopment, but also partly due to the higher stockingdensity of younger stands (Table 1). The maximumstemflow that was measured was 4.6 mm at Site C, duringthe 19 November 2001 storm. This represents 5.8% of thestorm rainfall. In all other cases the stemflow was <5% ofthe storm rainfall. Expressed as a percentage of stormrainfall, the stemflow in the immature stands can be rep-resented by a power function (for example, at Site D),while at the old-growth plot, stemflow was usually <1%

0 20 40 60 80 100Total Sto rm Vo lum e (m m )

0

20

40

60

Inte

rce

pti

on

Lo

ss (

%)

4 8 12 16 20 24

20

40

60

80

100Stand Type

Old G row th

18-metre second g row th

Immature (8 - 9 metres)

Immature (5 .5 metres)

Sum m er

Fa ll - W inte r

Figure 2. Relationships between interception loss and stormsize (mm) for mature and immature forest stands at upperGray Creek.

0.0 20 .0 40.0 60.0 80 .0 100.0

T o ta l S to rm R ain fall (m m )

0 .0

1 .0

2 .0

3 .0

4 .0

5 .0

Ste

mfl

ow

(%

of

sto

rm r

ain

fall)

S ite F (o ld g ro w th )

S ite D (8 - 9 m e tre s )

P o w e r F u n c tio n S ite D

20 40 60 80 100S torm Rain fa ll (m m )

0.0

1.0

2.0

3.0

4.0

5.0

Ste

mfl

ow

(m

m)

O ld grow th

18-m etre second growth

Im mature (8-9 me tres)

Im mature (5.5 metres)

0 10 20 30 40Storm Rainfa ll (m m )

0.0

0.0

0.1

1.0

Ste

mflo

w (

mm

)

Figure 3. Relationships between stemflow and storm rain-fall for mature and immature forest stands at upper GrayCreek. Top and middle: mm of stemflow vs. mm of rainfall.Bottom: stemflow as a percentage of storm rainfall.


11


of storm rainfall (Figure 3, bottom). The one storm whenthe stemflow exceeded 1% was the most intense stormmeasured, with rainfall intensities reaching 10 mm/h.

As noted above, the interception recovery data are pre-sented and used as averages over time. The error inmeasuring interception tends to be high for individualstorms, and that error is reduced by averaging intercep-tion losses over many storms. Hudson (2000b) observedthe same thing when discussing the snowpack recoveryresults. The error is probably due in large part to theinherent variability of naturally occurring forest standsand the meteorological phenomena that influence theeffect of the stand on hydrology of the site. To show thisvisually, the recovery data given in Table 2 for individualstorms were plotted against stand height along with sta-tistical properties of the distribution of those storm re-coveries (Figure 4). The recovery at each stand, calcu-lated for individual storms, forms a probability distribu-tion that is close to a normal distribution. Note that thevariability increases as the stand height increases. The95% confidence limits of the mean of each distributionquantify the variability under each stand. Because theprecision in measuring rainfall and interception does notchange from site to site, the increase must be attributedto the stand and not to measurement error. This suggeststhat increasing stand complexity is a major contributor tothe error in determining hydrologic recovery. For ex-ample, the �recovery� calculated for the old-growth standfor each storm was calculated as the ratio of the storminterception to the overall mean interception loss of allstorms, thus the old-growth stand is 100% ± 17.1% recov-ered, while the 5.5-m immature stand has a mean recov-ery ratio of 39.8% ± 7.4%. If the half interval is plottedagainst stand height a linear relationship is formed thathas an X-intercept of 5% (Figure 4, bottom). Thus mea-surement error is 5%, and the variability due to the standaccounts for up to ±12% under old growth.

Reference Stands for Determining Recovery

Rainfall interception depends on stand characteristics andrainfall regime. The amount of water that a tree can holddepends on the size and shape of its crown, the thicknessof its bark, the presence of moss and lichen in the canopy,etc. (Spittlehouse 1998). Therefore the interception ex-pressed as a percentage of rainfall depends on the spe-cies, size, and age of the trees, and on the rainfall regime.

Clearly the Vancouver Island Mountain Range divides theIsland into east and west coast regimes for snow (Hudson2000 b) and rainfall. For example, Environment Canada(2003) reports that the long-term mean annual rainfall atTofino is almost three times that at the Nanaimo airport.In addition to this, annual rainfall volumes up to threetimes that of the Tofino site have been measured at indi-vidual sites on the west side of the Island in the PortAlberni area. Because individual trees have a finite ca-pacity to hold water, a given stand will intercept a larger

fraction of rainfall in an east coast regime compared to awest coast regime, because the frequency and magni-tude of storms in an east coast regime is lower than thatin a west coast regime.

A reference site to determine rainfall interception recov-ery for an immature stand should be of a similar speciesmix and from the same rainfall regime. Gray Creek is aSnow Accumulation Zone 4 watershed, which is equiva-lent to an east coast (Vancouver Island) regime; at thestudy site, the species mix is similar at each plot. Thusthe Gray Creek site is well controlled. Among the Vancou-ver Island stands there is a mixture of species and regimes.However, there is only one old-growth stand, composedof western hemlock, and the immature stands are com-posed of Douglas-fir and Sitka spruce. There is no ma-ture Douglas-fir stand in which rainfall interception has

0 10 20 30 40 50S tand H eight (m )

0

40

80

120

160

200

Re

cove

ry (

%)

G ray C reekRa infall RecoveryIndividual S torms

95% con fidencein te rval o f m ean

80

70

60

50

40

19

01

40

90

40

13

01

10

90

70

50

Quartiles

Frequency Distribution

0 10 20 30 40S tand H e igh t (m )

0

4

8

12

16

20

Err

or

(%,

ha

lf o

f 9

5%

c

on

fid

en

ce i

nte

rva

l)

Figure 4. Statistical distribution of recovery data for indi-vidual storms at Gray Creek. Bottom: The error (half of the5% confidence interval) as a function of stand height formsa linear relationship suggesting that measurement error is±5%.

12



been measured to act as a control for the immature Dou-glas-fir stands. It is therefore necessary to refer to theliterature to develop a suitable reference for those stands.Simulated reference stands were developed from rainfallinterception and precipitation data reported by McMinn(1957) and Rothacher (1963) for mature Douglas-fir, andadjusted using rainfall data from Tofino, Nanaimo, andCampbell River airports (Environment Canada 2003).

McMinn (1957) measured rainfall interception in old-growth Douglas-fir stands from 1951 to 1953 and reportedinterception from 37 to 53% in summer and 24 to 43%annually in the Nanaimo River area for stands from 27 mto 74 m tall. Precipitation data from various sites aroundthe Nanaimo River area, including Cowichan Lake, werealso reported for 1952 (Table 3). McMinn reported sea-sonal rainfall and interception, but for different seasonalperiods than those used by Spittlehouse (2001). There-fore adjustments were made to the seasonal data and amethod developed to calculate spring/summer and fall/winter interception from summary data (Appendix B).This resulted in a model of seasonal and annual inter-ception for old-growth Douglas-fir to act as referencestands appropriate to the immature Douglas-fir at CowichanLake and Campbell River (Table 3). The results providegood predictive relationships between seasonal intercep-tion losses and stand height (Figure 5). For winter inter-ception the relationship is linear for stands <46 m tall and

Table 3. Interception under mature Douglas-fir, 1951-53: Nanaimo River, and other sites.

ecruosataD thgiehdnatS)m(

xednietiS

launnA ssolnoitpecretnI

noitatipicerP)mm(

noitpecretnI)%(

/llaFretniw I

W

)%(

/gnirpSremmus I

S

)%()7591(nniMcM 3.72 8.91 0.3371 3.42 7.51 0.73

9.23 2.62 5.7341 0.52 1.81 5.837.54 6.93 0.6241 0.82 4.51 8.444.25 7.24 0.0831 5.23 2.81 7.949.26 8.15 5.7431 0.53 5.42 7.057.37 0.16 3.3531 5.34 0.73 0.35

)35-1591(.picerplaunnanaeM 2.6441

)2591(reviRomianaN 1.0702 0.0702)2591(ekaLnahciwoC 0.5322

)3691(rehcahtoR 0.06 0.1312 7.31 0.42emiger.RomianaNotdetsujdA 2.12 1.73

keerCyarG a 0.23 0.1401 b 7.72 8.32 0.85emiger.RomianaNotdetsujdA 9.91 0.41 8.83

keerCnoitanraC c 0.53 0.3743 0.03 3.42 5.83emiger.RomianaNotdetsujdA 0.95 5.29

a .rifeniplabushtwog-dlOb .detroperylnollafniaRc .kcolmehnretsewerutaM

then becomes exponential up to the maximum stand heightof 74 m. This probably represents the period of time overwhich a �mature� Douglas-fir stand ages toward �oldgrowth�. This process might take hundreds of years. Therelationship for summer interception is logarithmic. Thedifference between the two relationships suggests that thissame aging process may be less important for summerinterception than for winter interception.

A comparison between measured old growth intercep-tion at three different sites (Oregon, and Carnation andGray Creeks) and the McMinn relationships (Figure 5)shed some light on the relative importance of rainfallregime and stand structure in governing interception.Rothacher (1963) obtained interception values of 24% insummer and 13.7% in winter in Oregon for Douglas-firup to 60 m tall (Table 3). Spittlehouse reports mean an-nual interception of 30% in old-growth hemlock at Car-nation Creek (west coast Vancouver Island regime). Afall/winter value was calculated at 24%. At the Gray Creeksite, the overall average rainfall interception at the old-growth stand (predominantly Abies lasiocarpa and Tsugamertensiana) was 28%, and for winter interception it was23.8%. Interception data from all three sites were ad-justed to the Nanaimo River regime using the precipita-tion ratio; the adjusted values from Rothacher�s OregonDouglas-fir stand and from Gray Creek fall on the samecurve as the Nanaimo River for winter interception, but


13


not for summer interception. The Carnation Creek hem-lock interception data does not agree for either winter orsummer interception. This suggests that winter intercep-tion is less sensitive to differences in stand structure thansummer interception. The interception vs. stand heightrelationship for winter interception based on McMinn�sdata is reliable for a range of regimes and can be used toassess rainfall interception recovery for the VancouverIsland Douglas-fir stands. A more cautious approach maybe warranted for assessing summer interception recov-ery for those same stands; however, the relationship al-lows recovery curves to be developed for different refer-ence heights, thereby introducing more flexibility intothe calculation of hydrologic recovery.

Selection of a “Stand Descriptor”

The primary dilemma in deriving an operational methodsuch as this is: How can we make the method relativelysimple to use without losing the inherent complexity ofthe processes involved? Given that forest stand dynamicsare sufficiently complex that one stand descriptor alonecannot adequately represent the recovery process, this

suggests that a composite stand descriptor must be de-veloped. The primary factor that controls interception isthe size of the tree crowns, which at the stand level canbe described by the canopy depth and the canopy den-sity. For regenerating trees in the size range in whichrecovery occurs, the canopy depth is essentially the sameas the tree height.

While investigating snowmelt recovery, it was determinedthat stand height (the average height of the dominantand co-dominant trees) was the primary stand descriptorgoverning snow recovery in coastal stands (Hudson2000a). Canopy density did not provide any additionalinformation when introduced into the analysis. Theoreti-cally, it was expected that in the case of rainfall intercep-tion, canopy density might be the primary variable re-sponsible for the recovery process. Other variables to beconsidered include species, site index, and some mea-sure of the age of the stand, because older trees developthicker bark that is capable of absorbing a significantquantity of water.1

As a �first approximation��pending further developmentas described later in the report�all preliminary recoverydata from different sources were plotted against standheight and canopy density (Figure 6). The data set in-cludes data collected at Gray Creek (as described earlierin this report), as well as the summarized snowmelt andsnow accumulation recovery data (Hudson 2000b) anddata reported by Spittlehouse (1998, 2001). At this initialstage the data summarized as annual means were used.The data form logical groups when stand height is usedas the descriptor (Figure 6, top), and the data suggestthat there may be more than one curve. When plottedagainst canopy density (Figure 6, bottom) there also ap-pears to be more than one curve, but the data that definethose curves do not represent any logical groupings. Thusif canopy density were used as the descriptor, it wouldnot be possible to separate the effects of other variablessuch as species, rainfall regime, site elevation, etc. There-fore it is reasonable to proceed with stand height as theprimary descriptor, while trying to work in the additionalvariability that could be explained by canopy density.When the data representing average recovery of peaksnow accumulation (Hudson 2000b) are also included,they appear to conform to the same relationship as whenstand height is used as a predictor. Because rainfall andsnow accumulation recovery are both interception pro-cesses, it makes some physical sense to combine theminto one relationship. In this case, it helps to define thelower end of the recovery curve.

On close examination of Figure 6 (top), there are someapparent anomalies that might arise from the variability in

1 D. Spittlehouse, Research Climatologist, Research Section, BC Ministry ofForests, Victoria, BC; personal communication, 2003.

0 20 40 60 80S tand He ight (m )

0

20

40

60

Me

asu

red

In

terc

ep

tion

Lo

ss u

nd

er

Old

Gro

wth

(%)

0

20

40

60

80

100

Gray C reek Suba lpine F ir Oregon Douglas-fir

G ray Creek Suba lp ine F ir O regon Douglas-fir

Carnation Creek Hem lock

Carnation Creek Hem lock

S um m er

W inte r

Figure 5. Summary of measured interception loss underold growth Douglas-fir stands. The curves represent an in-terception model based on McMinn’s Nanaimo River stands,plus data from other sites adjusted to Nanaimo River rain-fall regime for comparison. Gray Creek and Oregon standsagree with the model for winter interception.

14



stand height. In particular, the Cowichan Lake 1 stand (Table1) is very dense compared to the Cowichan Lake 2 stand,and more in line with the 33-m Campbell River stand. Usingstand height alone assumes that there is a relationship be-tween stand height and canopy density that defines the sizeof the tree crowns. Any stand that falls outside the 95%confidence bands of that relationship has a canopy sizesignificantly different from what the relationship suggests(i.e., it is an outlier). To compensate for those differences,stand heights were adjusted to bring the outliers within the

confidence bands. To do this, a logarithmic relationshipbetween canopy density and stand height was used. In thecase of the Gray Creek sites, that relationship was com-posed of data derived from all snow course plots in theGray/Chapman/Roberts Creek area (aka Mount Elphinstone/Tetrahedron Mountain) of the Sunshine Coast (Figure 7,bottom). In the case of overstocked stands, outliers lie abovethe upper confidence band. The height is adjusted to com-pensate for the canopy density by sliding the data point tothe right, thereby placing it either on or within the confi-dence bands. Similarly, if a stand is understocked in com-parison to other stands in the area, then its tree height isreduced to reflect the reduced interception capacity of thestand (i.e., the point is slid to the left to the lower confi-dence limit). Using this same method, all rainfall plots werelumped together and the canopy heights were adjusted tocompensate for deviations in interception due to their rela-tive canopy density characteristics. In this way, adjustedstand height becomes a composite variable that representsthe size of tree crowns that make up the canopy.

Using adjusted stand height as the stand descriptor, the

1 10Stand Height (m )

-20

0

20

40

60

80

100

Ca

no

py

De

nsi

ty (

%)

A d ju ste d S ta n d H e ig h t

S ta n d H e ig h t

9 5 % C o n fide n c e L im its

1 10

20

40

60

80

100

Ca

no

py

De

nsi

ty (

%)

50

50

R ain fall Interception PlotsGray C r/Vancouver Island

Mount Elph instone/TetrahedronSnow Course Plots

Figure 7. Adjustment of stand height to compensate forcanopy density outliers.

Figure 6. Rainfall interception recovery as a function of standheight (top) and canopy density (bottom). For Douglas-firstands, CL1 and CL2 are the Cowichan Lake stands. Theheight curve conforms to the asymptotic function used tomodel snowmelt recovery, while the density curve appearsto be an exponential function. While the data form logicalgroupings with stand height as a predictor, there is no ra-tionale behind the different curves with canopy density asthe descriptor.

0 20 40 60 80 100Canopy Density (% )

0

20

40

60

80

100

Re

cove

ry (

%)

R a in fa ll R e c ov e ry 1



R = 9.08 e (0 .0 2 6 C D )

0 10 20 30 40S tand H eight (m )

0

20

40

60

80

100

Re

cove

ry (

%)

G ray Creek Ra infa ll

G ray Creek snow in te rception

H emlock/S itka Spruce (west)

D oug las-fir (east)

C a m pbe ll R ive r

C L 1

C L 2


15


rainfall recovery data fall into two separate groups (Fig-ure 6) in which the Gray Creek data are distinctly differ-ent from the data collected on Vancouver Island, withthe exception of one plot. Snow accumulation recoveryat Snow Course J at Gray Creek was similar to that of therainfall interception recovery for Sitka spruce at Carna-tion Creek. That site contained a significant pine compo-nent that was not found at the other plots. This likelyreflects the fact that pine tends to grow in open standsand therefore has reduced interception capability com-pared to other species.

Development of Recovery Curves

Influence of Storm Size on Rainfall Interception Recovery

As noted above the storm size has a very substantial ef-fect on the components of interception. An importantconsideration in developing rainfall recovery criteria isthe issue of storm size; that is, should recovery be basedon mean annual conditions, or should it be based onseasonal conditions? This would recognize the fact thatthere are different concerns regarding the effects of for-est harvesting on streamflow depending on the season.In the summer the main concern might be the effects ofharvesting on low flows, while in the fall and winter themain concern is the potential of forestry activities to in-crease peak f lows. By the same reasoning, there mayalso be a sound rationale for using only storms above aminimum size for determining hydrologic recovery, be-cause only the larger storms contribute to channel change.Assuming that the bankfull event with a return period of1�1.5 years is the appropriate threshold, this adds somelogical basis for focusing the assessment of hydrologicrecovery on the most important events.

At Gray Creek, because recovery data are available on astorm-by-storm basis, the effects of varying the stormsize threshold can be investigated. To do this, recoverywas determined for several thresholds by successivelyremoving storms below the threshold, and then recalcu-lating recovery using Method 2 for the resulting data set(Table 4). The result of this analysis shows that as thethreshold is increased, the second-growth stand at a heightof 18 m becomes progressively over-recovered compared

Table 4a. Mean recovery (%) for all storms at or above a given threshold: the effects of varying the storm size thresholdon rainfall recovery at Gray Creek.

thgiehdnatS)m(

yrevoceRatadmrotsllA

)%(dlohserhtmm-51

)%(dlohserhtmm-03

)%(dlohserhtmm-54

)%(dlohserhtmm-06

)%(0.23 0.001 0.001 0.001 0.001 0.0010.81 2.99 8.201 1.821 9.731 1.731

8.8 6.85 8.95 0.55 4.15 8.245.5 3.53 5.63 5.14 0.23 9.92

).on(smrotS 52 61 8 6 4

Table 4b. Comparison of recovery data calculated in threedifferent ways.

thgiehdnatS)m(

atadmrotsllA dlohserhtmm-54

htworg-dlOecnerefer

)%(

htworg-dlOecnerefer

)%(

-dnoceShtworg

ecnerefer)%(

0.23 001 0.001 0.0010.81 2.99 9.731 0.001

8.8 6.85 4.15 0.545.5 3.53 0.23 1.52

to old growth. This effect reached a maximum at a stormthreshold of 45 mm, with the recovery calculated at 139%relative to old growth (Figure 8, top); therefore, for largestorms the advanced second-growth stands at Gray Creekis more efficient at intercepting rainfall than the oldgrowth. This would suggest one of two things:

1. either the recovery curves should allow for these ad-vanced second-growth stands to be over-recovered, or2. the reference stand should not always default to oldgrowth, but to the stand with the maximum intercep-tion capacity (Figure 8, bottom).

When the storm threshold is increased from 0 to 45 mmthe mean interception ratio of the old growth drops fromaround 32% to 21%, levelling off at a storm threshold of30 mm, while under the 18-m second growth canopyinterception remains steady at 29% (Figure 9). This sug-gests that the old-growth stand reaches some finite hold-ing capacity after 30 mm of rain, at which point the effectof the gaps probably offsets the effect of any further on-going evapotranspiration. The second growth has a moreuniform canopy than the old growth that probably ac-counts for its greater efficiency at intercepting even thelargest storms. In comparison, under the 9-m secondgrowth the combined effect of immature trees and canopygaps prevents it from reaching a steady state, and theinterception continues to decline as the storm size thresh-old is increased.

16



The above analysis suggests that the interception ratio ofaround 30% (±1%) could be taken as a �standard� inter-ception ratio, and also supports the idea that the second-growth stand at Gray Creek can be considered a suitablereference stand for rainfall interception recovery�per-haps more so than the old growth. The suitability of astand to act as a reference for hydrologic recovery isprobably more related to the stand quality than to itsactual age. The effect of altering the storm size thresholdis similar to the seasonal effect noted by McMinn andRothacher; this is partly due to the tendency of winter

storms to be larger and more frequent than summerstorms. At Gray Creek, the effect becomes identical abovea storm threshold of 23 mm because only three summerstorms were measured, and all three were below thatthreshold. The thresholds used to do the analysis dis-cussed above were somewhat arbitrary; the maximuminterception at Gray Creek occurred under the 18-m sec-ond-growth stand with the storm size threshold between42 and 51 mm. Return period analysis of the annual maxi-mum storm series from Gray creek indicates that thesethresholds correspond to return intervals of 1.15 and 1.45years respectively. This corresponds to the interval inwhich bankfull discharge generally occurs (Wolman andMillar 1960; Hudson 2002).

Effect of Season and the Choice of Reference Standon Hydrologic Recovery

Only summary data are available for the Vancouver Is-land stands at this time, therefore the above analysis ofthe influence of storm size on rainfall recovery cannotyet be done for those sites. However, the Douglas-firreference simulator provides a means to develop sepa-rate recovery curves for spring/summer and for fall/win-ter periods, and for different management scenarios withregard to the state of maturity that a managed forest islikely to achieve.

For immature Douglas-fir stands at Cowichan Lake andCampbell River, the following procedure was followedto generate the recovery data:

1. The measured annual and/or spring/summer inter-ception data were used to calculate fall/winter inter-ception data in one of two ways.

a) If spring/summer and annual interception datawere reported, then the winter interception can becalculated as

Figure 9. Mean interception as a function of storm size forthree stands at Gray Creek. This helps to explain why the18-m second growth is more efficient at intercepting rain-fall than the old growth.

0 20 40 60S torm Size Thresho ld (m m )

5

10

15

20

25

30

35

Me

an

In

terc

ep

tio

n f

or

Sto

rms

a

bo

ve t

he

th

res

ho

ld (

%)

S ite D : 9 -m e tre se con d g row th

S ite F : O ld G row th

S ite E : 18 -m etre se co nd gro w th

Figure 8. Top: The effects of varying the storm size thresh-old on recovery of immature stands.Bottom: The effects of using second growth as a referencestand with a 45 mm storm threshold compared to otherscenarios. If the old growth is the reference and all stormsare included the result is the black curve. If only storms>45 mm are used then the second growth is over-recov-ered relative to old growth, resulting in the red curve. If the18-m second growth is taken as the reference stand thenthe result is lower recovery for younger stands.

0 10 20 30 40Stand H eigh t (m )

0

40

80

120

160

Ra

infa

ll In

terc

ep

tion

Re

cove

ry (

%)

R e co v e ry fo r S to rm s a t o r a b o ve :

0 m m

1 5 m m

3 0 m m

4 5 m m

6 0 m m

0 10 20 30 40Stand H e igh t (m )

0

40

80

120

160

Ra

infa

ll In

terc

ep

tion

Re

cove

ry (

%)

E ffe c t o f s ta n d re fe re n c e a n d s to rm th re sh o l d

O .G . R e f, 0 m m thres h o ld

2 nd G ro w th R e f, 4 5 m m

O .G . R e f, 4 5 m m


17


Table 5a. Rainfall interception data and associated stand data: summary.

etis/ecruosataDyponaC

epyteerttnanimoD

seicepsgnikcotS

ytisned)ah/smets.on(

yponaCytisned

)%(

detsujdAdnatsthgieh

)m(

dnatSthgieh

)m(

noitpecretnIllaF /

retniw)%(

gnirpS /remmus

)%(launnA

)%(esuohelttipS

keerCnoitanraC erutaM kcolmehnretseW 084 58 0.53 0.53 3.42 5.83 0.03keerCnoitanraC erutammI ecurpsaktiS 0051 57 5.11 0.6 5.21 0.92 0.411ekaLnahciwoC

)7991(erutammI rif-salguoD 0501 58 5.81 0.61 3.71 8.72 0.22

ekaLnahciwoC)6-5991(

erutammI rif-salguoD 0501 58 0.81 0.51 5.51 3.61 0.12

2ekaLnahciwoC)7991(


2ekaLnahciwoC)6-5991(


reviRllebpmaC)1002(


reviRllebpmaC)0002(


keerCyarGFetiS dlO

htworgseibA

kcolmehniatnuoMradec-wolleY

0041 09 0.23 0.23 8.32 7.55 7.72

EetiS erutammI seibAkcolmehnretseW

005 88 5.02 0.81 9.42 2.34 5.72

DetiS erutammI seibAkcolmehnretseW

0012 66 8.8 8.8 8.41 3.32 3.61

CetiS erutammI seibAkcolmehnretseW

radec-wolleY

0812 74 5.5 5.5 9.9 8.02 9.7

( )IW

IWSAW p

pIII

)1( −⋅−= (6)

where,

IW

, IS, and I

A are interception ratios for fall/winter,

spring/summer, and for a full year respectively,and

pIW

is the proportion of the annual interceptionattributable to the fall/winter period. As shownin Appendix B, the value of p

IW is taken as the

proportion of rainfall pRW

that occurs in the fall/winter period multiplied by a factor of 0.8.

b) If only annual interception was reported thenthe winter interception was calculated as

IWAW pII ⋅= (7)

Based on the above calculation, spring/summerinterception was then determined as

( )IW

IWWAS p

pIII

−⋅−

=1 (8)

2. This basic data set is reported in Table 5a. For Grayand Carnation Creeks, the recovery data were calcu-lated directly in comparison with the reference standat each site (Table 5b). Winter recovery ratios werecalculated using winter interception of the immaturestand divided by that of the reference stand, etc. Forthe immature Vancouver Island stands the intercep-tion data first had to be adjusted to the reference re-gime as follows.

a) The interception data were adjusted to theNanaimo River regime by two ratios:

i) a general ratio to adjust mean site conditions

18



to Nanaimo River regime as reported, using long-term mean precipitation at Campbell River;

� for the Campbell River site, Nanaimo Riverrainfall for 1951/53 is higher than long termmean rainfall at Campbell River airport by aratio of 1.08, thus Campbell River data are di-vided by 1.08;

� for the Cowichan Lake site, in 1952 CowichanLake received higher precipitation thanNanaimo River, also by a factor of 1.08 (Table3), thus Cowichan Lake data were multipliedby 1.08.

ii) a ratio to adjust for differences between long-term mean conditions and conditions specific to

the year in which the interception data were col-lected.

b) The adjusted interception losses were then com-pared to the simulated reference stand data for vari-ous reference stand heights to calculate recoveryratios.

c) Where appropriate, alternatives were consideredin order to make sense out of the data from theCampbell River site.

All recovery curves were developed using a modifiedChapman-Richards curve (Sit and Poulin-Costello 1994),which was found to provide a good fit to the snowmeltrecovery data (Hudson 2000b). The recovery curve is amodified form of the Chapman-Richards curve in which

Table 5b. Rainfall interception recovery: effects of season (all sites) and choice of reference stand (unreferenced Douglas-fir sites) on rainfall interception recovery.

decnerefernUrevuocnaV,sdnats

dnalsI

detsujdAdnatsthgieh

)m(

dnatSthgieh

)m(

soitaryrevoceR a

rif-salguoD,ecnereferm-04 rif-salguoD,ecnereferm-05 rif-salguoD,ecnereferm-06/llaFretniw

)%(

/gnirpSremmus

)%(launnA

)%(

/llaFretniw

)%(

/gnirpSremmus

)%(launnA

)%(

/llaFretniw

)%(

/gnirpSremmus

)%(launnA

)%(rif-salguoD

1ekaLnahciwoC)7991(

5.81 0.61 8.39 3.47 0.68 4.78 4.86 8.97 5.07 3.36 6.76


0.81 0.51 0.411 2.57 5.89 2.601 2.96 4.19 7.58 1.46 0.77

2ekaLnahciwoC)7991(

5.31 0.41 6.19 7.84 4.47 3.58 8.44 1.96 8.86 5.14 9.75


5.31 0.31 2.27 5.73 7.74 3.76 5.43 3.24 3.45 9.13 7.63

reviRllebpmaC)1002(

0.33 0.33 5.701 8.98 3.79 1.401 0.28 4.19 0.79 5.57 6.48

reviRllebpmaC)0002(

0.33 0.33 6.68 9.001 4.88 8.38 2.29 0.38 1.87 8.48 8.67

decnerefeRrevuocnaV,sdnats

dnalsI

detsujdAdnatsthgieh

)m(

dnatSthgieh

)m(

soitaryrevoceR

yarGkeerC

wonsesruoc

detsujdAdnatsthgieh

)m(

noitpecretnIyrevocer

)%(

dnatSthgieh

)m(

tlemwonSyrevocer

)%(/llaFretniw

)%(

/gnirpSremmus

)%(launnA

)%(remmuS

)%( A 5.2 5.7 9.2 5.72B AN AN 0.3 3.54

keerCnoitanraC C AN AN 9.2 2.75kcolmeH 0.53 0.53 0.001 0.001 0.001 ecnerefeR D AN AN 2.4 7.55

ecurpsaktiS 5.11 0.6 4.65 0.94 7.64 AN E 6.41 8.98 6.41 3.79

keerCyarG derusaeM detamitsE G 2.1 1.01 2.1 3.31)dnats.fer(FetiS 0.23 0.23 0.001 0.001 0.001 0.001 H 8.8 5.45 7.6 1.78

EetiS 5.02 0.81 9.401 5.77 2.99 0.39 I 6.3 8.91 1.4 6.93DetiS 8.8 8.8 4.26 8.14 7.85 0.55 J 5.9 2.93 7.5 6.27CetiS 5.5 5.5 6.14 5.71 4.82 0.61 F 5.72 0.001 23 0.001

a .)3elbaT(stnemtsujdaemigerhtiwnniMcMotevitalersiyrevocer,sdnatsrif-salguoDroF


19


an offset was introduced to allow for a non-zero X-inter-cept. The recovery curve is of the following form:

cTHtbeaR

−−−= )(1 (9)

where,

R is the recovery ratio (expressed as a percentage),

T is the recovery threshold for snowpack accumula-tion, the stand height at which recovery begins, inmetres, and

b and c are curve fitting parameters that define theshape of the curve.

The parameter a is the asymptote.

Previously the recovery curve was presented with anasymptote of 100%, under the assumption that recoverywill always tend toward 100%. While that might be theo-retically true, it might not always occur in practice. Asthe analysis of the Gray Creek data showed, in which thestorm threshold was varied, it is possible to have greater

than 100% recovery depending on the selection of thereference stand and on how the data are treated.

The Fortran program used to do the curve fitting calcu-lates the curve coefficients for a range of offsets and se-lects the coefficients that yield the minimum sum of squaresdue to error (SSE). It then reports the coefficients for eachincrement of the threshold. The program was altered toallow for the user to select a range of asymptotes and toreport the same results at each increment of the asymp-tote as well. This allows for the possibility that a standmight be over-recovered, or that it might never achievefull recovery depending on how the reference stand isselected. This approach allows for a much larger range ofmanagement options which will ultimately be of greaterutility than the restrictive approach used thus far. All re-sults of curve fitting are given in Table 6, and are reportedas coefficients only, because all recovery curve equationsare of the same form as Equation 8.

Seasonal effects at Gray Creek: toward integration of rainfalland snowpack recovery

The application of separate winter and summer recoverycurves at Gray Creek was relatively straightforward. There

Table 6. Coefficients of recovery curves for Equation 9, and some site factors that might account for the different curves.

etotpmysA dlohserhT b c R2

keerCyarGylnoniarretniW .l.s.am0001

.emigertsaoctsaE001 0.0 622.0 38.2 0.99

wonsnoniaR 001 1.2 981.0 52.1 0.99

dnalsIrevuocnaVecnereferm-06,niarretniW .l.s.am002

.semigertsaoctsew/tsaE.seicepsfoxiM

49 0.0 051.0 99.2 2.97ecnereferm-05,niarretniW 001 0.0 062.0 18.01 3.19

ecnereferm-04,niarremmuS 001 0.0 041.0 61.4 2.89ecnereferm-05,niarremmuS 001 0.6 090.0 52.1 1.97

)retniw(ataddepmuLevrucgnidnuobreppU :yrevocerllafniar,ataddepmuL

tuobanwonksignihtonfiesu.etis

021 0.0 041.0 04.1 ANllafniarnaeM 001 0.0 061.0 70.2 3.97

wons-no-niarnaeM a

)wonsno-niarwol,b2R(001 6.3 031.0 87.0 5.67

evrucgnidnuobrewoL 08 0.5 061.0 93.1 AN)depmul(yrevocerremmuS .)m04(dnalsI.naV+.rCyarG 001 0.0 511.0 85.2 4.39

sevrucyrammuS noitacilppA)b1002nosduHmorf(1S .tlemwonS 001 elbairaV 12.0 47.0 7.58

yrevocerllafniar,a1R .llafniar.velehgiH 001 0.0 622.0 38.2 0.99yrevocerwons-no-niar,b1R .wonsnoniar.velehgiH 001 1.2 981.0 52.1 0.99

,retniw,llafniar,a2Rdecnereferhtworg-dn2

llafniarwoL.)decnereferhtworg-dn2( a

001 0.0 761.0 74.2 AN

retniw+remmus,3Rdecnereferhtworg-dlo,llafniar

htwog-dlo(llafniarwoLremmuslareneg,)decnerefer

.yrevocer

001 0.0 701.0 11.2 5.79

a .b2Rsatcaotdetseggussievruc"wons-no-niar-naeM"eht,noitulosetanretlaondahevruC2RehtesuaceB

20



is little difference between measured annual and winterrecovery at Gray Creek, but this is because only threesummer storms were monitored. Site C (5.5-m stand) wasnot operating during the summer storms. Analysis of themeteorological record shows that about half of the pre-cipitation falls as rain and half as snow. Of the rainfall,72% falls in the fall/winter period, and 28% in the spring/summer. The summer storms that were monitored wereonly 5% of the total rainfall in the period of study. How-ever, interception that was measured during those stormswas 11.4% of the total storm interception measured at theold-growth site. As an alternative, and to provide data forSite C, summer interception was calculated using themethod described above. There were substantial differ-ences between measured and calculated values: measuredsummer recovery at Sites D and E were 41 and 76%, com-pared to calculated values of 55 and 93% respectively.

The division of recovery into seasonal components makesintuitive sense because the fall/winter rainfall recoverycan be combined with the snow recovery data to give afully integrated rainfall�snowmelt�rain-on-snow model(Figure 10). The curve fitting program yields a table ofcoefficients for each set of recovery data that is analyzed.In other words, there is not a unique solution for eachrecovery curve that is fitted in this way. Instead, the re-sults often suggest several alternate solutions. In the caseof the rainfall/snowpack interception recovery data, thetable of coefficients shows a double minimum SSE (Fig-ure 11), suggesting two different recovery curves. Bothequations fit the data equally well and the two curvesmerge at a stand height of 5 m.

Recall the following equations given by Hudson (2000b)to calculate the recovery threshold for a given snow ac-cumulation zone:

( ) 5.0100 += DTROS (10)

for the 300�800-m elevation band (i.e., the rain-on-snow zone), and

( )100DT = (11)

for elevations above 800 m,

where,D is the maximum snow depth in centimetres, whichusually occurs around 01 April.

Gray Creek is in Snow Accumulation Zone 4, and with arecovery threshold of 1.4 m, the expected threshold forrain-on-snow snowmelt is 1.9 m. Therefore it is reason-able to assume that Equation R1b with a threshold of 2.1m represents rain-on-snow rainfall recovery, and thatEquation R1a with a threshold of 0 m represents rainfallrecovery with no snowpack at the same elevation.

Applying the variable reference stand approachon Vancouver Island

Initial attempts to calculate recovery for the Douglas-firstands were not entirely satisfactory because there wereno appropriate reference stands that were monitored con-currently with the immature stands. This created difficul-ties in interpreting the results because it was not possibleto reconcile the behaviour of the Campbell River standwith that of the Cowichan Lake stands. The results of in-terception studies were reported for different years andthe sites are far enough apart that they might be underdifferent regimes. In addition, the properties of the origi-nal stand at each site are not known. Among the Vancou-ver Island sites the only properly referenced immaturestand is the young Sitka spruce at Carnation Creek that is

0 10 20 30 40Adjusted S tand H eigh t (m)

0

20

40

60

80

100

Re

co

ve

ry (

%)

R e co v e ry C o m p o n e n tsSn ow m e lt

Sn ow In t erc ep tion

Ra in fa ll - w in te r

Ra in fa ll - su m m er (ca lcu la te d)

Ra in fa ll - su m m er (m eas ure d)

R e co v e ry C u rv e sSno w m elt

Ra in fa ll - w in ter

RO S

Figure 10. Seasonal recovery components at Gray Creek.

0 1 2 3Recovery Threshold (m)

130

140

150

160

170

Su

m o

f S

qu

are

s d

ue

to

Err

or

(SS

E)

R OS thresho ld(2.1 metres)

R ain fa ll R ecoverythreshold (0 m .)

Figure 11. Two possible solutions for a recovery curve basedon the same data set.


21


referenced to the mature western hemlock stand. The re-covery ratio for this stand may be used to judge the valueof the modelled results. The rainfall recovery curves forGray Creek can also serve as a basis for comparison; whilerepresenting a different climatic regime, they are based ona measured reference stand at the same site.

Initial attempts to apply the model based on McMinn�sdata were partially successful. During the process of ana-lyzing the data it became clear that the same referencestand could not be applied to both sites at the same time.However, if the Campbell River stand was assumed tohave a reference stand 10 m shorter than the CowichanLake sites, the resulting recovery calculation producedcomparable results. This strongly suggests that the sitepotential is greater at the Cowichan Lake site than at theCampbell River site.

Using the approach described above, the Vancouver Is-land data were used to calculate recovery relative to refer-ence stands of 40-m, 50-m, and 60-m stand height, forboth winter and summer recovery. Note that for a refer-ence stand of 60 m applied to the Cowichan Lake sites,the equivalent reference stand for the Campbell River siteis 50 m, etc. If the reference stand height is set at 60 m (50m for Campbell River) the resulting recovery curve forwinter rainfall recovery seems to agree well with mea-sured data (Figure 12). It also conforms to the acceptedform for a recovery curve in which the recovery approaches100% for progressively greater stand heights. Alternatively,if the reference stand height is 50 m (40 m for CampbellRiver), the Campbell River stand achieves full recovery,while the juvenile 15-m stand at Cowichan Lake becomesover-recovered relative to the modelled stand as well asto measured old-growth stands at both Gray and Carna-tion Creeks. This suggests that the fall/winter interceptioncharacteristics of old-growth stands at Cowichan Lake andat the Campbell River site are well represented by themodelled stands of 60 m and 50 m respectively.

Under the alternative scenario, the Cowichan Lake andCampbell River sites are compared to the simulated ref-erence stands of 50 m and 40 m to derive recovery data.The result of this comparison is very much like the resultof applying a design storm criterion to filter out the ef-fect of small storms on the recovery curve at Gray Creek(Figure 13). In both cases the result is that stands in therange of about 15 to 30 m are over-recovered relative toold growth. At Gray Creek this occurred because secondgrowth was found to out-perform old growth at inter-cepting rain during the larger storms. At Cowichan Lake,the result was due to the specific stand being over-re-covered relative to the chosen reference stand.

The assessment of summer rainfall recovery was done ina similar way. As noted earlier, while the reference standmodel based on McMinn�s data seemed to be fairly con-sistent when applied to winter rainfall recovery, it was

less so for summer recovery. In particular, in addition tousing a different reference stand height in order to com-pare the Campbell River and Cowichan Lake stands, itwas also found that while the summer interception atCowichan Lake was well represented by the Nanaimo

0 10 20 30 40A djus ted S tand H eight (m )

0

20

40

60

80

100

120

Ra

infa

ll In

terc

ep

tion

Re

cove

ry (

%)

0

20

40

60

80

100

Douglas-fir relative to:5 0 m et re re fe ren ce

6 0 m et re re fe ren ce

5 0 m et re cu rve

6 0 m et re cu rve

D o u g la s -fir re la tive to :4 0 m etr e re fe ren ce

5 0 m etr e re fe ren ce

4 0 m etr e c urv e

5 0 m etr e c urv e

G ray C re ek

G ra y C ree k

Winter

Summ er

Tree sym bo ls r ep res en t C a rna tio n C ree k s ites

Figure 12. Results of varying the reference stand for Doug-las-fir and its effect on the recovery curve.

0 10 20 30 40Ad justed S tand H eight (m )

0

40

80

120

160

Re

co

very

(%

)

G ray C re ek - u s in g 3 0 & 40 m m sto rm th res ho lds

C ow icha n L ak e /C am pb e ll R .50 /40 m etre re fe renc e s tan d

Figure 13. Effects of reference stand criteria on the recov-ery curve.

22



River regime, the Campbell river stand appeared to bemore like Rothacher�s old-growth stand in terms of thedifferences between summer and winter interception. Forexample, Rothacher reported summer interception of 24%for 60-m old growth, which becomes 35.4% when theregime adjustment is applied. This is 70% of the pre-dicted interception of 50.1% based on the McMinn model.Applying a scaling factor of 0.7 to the modelled old-growthdata for the Campbell River stand makes the summerrecovery at that site comparable to that of Cowichan Lake.As above, reference stands of 40-m, 50-m, and 60-m standheight were applied to the Douglas-fir data. A referencestand height of 40 m produces a recovery curve that agreesmost closely with observed recovery data from Carna-tion Creek (Figure 12). It also agrees closely with mea-sured recovery at Gray Creek; therefore, in spite of un-certainties concerning the representativeness of the datait seems reasonable to accept the measured recovery andreject the calculated values. This then results in a singlerecovery curve for summer rainfall interception.

The difference in reference stand height between sum-mer and winter recovery for the Douglas-fir stands atCowichan Lake and Campbell River may seem contra-dictory, however it was noted that the model was con-sidered less reliable for summer than winter intercep-tion, possibly because summer interception is more sen-sitive to stand differences than winter interception. Inaddition to this, it has also been noted that the summerinterception data reported by Spittlehouse is actually fora six-month period that includes the spring, whereas sum-mer interception reported by McMinn was for June toAugust. While an attempt was made to compensate forthe difference, the model probably overestimates inter-ception for the spring/summer period.

Summary of the Approach

To summarize the results as much as possible, the rain-fall recovery data appear to form three distinct recoverycurves (Figure 14):

� Curve R1: The curve based on fall/winter rainfallinterception at Gray Creek can be viewed as an uppercurve for rainfall recovery based on an old growthcontrol, including all storms regardless of storm size.Site elevation is 1000 m.

� Curve R3: The curve is based on a combination ofsummer rainfall interception recovery and winter rain-fall recovery under Douglas-fir, assuming that the ref-erence stand should be old growth. The data are basedon gross interception regardless of storm size, as withR1. Mean site elevation is 200 m for winter recovery.Data were lumped with summer recovery because thetwo sets were found to be collinear. For summer re-covery the curve applies to all elevations and all spe-cies, etc.

� Curve R2: There is an area between Curves R1 andR3 where the data that form the two curves overlap.There is a degree of uncertainty in the data due to thefact that reference stands were simulated as opposedto measured. The recovery data from the juvenile Sitkaspruce at Carnation Creek also falls into this zone.This can be thought of as an alternate low elevationcurve that is based on second growth as a referencestand, rather than on old growth.

� Over-recovered : As noted earlier there is a ten-dency for some second-growth stands to be over-re-covered relative to old growth. This is particularly trueof managed second-growth stands that tend to havevery uniform canopies compared to old growth. Al-though difficult to formulate, this aspect should berecognized in a method to assess hydrologic recovery.This tendency may be limited or may be common tomanaged stands and therefore deserves further inves-tigation and/or documentation.

Rainfall interception recovery of a forest stand is a ratioof the current interception capacity over the potentialof the original stand (or alternatively, a reference stand)to intercept rainfall. For this reason it should be indepen-dent of species. Even though different species have differ-ent interception characteristics, the relative effect of forestharvesting at a given site should therefore be a function ofthe size of the trees, assuming that the same species mixis re-established at the site. The analysis that was doneon the interception data from different sources supportsthis�there was no clear distinction between species.

0 10 20 30 40Adjus ted S tand Height (m)

0

20

40

60

80

100

120

140

Re

co

ve

ry (

%)

O ver -rec ove ry

Sn ow m e lt reco ve ry

Ra infa ll rec ove ry, h ig h e lev atio n

Ra infa ll rec ove rylo w e lev atio n, 2nd grow th re f.

Su m m er rec ov ery DF w ith old gro wth re ferenc e

S 1 reco very c urv e

R 1 reco ve ry c urv e



O ve r-re cov ery

Figure 14. A set of recovery curves that together form thebasis of a method to determine full hydrologic recovery,under a range of management scenarios.


23


The factor that is most likely to affect recovery is eleva-tion, because it controls both precipitation and air tem-perature (and hence, evaporation), as well as site index.The evidence strongly suggests that the difference be-tween Curves R1 and R3 is due to elevation. The GrayCreek sites are at a mean elevation of 1000 m, while theaverage elevation among the Vancouver Island sites isabout 200 m. Data used in this report would suggest thatold growth in an east coast regime could intercept about30�40% of rainfall, and about 20�30% in a west coastregime. Thus, interception for the old-growth hemlockat Carnation Creek is on the high side, while for thehigh-elevation, old-growth, subalpine fir-hemlock-cedarat Gray Creek, it is on the low side. In the fall, evapo-transpiration rates (and therefore, interception losses) areexpected to be much lower at high elevations than atlow elevations. Therefore, all other factors being equal,this would point to elevation as the factor distinguishingthe two curves. There is no reason, based on the existingdata, to further subdivide stands according to rainfall re-gime, species, or site index. While these factors havebeen controlled for in the experimental design of theGray Creek study, they are not well controlled in theVancouver Island studies. The model based on McMinn�sdata from Nanaimo River has reduced but not eliminatedthe sources of uncertainty.

Alternative Approach

As the reader will note, every effort has been made toprovide a logical approach to the formulation of rainfallrecovery curves such that they can be applied to meet avariety of management objectives; they are based on good,albeit limited, data. The reason for doing this is in part aneffort to provide a platform for further research and vali-dation studies. Once this has been done, or as it pro-ceeds, the method of assessing hydrologic recovery canbe updated and thus applied with greater confidenceacross larger areas within coastal British Columbia andthe American Pacific Northwest.

In addition, this research provides a milestone in the sensethat this is the first time that a more comprehensive re-search-based method for assessing hydrologic recoverycan be offered as an alternative to either the method inBC Ministry of Forests� Assessing Snowpack Recover y inthe Vancouver Forest Region (Hudson 2001b) or the In-terim Method that is given in the Coastal Watershed As-sessment Procedure Guidebook (CWAP) (BC Ministry ofForests and BC Ministry of Environment 1999). As such itconstitutes the best science-based information availableat this time. To many proponents this is a very importantconsideration. However, if this seems too prescriptive orif the reader for any reason does not agree with the in-terpretation given for the different curves, an alternativeapproach is offered:

� Lumped data set with bounding curves (Table 6,

Figure 15): All data used to develop the formulationthat is described above and that appears in Figure 14,were lumped together to produce a set of four curves:

� As with Curve R1, the curve fitting results indi-cated two possible solutions that appear to sug-gest rainfall and rain-on-snow recovery curves. Thetwo curves cross (as opposed to merging) at astand height of about 6 m. Above that height theycould be considered interchangeable.

� Upper and lower bounding curves that encom-pass 90% of the data points. Out of necessity thesecurves have asymptotes of 80 and 100%.

� While the mean curves both have asymptotes of100% (this was not assumed, but is a result of curvefitting), the range between the bounding curves atthe upper end is very close to the 95% confidenceinterval of the mean rainfall interception recoverymeasured at the old-growth site at Gray Creek (Fig-ure 4).

� The rain-on-snow curve has some utility becauseit is very close to the R2 Curve toward the upperend. Because the data analysis of the winter rain-fall recovery data from the Vancouver Island sitesdid not indicate more than one solution for eachdata set, then this curve is valid as an rain-on-snow recovery curve for low elevations (i.e., thelower end of the rain-on-snow zone).

Beyond this, if the proponent chooses to use these curvesto assess hydrologic recovery, he or she must supply therationale for such use.

0 10 20 30 40Adjus ted S tand Height (m )

0

40

80

120

Ra

infa

ll In

terc

ep

tion

Re

co

ve

ry (

%)

G ray C reek - s torm s above 40 m m

G ray C reek - s torm s above 25 m m

G ray C reek - ra in & snow in te rception

D ouglas-fir rela tive to 50 m re ference

D ouglas-fir rela tive to 60 m reference

S umm er R a infall R ecovery

M e a n R ain fa ll R e co v e ry

M ea n R O S R e c ov e ry

Figure 15. Alternative rainfall interception recovery curvesbased on lumping together all rainfall recovery data, withupper and lower bounding curves. Note that the relation-ship between summer recovery and winter recovery rela-tive to an old growth reference is shown on this graph.

24



Determination of Hydrologic Recovery forRain-on-Snow

Using the University of British Columbia (UBC) water-shed model (Quick et al. 1995), streamflow at UpperGray Creek was modelled for a period of time, includingthe 1994�95 water year. The period between January andMay 1995 included 18 rain-on-snow events with a widerange of conditions (Hudson 2000b). The modelling re-sults revealed the breakdown of the components thatcontributed to the flow (Figure 16). The proportion ofthe runoff that was derived from rainfall and snowmeltin each event was related to the total daily rainfall thatcontributed to the rain-on-snow event, and to the meandaily temperature.

The rain-on-snow events can be generally divided intotwo types: the warm rain and cold rain types. With coldrain, there is insufficient energy available to melt anysnow, and the runoff consists almost entirely of rainfall.Although there is some overlap between the two types,it appears that if the mean daily temperature is at least1.5°C, the event is of the warm type. Under these condi-tions, there is a negative linear�exponential relationshipbetween the proportion of the runoff contributed by snow-melt and the total basin average storm rainfall P

R (Figure

17). An equation to describe the proportional contribu-tion of snowmelt outflow to the total storm runoff is:

)(7.11.97)( RPSp −= (12)

for PR <34 mm, and

+

= RPeSp

4.4732.2

)( (13)

for PR >34 mm,

where,p(S) is the proportion of the runoff (expressed as apercentage) that is contributed by snowmelt.

A relationship for cold rain-on-snow is also suggestedalthough it is poorly defined (Figure 17). More work isrequired to determine what form that relationship willtake. The linear�exponential function described by Equa-tions 9 and 10 look very much like a similar relationshipgiven by Church (1988) for determining the relative con-tribution of snowmelt to the water yield of a rain-on-snow storm. Church presents the relationship as a familyof parallel curves based on temperature and wind condi-tions. This suggests that upon further analysis, this rela-tionship could be developed more completely to includeall rain-on-snow storms in a temperature based model,as opposed to simply dividing them into two classes.

Implementation

A framework for modelling hydrologic recovery due torainfall, rain-on-snow, and snowmelt in a watershed withan appropriate elevation range can now be formulatedfrom Equations R1b and R2b and Equations 10 to 13. Themethod involves a simple blending of the equations acrossthe rain-on-snow zone between 300 and 800 m eleva-tion. For the general situation in a �typical� watershedthe method can be applied as follows:

� Determine appropriate values for thresholds TROS

andT using Equations 10 and 11.

� 0�300 m: Assuming rainfall dominated, use Equa-tions R2a or R3.

0.0

0.4

0.8

1.2

1.6

Dis

cha

rge

(m

3/s

)

Obse rved Streamflow

Ra infa ll Runoff

Snow melt Runoff

11-Feb 13-Mar 12-Apr 12-May

Figure 16. The components of streamflow that occurred asa result of snowmelt and rain-on-snow events, as determinedby the UBC watershed model.

0 40 80 120 160Basin Average Storm R ainfa ll (m m )

0

20

40

60

80

100

Pro

po

rtio

n o

f S

no

wm

elt

in S

torm

Ru

no

ff (

%)

W inter - Spr ing 1995warm ROS

Linear - exponentia l curve

cold ROS

Linear Cold ROS

Figure 17. The proportion of snowmelt in daily runoff duringrain-on-snow at upper Gray Creek is a function of meandaily rainfall.


25


� 300�800 m: Assuming rain-on-snow is applicable,the rainfall component of rain-on-snow is derived fromblending Equations R2b and R1b as follows:

� Assume that Equations R1b and R2b apply torain-on-snow at 1000 and 300 m respectively, withT

ROS thresholds varying according to elevation and

snow accumulation zone.

� Select equation coefficients from Table 6, substi-tuting the appropriate threshold values for thethresholds given in the table. Note that the thresh-olds vary with elevation for a given zone.

� Blend the two equations across the rain-on-snowzone between 300 and 800 m using a weightedaverage. Determine the weighting factor by linearinterpolation between 300 and 1000 m:

)1)(1()2( bRFbRFRRC −+= (14)

where,F is a weighting factor such that F=1 at 300 mand F=0 at 1000 m, and

RRC

is the resulting recovery due to the rainfallcomponent of rain-on-snow.

� Full hydrologic recovery for the rain-on-snowzone can then be evaluated as follows :

RCSROS RSp

RSp

R ×

−+×=

100

)(1

100

)( (15)

where,R

S is the snowmelt component of rain-on-snow

recovery as determined by the snowmelt recov-ery Equation S1.

� Above 800 m: Use Equation S1.

The method described above has the potential to be ap-plied on a storm-by-storm basis if a detailed analysis iswarranted, and assuming data are available. However,hydrologic recovery is usually determined for stands un-der standardized conditions, and not for individual storms.Therefore the determination of a hydrologic recoveryequation for the rain-on-snow zone in a watershed mustbe based on an analysis of the average frequency of stormsof a given size that result in rain-on-snow conditions.This can probably be regionalized, but requires an ac-cepted standard design storm on which to base the valueof p(S). Some options include:

� average size of storms that result in rain-on-snowconditions, and� rain-on-snow storm of a given return period such asthe 1- to 1.5-year event.

There are important issues about how to evaluate such adesign storm:

� storm volume of all storms known to result in rain-on-snow conditions; or

� enhanced storm volume, i.e. storm volume + result-ant snowmelt contribution.

Wherever possible such decisions should be based onan objective evaluation of the options. This implies thatthe different options should be applied to several water-sheds as case studies, followed by a critical analysis ofthe results of those studies. By using examples that arefamiliar, the option that produces the most consistentresults is likely to be the most logical choice. For thisreason it is beyond the scope of this report to discussimplementation of the method. This report is part of anongoing series about developing the method of assess-ing hydrologic recovery and about its application in evalu-ating the effects of forest harvesting on streamflow. Imple-mentation of the method is currently under investigationand will be reported in a subsequent extension note.

DISCUSSION

Several assumptions had to be made to interpret the data.The interception model that was developed helped toidentify which assumptions were valid. For example, acomparison of measured interception under various old-growth stands to the modelled interception showed thatthere was a fundamental difference in the hemlock standthat makes it unsuitable to act as a reference for Dou-glas-fir. This is probably because the shade-tolerant na-ture of the hemlock greatly increases its interception ca-pacity compared to Douglas� fir. Another assumption wasthat the stands, once mature, would approach 60 m inheight, but the model showed that this is unlikely in thecase of the Campbell River stand. The data reported byMcMinn are also useful in addressing this issue becausethey show the variability in old-growth characteristics thatoccurs within part of a watershed. All the stands thatwere monitored were in the range of 300�350 years old.Stand heights ranged from 25 to over 70 m, with thetallest stands occupying the valley bottoms. If we tried torelate recovery of any site in the watershed to the inter-ception characteristics of a 70-m valley bottom stand, theresult would be far too conservative; in fact most of thestands could never possibly reach recovery according tosuch a criterion.

While the simulations based on McMinn�s measurementsof interception under old growth at Nanaimo River havereduced the uncertainty in assessing recovery of the vari-ous Douglas-fir stands, there is no real substitute for aconcurrently measured reference stand. The data obtainedat Gray Creek were properly controlled, and while thesampling was somewhat less rigorous than that used bySpittlehouse, there is less uncertainty in evaluating re-covery. The measurement error might affect the preci-sion of the result, but not its accuracy. Furthermore, theeffect of species is controlled for at the Gray Creek site

26



but not at the Vancouver Island sites. Nonetheless, themodel was able to identify major species-related differ-ences in interception by comparing the hemlock standto other measured old-growth stands. In the final analy-sis, the remaining uncertainty has been minimized bypooling the data; the resulting generalized recovery curvescan be used to achieve specific management objectives.

It should be noted that the recovery data for Douglas-firwas based initially on an estimate of what the intercep-tion characteristics of an old-growth reference standshould be like. However, it has been shown that oldgrowth might not always be the most appropriate refer-ence stand to use as a benchmark against which to mea-sure recovery. In the case of Gray Creek, there is a cleartendency for the second growth to become over-recov-ered because it is more efficient at intercepting rainfallthan the old growth. The stand in question is a managedstand, having been subject to juvenile spacing. The notedtendency might be generally true of managed standsbecause they are maintained at a higher and more uni-form stocking density than old growth. For this reason,in areas of extensive second growth (e.g., the east sideof Vancouver Island) the use of old growth as a refer-ence stand to judge recovery might be inappropriate.For this purpose, two optional recovery curves are givento assess fall/winter recovery for low elevations: R3, withold growth as a reference, or R2, which represents re-covery relative to advanced second growth.

R3 represents the result of using a target old-growth heightof 60 m for the Cowichan Lake stand and 50 m for theCampbell River stand. This scenario indicates progres-sion toward recovery; the 33-m stand is about 88% re-covered and a stand of 18 m height is about 78% recov-ered. The alternative curve, R2, represents the selectionof 50 and 40 m reference stand height. This scenarioresults in recovery characteristics closer to those of GrayCreek; the 33-m stand is close to fully recovered and the18-m stand exhibits behaviour whereby in some years itis over-recovered. The recovery curve is still below thatof the Gray Creek recovery curve for rainfall/rain-on-snow, but it may be more realistic when applied to low-elevation, second-growth stands. If recovery is to be anassessment of the condition of the stand relative to itshydrologic characteristics prior to harvesting, then to useold growth as a reference would be inappropriate.

Notwithstanding the above discussion, the summer re-covery is represented by Curve R3. There are differenthydrologic issues related to summer and winter flows,and therefore the separation of hydrologic recovery byseason is appropriate. In the summer the main issue islow flows and how forest harvesting might affect thoseflows. The situation is less complex because snow is notconsidered, so only one recovery curve is needed. Therecovery status according to the R3 Curve may serve asan index of the potential of forest harvesting to increase

summer low flows. In the fall and winter the main issueof concern is the potential of forest harvesting to increasepeak flows. Analysis of the Gray Creek rainfall recoverydata showed how the 18-m stand became over-recov-ered relative to the old-growth reference stand, andreached a peak at around the 1.2-year event, which isgenerally accepted as the main channel-forming event.In this case, if this is a widespread phenomenon, thenconsideration should be given to the tendency for standsin the 18�30-m range to be over-recovered. This is im-portant because if the regenerating forest growth is over-recovered relative to the original stand, then these over-recovered stands will serve to mitigate against un-recov-ered stands elsewhere in the watershed.

The effect of the hydrologic recovery status of a regener-ating stand on potential runoff is relative to the intercep-tion capacity of the original stand. The way that hydro-logic recovery is defined does not allow for the variabil-ity in the interception capacity of different stands. Thus iftwo regenerating stands of equal area are evaluated atthe same level of recovery (say, for example, both standsare found to be 50% recovered) the equivalent clearcutarea (ECA) of both stands would be the same. Nonethe-less, the effect of the status of each of these stands onstreamflow would still be proportional to the intercep-tion capacity of the original stand. This is true not onlyfor rainfall recovery, but for snowmelt as well. The snowaccumulation recovery is subject to slightly different pro-cesses than rainfall recovery (e.g., sublimation and/ormelting of snow, and the capacity of the canopy to holdsnow as opposed to rain). Consequently there are somedifferences in interception; snow interception at GrayCreek currently averages about 50%, whereas the samesite intercepts <30% of rainfall in the fall and winter. Itwould make the concept of hydrologic recovery and ECAmore physically meaningful if the method could be de-veloped to account for these differences. This is also apotential issue to be aware of in interpreting the results,and an area on which future research should focus.

The concept of hydrologic recovery is based on the ideathat the re-growth of a forest canopy will restore thehydrological characteristics of harvested forest stands tothose of old growth. The influence of forest canopies onhydrological processes is inherently difficult to measureand characterize, because forest stands are non-uniform,and are composed of living things that interact dynami-cally with their environment. The interchange of energyand water between the canopy and atmosphere is verycomplex. An effort has been made to account for as manyvariables as possible, but inevitably some of that com-plexity is lost for the simple reason that there is not enoughdata to sort out all of the variables. Notably, both canopydensity and stand height have been accounted for in thecomposite variable �adjusted stand height�.

The need for operational methods that are relatively


27


simple to apply must be weighed against the need forthose methods to be scientifically defensible. It is impor-tant to recognize that most operational methods are ap-proximations for this reason. An operational method mustbe practical; it must be based on a stand variable that isregularly reported and is easy to measure with high pre-cision and without bias. In this sense, stand height isinherently a better variable than canopy density or clo-sure. The fact that stand height correlates better withrecovery than canopy density probably reflects the factthat height is a precision measurement, whereas canopydensity is highly subjective. There are several methodsof measuring it (e.g., moosehorn, spherical densiometer,�fish-eye� photography). All methods produce somewhatdifferent results, with varying levels of precision and ob-server bias, but there is no standard method of measur-ing it and consequently it is often estimated (as �crownclosure�, which is generally higher than measured canopydensity).

The concept of hydrologic recovery is routinely used tocalculate the ECA of watersheds. It is generally treated asan index of the potential for forest harvesting and regen-eration to alter the peak flow response of the watershedin question. A simplified approach such as the methodpresented here is appropriate for that purpose, becausethere are other factors that govern peak flow responsethat are probably more important than minor deviationsin forest canopy structure. If anything, the method willtend to be conservative if it is applied without adjustingthe height of overstocked stands, because if a stand isunder-stocked, this may be compensated by using thepatchiness factor. There is no other mechanism to com-pensate for the effects of overstocking.

One limitation of the analysis is that the calculation ofthe proportions of runoff supplied by snowmelt and rain-fall is based on modelled data from only one site. Al-though the data are modelled, the model has been shownto simulate snowpack dynamics very well (Hudson2000b). The relative contribution of snowmelt to any run-off event can be easily verified by examining the changesin the snowpack over time. The proportions are basedon a representative sample of the data from that site,from a range of flow and precipitation data representingreturn periods of up to 2�3 years.

Because of the need to base hydrologic recovery fore-casting on standard conditions, the relative proportionsof rainfall and snowmelt runoff in total storm flow canbe based on a �mean� storm, weighted by the probabil-ity that a given storm will fall into a given size class, oron some other criterion. While the mean storm approachmay be an accepted method of forecasting recovery basedon average storm conditions, it downgrades the weightgiven to the largest storms which, in a sense, are themost important. This introduces an inherent error accord-ing to the difference between mean hydrologic recovery

and actual recovery that could be calculated after the factfor a given storm. Because rainfall interception recoveryis slower than snowmelt recovery, the recovery specificto a large rain-on-snow storm will be underestimated bythe forecasted mean recovery if the mean storm is used.This raises the issue of how to define the design storm soit can be used as a basis for establishing a standardmethod. If the 1.2-year event (representing bankfull dis-charge) is to be used, then the error will be minimizedfor that event, which is considered the most significantchannel-forming event. This will require a regional ap-proach for establishing design storms for the operationalapplication of the method.

Hudson (2000b) gave the error in applying the snowmeltrecovery curve at 10.6%, based on the standard error ofthe curve fitted to the overall 7-year mean snowmelt andrain-on-snow melt recovery data. This is also discussedin Appendix A; the 10% �error� due to stand variability isconstant and independent of stand height. For rainfallrecovery at Gray Creek, an analysis of the distribution ofrecovery ratios of individual storms showed that therewas a basic ±5% error that was not attributable to thestands themselves. The remainder of the variability wasin direct proportion to the stand height such that the old-growth stand relative to its own mean interception couldbe anywhere from 82 to 117% recovered (Figure 4a). Ifthe 5% measurement error were removed that rangewould be reduced to 77�112%, or ±12%. In contrast, thevariability in interception attributable to the stand underthe 18-m second growth was ±6.2%. These apparent er-ror distributions were calculated in different ways; rain-fall interception recovery was calculated by averagingacross storm intensities, over a shorter time frame thansnow melt recovery. When calculated in the same wayas the mean snowmelt recovery curve, the rainfall recov-ery curves have lower error (and consequently higherR2, Table 6) than the snowmelt curve. This suggests thatthe error due to stand variability at Gray Creek is within±10%. The error in applying the method at other sitescannot be evaluated without further research to validatethe methods.

The heterogeneity of rainfall and the variability of otherfactors that influence interception (such as wind), arealmost certain to generate a large error if the data areused in raw form. Averaging of the data should thereforeremove most of that variability, thereby resulting in adata set where the errors are due to the variability in thestand and to measurement error. It may be possible tominimize error, but because forest stands are inherentlyheterogeneous, our ability to control variability is ex-tremely limited. This is not necessarily error, but can bethought of as uncertainty. We need to make manage-ment decisions in spite of that variability. This impliesthat professional judgement is required to interpret anyresults based on the application of these methods.

28



LIMITATIONS

This report describes the current findings of an ongoingregional research initiative that has been in progress forseveral years, and is far from complete. Therefore themethod described has limitations that may or may notaffect its applicability. This report, together with Vancou-ver Forest Region�s Technical Report TR-004 AssessingSnowpack Recover y of Watersheds in the Vancouver For-est Region (Hudson 2000b), provides a basis that can nowbe built upon with additional studies to test the broadapplicability to south coastal British Columbia. Knownlimitations are listed below, but the list might not beexhaustive.

1. The methods are based on data collected at a smallnumber of sites. General applicability remains largelyunknown, although the basic framework for general-izing the method is already in place.

2. There were insufficient data for investigating theeffects of such variables as tree species and site indexon rates of hydrologic recovery. These issues requirefurther research.

3. The method of blending the rain and snow curves tosimulate components of rain-on-snow runoff is basedon modeling results that were applied at only one site.

4. A method of blending the rainfall/rain-on-snow re-covery curves according to elevation is suggested butnot tested. While it is logical to assume that the mainfactor behind the two curves is elevation, there mightbe other factors as well.

5. The reference stands for Douglas-fir are simulationsas opposed to actual measured stands. However, theyare based on measurements done under Douglas-firstands in the Nanaimo River watershed which appearsto be a good reference for the Cowichan Lake sites.But, adjustments were needed to incorporate theCampbell River stand. This always leads to uncertainty.

CONCLUSION

This report describes the second step toward establish-ing a science-based, comprehensive, integrated approachfor assessing full hydrologic recovery for coastal BritishColumbia watersheds. It builds on an earlier report, Van-couver Forest Region�s Technical Report TR-004 Assess-ing Snowpack Recover y of Watersheds in the VancouverForest Region (Hudson 2000b), that describes a regionalapproach for applying the snowmelt recovery method(Hudson 2000a). This report is put forward as the bestavailable information, to be used with caution and to beapplied using professional judgement. The user shouldrecognize that the method described here has not beenwidely tested in a research capacity, although the methodpresented in the earlier report (Hudson 2000b) has beenwidely applied. To use the method, the proponent should

be aware of the limitations listed above, and should con-sider the application of this methodology in light of theselimitations.

Recognizing that forest harvesting and subsequent re-generation have the potential to alter peak streamflow,the concepts of hydrologic recovery and equivalentclearcut area (ECA) provide an index of the changes inhydrologic characteristics of forest stands. The link be-tween ECA and actual peak flow changes is not yet de-fined, although a start has been made in that direction(Hudson 2002). In the meantime, ECA should still betreated as an index of potential peak flow effects. As acomponent of a watershed assessment, it is important tokeep it in context of the other components, and to recog-nize the way in which the different components interact.

Notwithstanding the above, the methods described arebased on an extensive body of research that began morethan 45 years ago. Further, the direction of future re-search to fully develop this methodology is well estab-lished. The primary goal of future research should be tovalidate the integrated hydrologic recovery method byconducting similar research trials in representative coastalwatersheds in other parts of the region.

Other areas requiring research include:

� Synoptic validation of snowpack extent in relationto snow courses to establish the applicability of snow-melt recovery using satellite imagery.� Establishment of a method to account for differ-ences in interception capacity, to normalize the effectof hydrologic recovery on potential runoff production.� Investigate the relationships between site index andhydrologic recovery.� At the same time, it is essential to establish the linkbetween changes in ECA and documented changes instreamflow that can be reasonably attributed to forestharvesting and regeneration.� Development and testing of a numerical hydrologicsimulator as a tool for forecasting effects of forest har-vesting and roads on streamflow.

REFERENCESBCMOF and BCMOE. 1999. Forest Practices Code of BC,

Coastal Watershed Assessment Procedure Guidebook(CWAP), Interior Watershed Assessment ProcedureGuidebook (IWAP), Second Edition, April 1999. Victo-ria, BC. <http://www.for.gov.bc.ca/tasb/legsregs/fpc/fpcguide/wap/WAPGdbk-Web.pdf>

British Columbia Ministry of Sustainable Resource Man-agement. 2003. Resources Information Standards Com-mittee home page. http://srmwww.gov.bc.ca/risc/

Church, M.C. 1988. �Floods in Cold Climates� pp. 205�229 in Flood Geomorphology. Editors V.R. Baker, R.C.Kochel, and P.C. Patton. John Wiley and Sons, Inc.


29


Environment Canada. 2003. Home page for Canadian Cli-mate and Surface Water Information. http://www.msc-smc.ec.gc.ca/climate/index_e.cfm.

Giles, D.G. 1983. Soil Water Regimes on a Forested Wa-tershed. M.Sc. Thesis, University of British Columbia,Vancouver, BC.

Hudson, R.O. 2000a. �Snowpack Recovery in Regenerat-ing Coastal British Columbia Clear-cuts� in CanadianJournal of For est Research 30(4):548�556.

��. 2000b. Assessing Snowpack Recover y of Water-sheds in the Vancouver Forest Region. Forest ResearchTechnical Report TR-004, Hydrology. Vancouver ForestRegion, Nanaimo BC.

��. 2002. Effects of Forest Harvesting and Regen-eration on Peak Str eamflow in a Coastal Watershed.Forest Research Technical Report TR-022, Hydrology.Vancouver Forest Region, Nanaimo BC.

McMinn, R.G. 1957. Water relations and forest distribu-tion in the Douglas-fir region on Vancouver Island. Ph.D.thesis, Department of Botany, University of British Co-lumbia, Vancouver BC.

Quick, M.C., A. Pipes, D. Nixon, E. Yu, A. Loukas, R.Millar, H. Assaf and B. Start. 1995. U.B.C. WatershedModel Manual, Version 4.0. Mountain Hydrology Group,Department of Civil Engineering, University of BritishColumbia, Vancouver, BC.

Rothacher, J. 1963. �Net Precipitation Under a Douglas-Fir Forest� in Forest Science 9:423�429.

Sit, V. and Poulin-Costello, M. 1994. Catalog of Curves forCurve Fitting. Research Branch, BC Ministry of Forests,Victoria BC. Biometrics Information Handbook 4. http://www.for.gov.bc.ca/hfd/pubs/Docs/Bio/Bio04.htm

Spittlehouse, D. 1998. �Rainfall Interception in Young andMature Conifer Forests in British Columbia� pp. 171�74in Weather Data Requirements for Integrated Pest Man-agement: 23rd Confer ence on Agricultural and ForestMeteorology, 2�6 Nov 1998, Albuquerque, N.M. Ameri-can Meteorological Society. http://www.for.gov.bc.ca/research/pubs/pubs/0182.htm.

��. 2001. �Evaluation of Interception Data in Gileset al., 1985�. Research Branch, BC Ministry of Forests,Victoria. Unpublished report, September 2001. 2 pp.

Spittlehouse, D.L. and T.A. Black. 1981. �A Growing Sea-son Water Balance Model Applied to Two Douglas FirStands� in Water Resources Research 17(6):1651�1656.

30



APPENDIX A

AUTHOR’S NOTES: IMPLEMENTATION OFHYDROLOGIC RECOVERY METHODS

Since the Vancouver Forest Region released Techni-cal Report TR-004 Assessing Snowpack Recover y of Wa-tersheds in the Vancouver Forest Region (Hudson 2000b),the method has been widely implemented. It is assumedthat anyone using this method would apply his/her pro-fessional judgment in its application. The heterogeneityof a watershed�s climate, soil properties, geology, etc., aswell as the genetic variability among tree species, allcontribute to a large unexplained variability in the influ-ence of regenerating forest stands on site level hydrol-ogy. The fact that we must use a simplified approach toassessing hydrologic recovery is a direct consequence ofthat variability. In other words, we might eventually beable to account for all those factors that cause the vari-ability but the resulting method would be far too com-plex to apply operationally. It is for this reason that pro-fessional judgment is needed to interpret the results ofapplying hydrologic recovery methods.

The method described in TR-004 is based on snowmeltduring rain-on-snow and radiation melt events at GrayCreek between 1993 and 1997. This data set is shown inFigure 5 in TR-004 (reproduced here as Figure A1). Forexample, according to the curve, a 4-m stand is 55% re-covered. The scatter of the observations indicates thatthe recovery could be anywhere between 20 and 85%depending on the specific combination of variables thathad occurred at that site in any given year. Thus by in-cluding the total variability in the observations, the 4-mstand is 55 ± 32 % recovered.1 Because that data set ismade up of repeated measurements (eight events) at eachof 10 snow courses, the points in Figure 5 are not en-tirely independent. If recovery is averaged over the 8events at each snow course, the resulting data set con-sists of 10 points that are completely independent of eachother, as shown in Figure 6 (reporduced here as FigureA2). Using the error bands in Figure 6, recovery for the4-m stand is 55 ± 10 %. This means that about two-thirdsof the variability in the response of snowmelt to thatstand is due to climatic variability. In both Figure 5 andFigure 6, the data points form a uniform band about therecovery curve, suggesting that both components of vari-ability are independent of stand height. Therefore theunaccounted variability in snowmelt recovery is 10%, re-gardless of the stand height. The additional 22% variabil-ity is due to climatic conditions and can therefore beignored because it has nothing to do with the stand.

1 Because the dependent variable is expressed as a percent, then the absoluteerror and the relative error as a percentage of the value are both percentages.This could be confusing, particularly when the value is around 50%, which makesboth absolute and relative error the same. I have used absolute error.

0 4 8 12 16S tand H eig ht (m )

0

20

40

60

80

100

120

140

Rec

over

y (%

)

R O S M elt R ecoveryS pring M e lt R ecoveryS pring C urveR O S C urveC om bined C urve

Figure A1. Spring and rain-on-snow (ROS) melt recovery,with separate and combined curve fits. (Figure 5 in Hudson2000b)

0 4 8 12 16

5 -yea r M ean S tand H e igh t (m )

0

20

40

60

80

100

120

5-ye

ar M

ean

Rec

over

y (%

)

Figure A2. Combined spring and rain-on-snow melt recov-ery curve based on averaged recovery data. The dashedlines represent + 1 s.e. about the curve. (Figure 6 in Hud-son 2000b).


31


Any forestry technician or researcher who has measurednatural forest stands can appreciate the inherent variabil-ity in those stands, and particularly among young stands.The recovery curve represents the mean response. Forlarge watersheds with lots of forest openings close to thethreshold height there is almost certainly a high degreeof variability in how those stands affect the hydrology ofthe site. That variability is at its maximum at the time thestand is just beginning to alter its hydrologic regime, be-cause that effect is very tenuous and difficult to detectseparately from the effects of local topography. How-ever, taken across the landscape level, the effect of allsuch �marginal� stands will approach the average. Thatis what the hydrologic recovery method aims to do: toestimate the mean effect that all stands of more-or-lessequal height have on site-level hydrology. As such it formspart of a methodology to examine the cumulative effectof a large number of small effects dispersed throughoutthe watershed. It is accurate for large sample sizes, andits accuracy declines as the sample size (or watershedarea) decreases, although the error is not likely to ex-ceed 10%. If there is some doubt as to the applicability ofthe curve to a specific stand that is at or near the thresh-old, recovery may be reported as a possible range ofvalues. Either way, the professional proponent shouldprovide a clear rationale for his/her choice.

The recovery curve, based on the Chapman-Richardscurve, is actually an S-shaped curve that depicts earlytree growth as gradual; this is followed by a period ofrapid growth. In the case of the snowmelt recovery curve,the bottom of the S-curve (i.e., the gradual part) is trun-cated by the snowpack; that is, all trees below that thresh-old are covered over. It may be difficult for some to visu-alize the large increment in control that the stand appar-ently exerts for a relatively small increment in height.The reason why this occurs is that the threshold is basedon the mean annual peak snow depth. However, thepoint where the melt rate is at a maximum occurs after atleast part of the snowpack has melted. For example, ifthe threshold is 1.8 m, and the stand grows by up to 1 m/y, it could theoretically achieve 30% recovery within oneyear after clearing the threshold.

This may seem incredible, but it is critical to understandthat the snowmelt and/or the potential for rapid melt tooccur is probably at a maximum when the snowpack isat least half gone. So, the trees cannot influence the snow-

melt process when they are totally covered by the snow-pack; they do not have to because the snowpack is notready to melt rapidly either.

The pack first consolidates and gradual melt occurs untilthe depth is down to about 0.9 m. At this point, thesnowpack occupies the part of the stand that is mostlikely growing outward at its maximum rate. (Recall thatin juvenile trees, their crowns go right down to the ground,and they do not just grow upwards, they also grow out-ward, thereby filling up the space between trees). Thepoint where the snowpack becomes discontinuous is aturning point�once this happens, the melt rate is greatlyincreased due to the sudden increase in exposed surfacearea, if in fact the snowpack is exposed at that time.

Forest stands are dynamic�and juvenile ones are par-ticularly so. We often forget that trees are living things,not inanimate objects, and they do a lot more to altersnow and rain interception conditions than just to sitthere with their arms out, so to speak. One importantaspect of what they do is to gather and re-radiate heat.That has a large effect on snowpack dynamics. In areaswhere snowpacks are not particularly deep (e.g., Snow-pack Accumulation Zone 4 on the east side of VancouverIsland), a small increment of growth can have the effectof totally altering the thermodynamics of a snowpackthat is primed for rapid melt. This is not just due to thesize of the trees, but also to the fact that they are devel-oping a canopy together with neighbouring trees. Thedevelopment of individual tree crowns and the develop-ment of a canopy are two different processes.

There comes a point where the canopy develops to theextent that the snowpack, once it becomes discontinu-ous, is under the canopy as opposed to in the canopy.This does not necessarily occur gradually, but may oc-cur over the course of one or two growing seasons. Asfar as the snowmelt recovery is concerned, that changeis instantaneous. The stand goes from leaving the dis-continuous snowpack exposed to rapid melt to shelter-ing it in the course of one or two growing seasons.These effects are difficult to quantify, but nonethelessthey are very important in their influence on watershedhydrology. I believe that this process, or something likeit, occurs to cause rapid changes in the thermodynam-ics of melting snow. The question is, how can this bequantified?

32



APPENDIX B

NORMALIZING INTERCEPTION DATA

The main difficulty is interpreting the interception datafrom various sources is that only summary data are avail-able, and in each case the data are summarized somewhatdifferently. Most studies identify seasonal components sepa-rately, but define the seasons differently. McMinn (1957)reported annual and summer (June�August) interception,whereas Spittlehouse1 reports annual and spring/summer(April�September) interception. Because the time periodsfor reporting seasonal interception are different, a normal-ization procedure was developed, primarily to separateinterception into spring/summer and fall/winter periods.The fall/winter period was of primary interest in this studybecause the major storms occur at that time.

This procedure was based on precipitation data from afew key sites and on interception data where the seasonalcomponents are known. The purpose was twofold:

1. to adjust the McMinn interception data to providean interception model suitable for deriving seasonalrecovery ratios from data reported by Spittlehouse, and2. to calculate seasonal interception data (specifically,fall/winter) from reports where only annual or annualand summer interception values are reported.

The main goal of the analysis was to differentiate fall/winter recovery and spring/summer recovery periods.Therefore for the purpose of this analysis, the followingseasons are defined:

Fall/Winter W October�March

Spring/Summer S April�September(Spittlehouse)

Fall/Winter/Spring FWS September�May(McMinn)

Annual A(McMinn, Spittlehouse, Rothacher)

Precipitation data in the form of long-term climate normalscan be obtained free of charge from the Environment Canadawebsite. Precipitation normals for Campbell River, Nanaimo,and Tofino were summarized into seasonal totals, and fromthese summaries some key ratios were obtained (TableB1). In addition to this, monthly data were purchased fromAES for the same sites for the period 1995�2001 (Table B2)so that data obtained from interception studies could bereferenced to the precipitation data for the specific timeperiod in which the interception data were collected.

A Working Method to Derive W and S Componentsfrom Summary Data

The underlying assumption in this analysis is that there isa relationship between the ratio of seasonal to annualrainfall and the corresponding interception ratio. For ex-ample, the mean winter rainfall ratio PP

W/PP

A (the pro-

portion of annual rainfall that falls in the fall/winter sea-son) is between 0.7 and 0.75 at most sites. The corre-sponding interception ratio I

W/I

A is expected to be re-

lated to, but not equivalent to, the rainfall ratio. Much ofthe interception data require a good estimate of seasonalinterception ratios to disaggregate annual interceptioninto seasonal components.

The correct way to calculate seasonal interception is touse the measured interception totals in mm (i.e., I = PP-T-S) and divide these values by the seasonal rainfall. Thiscan be done if interception is reported for one seasonand for the whole year if appropriate rainfall data areavailable. If interception is reported only as a percentagewithout supporting data, it is still possible to apportionthe interception between seasons using the given ratiosalone. The following expression can be used to divideannual interception into seasonal components:

SIWWIWA IpIpI ⋅−+⋅= )1( (B1)

where,p

IW is the proportional component of interception that

occurs in fall/winter relative to the whole year, and

IW

and IS are the winter and summer components of

annual interception, IA.

All data expressed as percentages.

If annual, winter, and summer interception componentsare all known, then re-arranging Equation B1 will yieldthe following expression to calculate the value of p:

SW

SAIW II

IIp

−−

= (B2)

Note that a similar value, p�, can be calculated directly bya simple ratio:

AW IIp =' (B3)

where,I

W and I

A are expressed in mm.

This can be done only if measured interception and rain-fall data are available.

Using data with measured interception, interception andseasonal interception ratios were calculated using twodifferent methods: the Direct Method and the WorkingMethod (Table B3 and Table B4).

Direct Method: Measured interception data were usedto calculate p� :

1 Spittlehouse 1998, 2001, and see Footnote 1.


33


� At Gray Creek, all parameters were measured at thesite. This allowed p� to be calculated directly as theratio of winter over annual interception. Similarly, to-tal rainfall during storms in which interception wasmeasured was also divided up seasonally, and a win-ter rainfall ratio p

R was calculated.

� The interception data for Campbell River andCowichan Lake are based on annual and spring/sum-mer interception ratios reported for specific years, andmonthly rainfall data from Campbell River airport forthe same years. Using reported summer and annual in-terception as a percentage, interception volumes forthose periods were calculated from the rainfall data andthe winter interception was determined by subtraction.� The Campbell River rainfall data were used �as-is�to determine interception for the C.R. Douglas-fir plot,whereas the multiplier 1.08 was used as a regime ad-justment factor for the Cowichan Lake plots.� To apply the ratio p� to summary data, it is multipliedby the p

R ratio when interception values are converted

back to percentages. In the case of Gray Creek, the sum-mer interception data are derived from only five stormswhose total volume was only 7.6% of the total annualstorm rainfall. Thus the winter rainfall ratio is 0.924 inthis case, while total winter makes up 72% of the totalannual rainfall (both storm and non-storm rain).

Working Method: The ratio p was calculated fromseasonal interception (as %) using Equation B2. Thiswas applied directly to interception data that were cal-culated as per the above.

The results of this analysis show that either method canbe used to calculate seasonal interception components,depending on availability of data. While the primary goalwas to derive a factor to estimate winter interceptionfrom winter rainfall ratios, the Direct Method serves tovalidate the Working Method. To apply either method,the winter precipitation ratio must be multiplied by thewinter interception ratio p to derive an adjustment factorthat is calculated as follows:

Direct Method

17.0)(09.1' −= RWpp (B4a)

)(' RWIW ppp = (B4b)

Working Method

14.0)(78.0 += RWIW pp (B5)

Application to McMinn Data

Before this adjustment can be applied to the McMinndata, another adjustment factor is needed that will com-

pensate for differences in the length of season. Usingmonthly data from Nanaimo and Campbell River, the ra-tios PP

W/PP

A and PP

FWS/PP

A were calculated. The rela-

tionship between those two variables can be used toestimate the PP

W/PP

A ratio among the McMinn data (Fig-

ure B1b), according to the following equation:

9.260.317.582

−

−

=

A

FWS

A

FWS

A

WPP

PPPP

PPPP

PP (B6)

The McMinn data are summarized in Table B5 and TableB6. The original data set consisted of 21 plots that werethen organized into classes according to several differentcriteria, including stand height, site index, and total pre-cipitation. Once again, stand height promised to be thebest single classification variable. The plots were groupedinto six classes according to 10-m increments in standheight starting with 20�30 m up to 70�80 m. The fall/winter interception was determined by first applying Equa-tion B6, and then Equation B5 to give the final resultreported in Tables B6 and B7.

A Model to Derive Reference Stands forImmature Douglas-FirBecause the calculation of seasonal interception data usingboth methods is in close agreement (Figure B2), thefollowing equation can be used to calculate interceptionunder a Douglas-fir reference stand:

4.14)(052.0 += SHIW

for stand heights (SH) <46 m, and

04.4)(0607.0)(000747.0 2 +−= SHSHW eI

for SH >46 m (B7)

SummaryThere are two possible approaches to determining seasonalinterception: a Direct Method and a possible Working Methodthat can be applied if there is insufficient information tocalculate those values directly. The proportional seasonalweighting factor, or interception ratio (p) is related to theseasonal rainfall ratio (p

R). The value of p could be de-

rived from any season, but because the primary interestfor coastal British Columbia is fall and winter rain-on-snoweffects, the winter interception ratio was determined. Thoseratios were calculated from data where both methods couldbe applied. The ratios were then applied to the McMinnold-growth interception data, thereby using the direct ap-proach to validate the working method.

The two methods used to calculate seasonal interceptiondata produced similar results (Figure B2). Thus two ob-jectives have been achieved: a working method for ap-portioning seasonal interception has been validated, anda model has been developed to act as reference stand forimmature Douglas-fir on Vancouver Island in order tocalculate rainfall interception recovery for those stands.

34



Table B1. Long-term mean precipitation data, by season for three Vancouver Island sites: summary.

PPA

PPW

PPS

PPSWF

oitaR

PPW

PP/A

PPSWF

PP/A

reviRllebpmaC)mm(llafniaR 1.4431 4.589 8.992 5311 37.0 88.0)mc(llafwonS 0.901 4.34 0.0

)mm(noitatipicerP 5.1541 1.895 8.992

omianaN)mm(llafniaR 9.7701 4.428 2.512 2.739 67.0 09.0)mc(llafwonS 9.08 7.13 0.0


onifoT)mm(llafniaR 4.7523 8.2042 3.127 5.5182 47.0 09.0)mc(llafwonS 8.24 53 0.0


Table B2. Seasonal rainfall at Campbell River, for specific time periods.

raeYPP

A

)mm(PP

W

)mm(p

WRPP

S

)mm(5991 4.0881 4.4641 977.0 0.614

69/5991 3.3361 3.7121 547.0 0.6146991 6.3931 9.329 366.0 7.9647991 4.1971 4.2511 346.0 0.9368991 4.0081 5.8751 778.0 9.1229991 8.5891 5.3761 348.0 3.2130002 0.4111 5.138 647.0 5.2821002 8.4921 8.259 637.0 0.243

Table B3. Determination of p’ as a function of pR using measured interception data at Gray Creek and at Campbell River,by the Direct Method (using measured interception volumes).

tolP thgiehdnatS)m(

IW

)mm(I

S

)mm(I

A

)mm('P p

WR

)0002(reviRllebpmaC 33 3.061 57.48 1.542 456.0 647.0)7991(1ekaLnahciwoC 5.81 5.522 1.002 6.524 035.0 346.0)7991(2ekaLnahciwoC 5.31 8.791 1.131 9.823 106.0 346.0

keerCyarGFetiS 23 6.651 0.03 5.791 397.0 429.0EetiS 81 3.461 7.13 691 838.0 429.0DetiS 8.8 7.58 1.71 8.201 438.0 429.0

rofderotinomsmrotsgnirudniaR I 6.856 4.37 0.317 429.0llafniarlatoT 0.3701 3.714 3.0941 027.0


35


Table B4. Determination of p as a function of pR using measured interception data at Gray Creek and at Campbell River,by the Working Method (using interception ratios).

tolP thgiehdnatS)m(

IW

)mm(I

S

)mm(I

A

)mm('P p

R

)0002(reviRllebpmaC 33 3.91 03 22 647.0 647.0)7991(1ekaLnahciwoC 5.81 1.81 92 22 346.0 346.0)7991(2ekaLnahciwoC 5.31 9.51 91 71 346.0 346.0

keerCyarGFetiS 23 8.32 9.04 7.72 177.0 429.0EetiS 81 9.42 2.34 5.72 168.0 429.0DetiS 8.8 8.41 3.32 3.61 368.0 429.0

Table B5. McMinn data as reported and after seasonal adjustment: reported data along with adjusted interception volumes.

egarevAthgiehdnats

)m(

seulavlaunnA stnenopmocdetropeR stnenopmocdetsujdAPP

A

)mm(I

A

)mm(I

A

)%(PP

SWF

)mm(PP

remmuS

)mm(oitaR p

WRI

W

)mm(I

S

)mm(23.62 3.3371 8.124 3.42 4.5841 0.842 638.0 7.212 3.26358.23 5.7341 5.204 0.82 5.7321 0.002 248.0 7.212 3.26376.54 0.6241 3.993 0.82 4.2221 6.302 138.0 5.202 3.96344.25 0.0831 5.844 5.23 5.7711 5.202 028.0 1.712 0.02488.26 5.7431 4.245 3.04 9.1411 6.502 428.0 6.662 3.09486.37 3.3531 7.374 0.53 4.3711 9.971 528.0 5.232

5.804

egarevAdnatsthgieh

)m(

retniWllafniar PP

W

)mm(

launnAnoitpecretni I

A

)%(

noitpecretnilanosaesdetaluclaCdohteMgnikroW dohteMtceriD

noitpecretnIoitar p

WII

W

)%(I

S

)%(

noitpecretnIoitar 'p I

W

)%(I

S

)%(23.62 3.7541 3.42 297.0 7.51 0.73 247.0 5.71 8.3358.23 4.6121 0.52 797.0 1.81 5.83 847.0 5.71 8.3376.54 4.1911 0.82 887.0 4.51 8.44 637.0 0.71 2.9344.25 8.8311 5.23 087.0 2.81 7.94 427.0 1.91 0.6488.26 8.7111 0.53 287.0 5.42 7.05 827.0 9.32 1.9486.37 2.4211 5.34 387.0 0.73 0.35 927.0 6.92 4.73

Table B6. McMinn data as reported and after seasonal adjustment: adjusted interception calculated by the WorkingMethod and the Direct Method.

36



0.86 0.88 0.90 0.92 0.94 0.96Rainfa ll R atio FW S/A : (F a ll + W inter + Spring) / Tota l

0 .60

0.65

0.70

0.75

0.80

0.85

0.90

Ra

infa

ll R

atio

W/A

: (F

all

+ W

inte

r)/T

ota

l 0 .5 0.6 0.7 0.8 0.9W inter Rainfa ll R atio pR

0.60

0.70

0.80

0.90

1.00

Win

ter

Inte

rce

ptio

n r

ati

o (

p)

W o rk in g M e th o d

D ire c t M e th od

W /A = 867 .6 (FW S/A) - 922.7(F W S/A )2 + 327.4 (FW S/A)3 - 271 .4

0 20 40 60 80Stand H eight (m )

10

20

30

40

50

60

Se

aso

na

l In

terc

ep

tio

n f

or

Re

fere

nce

Sta

nd

(%

)

S e a so n a lly A d j u ste d In te rce p tio nW o rk ing M e tho d

D irec t M e tho d

In te rc ep tion - S tan d H e ig h t M o de l

20

30

40

50

60

Spring - Sum m er

Fall - W inter

Douglas-fir Eas t Coast Regim e (N ana im o R iver)

Figure B1. Relationships to convert seasonal rainfall ratio(bottom) and to derive interception ratios from rainfall ratios(top).

Figure B2. Relationships between interception under ma-ture Douglas-fir and stand height for Nanaimo River (basedon data from McMinn 1957).

tr27 cover2...title tr27 cover2.p65 author user created date 191030529115831

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