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A critical review of pairedcatchment studies withreference to seasonal flows and climatic variabilityAlice Best, Lu Zhang, Tom McMahon, Andrew Western, Rob Vertessy
A critical review of pairedcatchment studies withreference to seasonal flows and climatic variabilityAlice Best, Lu Zhang, Tom McMahon, Andrew Western, Rob Vertessy
Authors: Alice Best1,2,3, Lu Zhang1,3, Tom McMahon1,2, Andrew Western1,2, Rob Vertessy1,3
1. Cooperative Research Centre for Catchment Hydrology.2. Department of Civil and Environmental Engineering,
University of Melbourne.3. CSIRO Land and Water, Canberra.
CSIRO Land and Water Technical Report 25/03CRC for Catchment Hydrology Technical Report 03/4MDBC Publication 11/03
Published by: Murray-Darling Basin Commission
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Cover photo: Arthur Mostead Margin photo: Mal Brown
© 2003, Murray-Darling Basin Commission and CSIRO
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Acknowledgements
This work was funded by the Murray-Darling Basin Commission through SI&E Grant NumberD2013: “Integrated Assessment of the Effects of Land use Changes on Water Yield and SaltLoads”, and the Cooperative Research Centre for Catchment Hydrology.
State Forests—NSW, CSIR South Africa and Manaaki Landcare Research kindly provided uswith paired catchment data from Redhill, South Africa and New Zealand respectively. We thankSandra Roberts and Peter Richardson from their helpful comments on this review.
The senior author was supported by a University of Melbourne Research Scholarship, and theCRC for Catchment Hydrology.
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYi
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYii
Executive summary
Since European settlement in Australia, large-scale clearing of native vegetation for agriculture has caused alterations in the hydrologic regime of many Australiancatchments. The Forest Plantations 2020Vision states that by 2020 the area of treeplantations within Australia will treble. If implemented, this will impact on water yieldat both local and regional scales. Pairedcatchment studies have been widely used as a means of determining the magnitude ofwater yield changes resulting from changesin vegetation and they provide a possiblemeans of predicting the likely impacts ofbroad-scale vegetation changes on water yield.
This review focuses on the use of pairedcatchment studies as a means for
determining long-term changes in water yieldas a result large scale changes in vegetation.Paired catchment studies can be divided intofour broad categories: afforestationexperiments, deforestation experiments,regrowth experiments and forest conversionexperiments. The methods used to assessthe magnitude of annual and seasonalchanges in water yield have been reviewedand implications for applying pairedcatchments results to large catchments,where the land use is likely to consist of amosaic of vegetation at different stages ofdevelopment, have been identified. Currentknowledge gaps in relation to the impacts ofbroad scale vegetation changes on flowregime and seasonal flows are highlightedand possible methods of addressing thesegaps are suggested.
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYiii
Acknowledgements i
Executive summary ii
1. Introduction 1
2. Paired catchments 32.1 Methods used to determine annual changes in water yield 32.2 Methods for determining seasonal changes in water yield 32.3 Types of paired catchment experiments 4
3. Hydrological processes in relation to vegetation type 53.1 Precipitation 53.2 Evapotranspiration 5
3.2.1 Transpiration 63.2.2 Interception 6
3.3 Infiltration 63.4 Overland flow 63.5 Deep Drainage, base flow and recharge 73.6 Soil water storage 73.7 Summary of processes 7
4. Changes in water yield due to changes in vegetation type 84.1 Generalisations based on paired catchment data 84.2 Mean annual and annual water yield 114.3 Annual flow regime 15
4.3.1 Flow duration curves 154.3.2 High and low flows 164.3.3 Impact of vegetation changes on annual FDC,
high and low flows 164.4 Seasonal water yield and flow regime 18
5. Summary of limitation of paired catchments studies 225.1 Limitations in reported literature 225.2 Application to large catchments 22
5.2.1 Spatial issues 225.2.2 Climatic variability 23
6. Summary and conclusions 24
Reference list 25
Appendix A 31
Table of contents
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITYiv
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY
Since European settlement in Australia, large-scale clearing of native vegetation foragriculture has caused alterations in thehydrologic regime of many Australiancatchments. In southern Australia salinity is recognised as one of the most seriousenvironmental degradation issues, affectingboth soil and water quality. The massiveclearing of native vegetation and itsreplacement by shallow-rooted annual cropsand pastures has caused salinity, through thereductions in evapotranspiration andincreases in groundwater recharge. Thedecrease in evapotranspiration is also likelyto lead to an increase in streamflow, whichnot only increases water supply, but alsohelps to dilute salt inflows (Zhang et al.1999). Plantations for Australia: The 2020Vision (DPIE 1997) states that by 2020 thearea of tree plantations within Australia willtreble. If implemented, this will impact onwater yield at both local and regional scales.The response of catchments to such a landuse change is likely to vary in both spaceand time and in order to develop sustainableland management options, it is necessary topredict the effects of such afforestation onwater yield and it seasonal variability.
Paired catchment studies have been widelyused as a means of determining themagnitude of water yield changes as a resultof changes in vegetation. A number of reviewarticles have summarised the results of thesestudies. Bosch and Hewlett (1982) reviewedcatchment experiments to determine theeffect of vegetation change on water yield.They updated an earlier review by Hibbert(1967) in which 39 experimental catchments,predominantly in the USA, were analysedand the following generalisation made:
1. reduction in forest cover increases water yield
2. establishment of forest cover on sparsely vegetated land decreases water yield
3. response to treatment is highly variableand, for the most part unpredictable.
Bosch and Hewlett (1982) added anadditional 55 catchments to those reviewedby Hibbert (1967). Two types of experimentswere reviewed—paired catchment studiesand time-trend studies—that providecircumstantial evidence of the influence ofcatchment management on water yield.While Bosch and Hewlett (1982) supportedthe first two conclusions made by Hibbert,their results indicated that to a certain degreethe influence of afforestation anddeforestation could be predicted. Since 1982a number of additional paired catchmentstudies have been reported in the literature.The results of some of these studies havebeen summarised in the subsequent reviewsof Hornmeck et al. (1993), Stednick (1996)and Sahin and Hall (1996). Vertessy (1999,2000) reviewed the literature available onpaired catchment studies with respect toforestry and streamflow. These two reviewsprovide a comprehensive summary of thepresent understanding of land use changeimpacts on water yield, with particularreference to Australian conditions. While theimpact of afforestation and deforestation onmean annual water yield is well understood,there is little reported in the literature onseasonal water yield and what has beenreported is mainly of a descriptive nature.
While results from the many pairedcatchment studies demonstrates that theycan be used to assess the impact of landuse change on water yield at the local scale,doubts exist about the application of pairedcatchment results to large catchment or aregional scale. On a regional scale, land usechanges are likely to be mosaics withvegetation at different stages ofdevelopment. Therefore uncertainty existsabout the application of small scaleexperimental results to large catchments(Wilk et al. 2001). An alternative method thatcould be used to assess the impacts ofvegetation changes on large catchments is the use of time-trend studies. However this requires the separation of the impact ofvegetation changes from climatic variability.Munday et al. (2001) used a general additivemodel (GAM) to assess to impact of a
1
1. Introduction
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY2
mosaic of vegetation types (pine plantation,eucalypt forests and pasture) on water yieldin the Adjungbilly catchment in south easternAustralia. This model simulated the annualaverage water yield changes in response to the natural ageing of forests and to userdefined logging regimes. While a time-trendstudy with a good vegetation history wasused in this study, paired catchmentexperiments were used to gain anunderstanding of the impact of pineplantations and the stand age of nativevegetation on water yield.
The purpose of this report is to:
1. review the paired catchments methodsused to assess the magnitude of annualand seasonal changes in water yield thatcan be attributed to alterations invegetation type
2. investigate the possible methods forseparating the impacts of climaticvariability on water yield form the effectsof alterations in vegetation types
3. highlight the knowledge gaps in theliterature in relation to the impacts ofvegetation type on seasonal water yieldand flow regime
4. suggest how generalisation made frompaired catchments can be applied tolarge catchment with a mosaic ofvegetation types.
This review includes an additional 56 pairedcatchments on top on those reviewed byBosch and Hewlett (1982) bringing the totalnumber of paired catchment experimentsreviewed to 150. Details of theseexperimental catchments can be found in Appendix A.
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY3
2. Paired catchments
Paired catchment studies have been widelyused to assess the likely impact of land usechange on water yield around the world.Such studies involve the use of twocatchments with similar characteristics interms of slope, aspect, soils, area,precipitation and vegetation located adjacentto each other. Following a calibration period,where both catchments are monitored, oneof the catchments is subjected to treatmentand the other remains as a control. Thisallows the climatic variability to be accountedfor in the analysis. The change in water yieldcan then be attributed to changes invegetation. The paired catchment studiesreported in the literature can be divided into four broad categories:
(i) afforestation experiments;
(ii) regrowth experiments;
(iii) deforestation experiments; and
(iv) forest conversion experiments.
2.1 Methods used todetermine annual changesin water yield
Various methods have been applied in theanalysis of paired catchment data to assessthe impacts of vegetation changes on wateryield a various time scales. The mostcommonly used method is to produce a linearregression between the control and thetreated catchment for annual data collectedduring the calibration period (Hornbeck et al.1993). The regression equation is then used topredict the water yield that would haveoccurred in the treated catchment if thetreatment has not taken place. The differencein the observed and the predicted streamflowis then assumed to be due to land usechange as the method provides a control overclimatic variability (Bari et al. 1996). While themethod of linear regression is most commonlyused on annual data it has also been used onthe components of streamflow, the quick flowresponse and baseflow (Bari et al. 1996).
South Africa has a very comprehensive set ofpaired catchment studies that have been
used to assess the impacts of afforestationon water yield. A significant amount ofliterature is available on these catchments anumber of different methods have been usedto assess the impacts of afforestation onwater yield at an annual scale. The latestSouth African work is summarised in Scott et al. (2000) and provides details of all theafforestation experiments undertaken inSouth Africa. To predict the impacts ofafforestation on annual streamflow and thevariations on between years due todevelopment of plantations, Scott and Smith(1997) developed an empirical model thatpredicts the percentage reduction in wateryield with time. This work is further discussedin Section 4.
2.2 Methods for determiningseasonal changes in water yield
Seasonal or monthly analysis of pairedcatchments data is less common than annualanalysis. As with annual analysis the mostcommonly used method is to use standardlinear regression techniques on monthly data(making no adjustments for the serialcorrelation) to establish pre-treatmentrelationships between the control and thetreated catchments. Lane and Mackay (2001)adopted this method in their analysis of datain the Tantawangalo Creek catchments inNew South Wales as insufficient data wasavailable during the pre-treatment year to useannual data to develop the relationships.Scott and Lesch (1997) also used monthlydata in their analysis of the Mokobulaanexperimental catchments in South Africa. Toadjust for the serial correlation of monthlydata both streamflow and rainfall data wereincluded as independent variables in amonthly multiple regression. The rainfall termwas considered as part of an antecedentwetness index, which considered thewetness index for the previous month andthe rainfall in the present month. The analysisof Scott and Lesch (1997) looked at annualflows as well as wet and dry season flows.Watson et al. (2001) developed an improved
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY4
method to assess the water yield changesfrom paired catchment studies and appliedthese to the Maroondah experimentalcatchments in Victoria, Australia. Theyargued that the short pre-treatment periodsin most paired catchment studies, limits thestrength of the regression analysis andrecommended that monthly data with anexplicit seasonal component should be used.In a number of studies in south WesternAustralia, only three years of pre-treatmentdata have been used to generate the linearregression on annual flows (Ruprecht andSchofield 1989), casting doubt on thestrength of the correlation gained. Theadvantage of using monthly data is that thereare 12 times as many data points, than in theanalysis of annual data (Watson et al. 1999).However it is important to note that while the use of monthly data represents moreinformation if the serial correlation is treated,it does not represent 12 times the annual data.
2.3 Types of pairedcatchment experiments
The paired catchment experiments reviewedby Bosch and Hewlett (1982), Whitehead andRobinson (1993), Sahin and Hall (1996) andStednick (1996) focused mainly on regrowthexperiments, where harvesting of forests isundertaken followed by the regrowth of thesame vegetation type. While the activitiesinvolved in regrowth of vegetation may impacton the short-term water yield, permanentvegetation changes such as afforestation anddeforestation are likely to have a muchgreater long-term impact on streamflow andthe associated issues, such as salinity andwater resource security.
The paired catchment experiments reviewedin this report can by divided into four broadcategories.
1. Afforestation experiments—conversion of sparsely vegetated land to forest.Examples of these can be found in SouthAfrica (Scott et al. 2000), New Zealand(McLean 2001), Australia (Hickel 2001)and in the UK (Kirby et al. 1991, Johnson 1995).
2. Regrowth experiments—these look at theeffects of forest harvesting where regrowthis permitted. Experiments in this categoryconstitute the majority of the pairedcatchment studies worldwide. Theyinvolve the removal of vegetation from apercentage of a catchment followed byregrowth of the same vegetation type(Stednick 1996).
3. Deforestation experiments—the clearing ofdensely vegetated land to grass orpasture. Examples include the Colliecatchments in Western Australia(Ruprecht and Schofield 1989, Ruprechtand Schofield 1991a, Ruprecht andSchofield 1991b, Ruprecht et al. 1991,Schofield 1991).
4. Forest conversion experiments—thereplacement of one forest type withanother. This includes the conversion fromsoftwood to hardwood, deciduous toevergreen or pine to eucalypt. StewartsCreek provides an example of theconversion of native vegetation to pine inVictoria, Australia (Mein et al. 1988,Nandakumar 1993).
Vertessy (1999) highlighted some of theproblems with using regrowth experiments forestimating yield increases. Where a forestsare permitted to regenerate only the dataobtained in the first few years followingtreatment are used in building relationshipsbetween the percentage change in cover andthe change in yield. Three problems werehighlighted in relation to the use of such data:
• it takes time for a catchment to adjust itsrun-off behaviour following vegetationchange
• soil compaction and disturbance duringlogging and regeneration burning cantemporarily increase overland flow andchange the pattern of streamflow
• due to the short data set used to buildthe linear relationships that are used topredict water yield change, naturalvariability in the water yield data due toclimatic variability may have a stronginfluence on the results.
The results of various paired catchmentexperiments are discussed in Section 4,following a review of the major hydrologicalprocesses in Section 3.
3. Hydrological processes inrelation to vegetation type
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY5
The effects of two main vegetation types on components of the water balance arediscussed in the following section. The twomain vegetation types considered are grassor pasture and forests.
The water balance equation for a givencatchment can be written as:
P = ET + Q + D + ∆S (1)
where P is the precipitation, ET is the actualevapotranspiration, Q in the streamflow, D isthe recharge to the ground water and ∆S isthe change in soil water storage.
The evapotranspiration and streamflow termsin equation (1) can be rewritten as
ET = I + T + E (2)
where I = interception loss, T = transpirationand E = soil evaporation;
Q = OF + BF (3)
where OF = overland flow and BF = baseflow
The hydrological processes and waterbalance components discussed in relation to vegetation types are precipitation,evapotranspiration, interception, transpiration,soil evaporation, infiltration, overland flow,deep drainage, baseflow and recharge.
3.1 Precipitation
Precipitation is the largest term in the waterbalance equation and varies both temporallyand spatially (Zhang et al. 2001). Indiscussing the impact of vegetation type onprecipitation it is important to distinguishbetween gross precipitation and netprecipitation. Gross precipitation is theamount of rainfall (or snow) falling above thevegetation, while net precipitation is theamount of precipitation reaching the ground
surface. In most cases gross precipitationcan be assumed to be independent ofvegetation type (Calder 1998).
Calder (1999) suggests one of the mythsassociated with forests is that forestsincrease precipitation. In most cases it isreasonable to assume that vegetation haslittle or no influence on gross precipitation.However, in some instances there is evidencethat forests increase the amount of grossrainfall. It has been suggested that tall treesincrease the orographic effect, increasing theamount of gross rainfall. However anyincreases in gross rainfall are likely to beoffset by the increased rate ofevapotranspiration of these taller trees,resulting in an overall decrease in waterresources. On a continental scale it isthought that vegetation type may well impacton the amount of gross precipitation throughland-atmosphere feedbacks (Calder 1996).However, there is no data to show that thiseffect operates at the catchment scale.
3.2 Evapotranspiration
As described in equation (2)evapotranspiration can be divided into threecomponents: interception, transpiration andsoil evaporation. Evapotranspiration isdefined as the total process of water transferinto the atmosphere from vegetated landsurfaces. The two major components ofevapotranspiration (transpiration andinterception) are defined and discussed inSections 3.2.1 and 3.2.2. The total amountof evapotranspiration, under differentvegetation types is dependent not only onthe vegetation type, but also on the soil andclimate of the catchment (Calder 1999).
Changes in evapotranspiration due tochanges in vegetation can have a significantimpact on the water balance. For examplewhen comparing the annualevapotranspiration between forest and grassfor catchments with the similar rainfall,
Turner (1991), Holmes and Sinclair (1986)and Zhang et al. (1999) found that forestsconsistently had higher rates ofevapotranspiration than grass.
3.2.1 Transpiration
Transpiration is the process by which waterin plants is transferred to the atmosphere inthe form of vapour (Ward and Elliot 1995).The amount of transpiration differs withdifferent vegetation types and is controlled bythe physiological characteristics of thevegetation, with the majority of transpirationoccurring through the stomates, the smallpores in the leaf epidermis. The combinedeffect of large leaf area and more extensiveroot systems of forests compared to grass orpasture results in much greater transpirationrates (Ward and Elliot 1995). This largertranspiration rates of forests compared topasture is not only due to the increased leafarea, but is also due to the ability of forest toaccess deeper water stores.
3.2.2 Interception
Interception loss is the amount of gross rainfallintercepted by leaves or litter and evaporateddirectly back to the atmosphere. Water that iscaptured on foliage and evaporated does notcontribute to streamflow. Interceptions can bedivided into two types:
1. canopy interception
2. litter interception.
The amount of interceptions is largelydependent on the type of vegetation and onthe intensity, duration, frequency and form ofprecipitation (Dingman 1994). Interception isgenerally proportional to leaf area index (LAI)with forests having a larger LAI then shrubsor grasses. This combined with the greateraerodynamic roughness of forests leads togreater interception loss and reduction in net rainfall under forested conditions(Vertessy 2000).
Using a paired catchment study to determinethe impact of replacing native vegetation withpasture on a small catchment in southWestern Australia, Ruprecht and Schofield(1989) attributed the initial increase instreamflow (~13% of rainfall) to an decreasein the interception loss as a result of changesin vegetation type. Bari et al. (1996) also
observed this response in the March Roadcatchment in south Western Australia. Thedifference in interception between forest andgrass or pasture impacts on the waterbalance equation by affecting theevapotranspiration.
Afforestation, deforestation and forestconversion are all likely to alter the waterbalance through their influence on LAI andinterception loss.
3.3 Infiltration
Infiltration is the process by which waterarriving at the soil surface (after canopy andlitter interception) enters the soil (Dingman1994). The rate of infiltration is affected bythe initial water content and the permeabilityof the soil. Although the antecedent moistureand permeability are largely determined byrainfall and soil type, vegetation also affectssoil moisture levels through transpiration,interception and shading. Soil permeability is also impacted by vegetation through thecontribution of organic matter and number ofmacro and micro-pores that develop aroundthe roots.
The higher transpiration rates of forestsresults in the initial soil moisture contentbeing considerably lower than under crops orpasture (Ruprecht and Schofield 1989) whilethe nature of the root structure associatedwith forests increases the number of macro-pores and the amount of organic mattercontent under forested conditions leads tohigher rates of infiltration under forests thanunder grass or pasture (Mapa 1995).
3.4 Overland flow
Overland flow occurs when the soil issaturated either from above (Hortonian overland flow) or from below (saturation overlandflow) (Dingman 1994). Greater overland flowis generally observed from pastures thenfrom forests. Ruprecht and Schofield (1989)attributed increases in overland flow indeforested catchments to the largerpermanent groundwater discharge areas thatresulted as a consequence of vegetationremoval and decreased evapotranspiration. It is also possible that increased overlandflow occurs for pastures relative to forestsbecause of the changes to the infiltration
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY6
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY
capacity of the soil. Scott and Lesch (1997)noted that the delayed recovery ofstreamflow after the clear felling of aplantation catchment in South Africa. Theyattributed the delayed response to the treestapping deep soil-water reserves reducingthe soil water storage below levels necessaryto generate streamflow, indicating that asaturation excess flow mechanism may alsooperate in this catchment.
3.5 Deep drainage, base flowand recharge
Deep drainage is the water that movesdownward through the soil profile below theroot zone that cannot be used by transpiredby plants (Ward and Elliot 1995). Where thereis no lateral flow of water between the rootzone and the water table, deep drainage isequivalent to the recharge to the groundwater table. Where lateral flow occursbetween the root zone and the water table aportion of deep drainage may contribute tobaseflow. Baseflow can therefore consist oftwo components: the discharge from thegroundwater table; and the lateral flow ofdeep drainage that becomes streamflow.
Deep drainage is generally greater beneathpastures than forests, as the deeper roots offorests and increased evapotranspirationutilises more water, reducing deep drainage.In an experimental catchment in Coweeta,USA, Burt and Swank (1992) observed thatwith the conversion of hardwood to grass theamount of low flow increased, particularlybaseflow. This has been observed almostuniversally in paired catchment studiesinvolving deforestation. The lowerevapotranspiration rates and shallower rootzones of short vegetation compared toforests results in an increase in the deepdrainage and baseflow.
3.6 Soil water storage
The last term in the water balance equationis the change in soil water storage. The soilwater storage represents the amount ofwater stored in the soil profile that can eitherbe transpired or that contributes to baseflow.Over long periods it is reasonable to assumethat changes in soil water storage arenegligible, if no change to vegetation typehas occurs (Zhang et al. 2001) the change in
soil water storage term in the water balanceequation can be ignored. However, on aseasonal basis the changes in soil waterstorage may be significant.
3.7 Summary of processes
The above discussion highlights the majorhydrological processes in relation tovegetation type. The general conclusions thatcan be drawn are:
1. Alterations to vegetation type on thelocal and catchment scale are not likelyto impact on gross precipitation;however, regional or continentalchanges to vegetation may alter grossprecipitation.
2. Interception loss is greater for forestthan for grasses or pasture. Underdeforestation it is likely that the initialincrease in water yield is due to areduction in interception loss.
3. Overland flow or quick flow is less likelyto be affected by changes in vegetationcover than baseflow.
4. Changes in water yield as a result ofchanges in vegetation, particularlypermanent vegetation changes are likelyto be reflected as changes to baseflow.Larger soil moisture stores andgroundwater reserves accumulated inresponse to removal of forest anddecreased evapotranspiration cause theobserved increase in baseflow.
5. Under most climatic conditions,evapotranspiration from forests will begreater than from grasses.
7
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY8
4. Changes in water yield dueto changes in vegetation type
Water yield changes have been reported atmean annual, annual and monthly temporalscales for paired catchment studies. Themajority are reported on an annual basis. Thefollowing section summarises the results forprevious reviews and uses specific examplesfrom Australia, South Africa and New Zealandto highlight some of the conclusions that canbe drawn from paired catchment studies.
4.1 Generalisations based onpaired catchment data
A number of reviews have been undertakento draw generalisations from pairedcatchment studies, particularly in reference tochanges in forest cover on water yield. Thefirst of these was by Hibbert (1967). In thisreview 39 experimental catchments werereviewed and the following conclusions weredrawn:
• reduction in forest cover increases wateryield
• establishment of forest cover onsparsely vegetated land decreaseswater yield
• the response to treatment is highlyvariable and, for the most part,unpredictable.
Bosch and Hewlett (1982) undertook at furtherreview of paired catchments and in reviewing94 experimental catchments, they concluded:
1. reducing forest cover causes as increasein water yield
2. increasing forest cover causes a decreasein water yield
3. coniferous and eucalypt cover types cause~40mm change in annual water yield perten per cent change in forest cover;
4. deciduous hardwoods are associatedwith ~25mm change in annual water yieldper ten per cent change in cover;
5. brush and grasslands are associated witha ~10mm change in annual water yieldper ten per cent change in cover;
6. reductions in forest of less than 20%apparently cannot be detected bymeasuring streamflow
7. streamflow response to deforestationdepends on both the mean annualprecipitation of the catchment and on theprecipitation for the year under treatment.
Figure 1: Yield increases following change in vegetation cover (after Bosch and Hewlett 1982). The points representthe maximum annual increase in water yield during the first five years after treatment for cover reduction experimentsand maximum decrease (within the time frame of the experiment) in water yield for afforestation experiments.
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY9
Figure 2: The distribution of water yield change after clear cutting of conifer and scrub (scaled to 100% reductionin cover), as a function of mean annual precipitation (after Bosch and Hewlett 1982).
Figure 1 shows the results of the Bosch andHewlett review relating the maximumincrease in water yield during the first fiveyears after treatment to percentage reductionin cover and the cover type. Figure 1illustrates that with an increase in thepercentage reduction in cover an increase inannual streamflow occurs.
To explain some of the within group variabilityevident in Figure 1, the results of pairedcatchment studies involving scrub andconifers were scaled to predict the wateryield increases that would have occurred if100% of catchments had been cleared andthese increase were plotted against the meanannual rainfall for the catchments (Figure 2).
From this work Bosch and Hewlett (1982)concluded that:
• water yield changes are greatest in highrainfall areas
• the effect of clear cutting is shorter livedin high rainfall areas due to the rapidregrowth of vegetation
• the annual change due to treatment inhigh rainfall areas appears to beindependent of the variation in rainfallfrom year to year
• changes in water yield are morepersistent in drier areas because of theslow recovery of vegetation, and arerelated to the precipitation in during theyear following treatment.
In order to include afforestation experiments in their analysis, Bosch and Hewlett (1982)assumed that the maximum decrease in wateryield was analogous to the first year increasein water yield for a deforestation experiment.This allowed general conclusions to be drawn.The use of maximum increase in water yield inthe first five years after treatment may alsointroduce bias into the results as themaximum is likely to be affected by climate.
The reviews of Hibbert (1969) and Bosch andHewlett (1982) mainly focused on catchmentsfrom temperate zone. Bruijnzeel (1988) lookedat the impacts of vegetation changes on wateryield, particularly dry season flows in thetropics. From this work it was concluded that:
• surface infiltration andevapotranspiration associated with therepresentative types of vegetation play akey role in determining what happens tothe flow regime after forest conversion
• if infiltration opportunities after forestremoval decrease to the extent that theamount of water leaving an area asquick flow exceeds the gain in baseflowassociated with decreasedevapotranspiration, then diminished dryseason flows will result
• if surface infiltration characteristics aremaintained the effect of reducedevapotranspiration after clearing willshow up as an increase in baseflow
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY10
• the effect of reforesting will not onlyreflect the balance between changes ininfiltration and evapotranspiration, butwill also depend on the available waterstorage capacity of the soil.
The conclusion that under deforestationeither a decrease or an increase in wateryield may occur, seems to conflict with manyof the results of paired catchment studies intemperate zones, in which increases inbaseflow is almost uniformly observed(Hornbeck et al. 1993).
Reviews by Stednick (1996) and Sahin and Hall (1996) expanded on the work byBosch and Hewlett (1982). Stednick (1996)reviewed result of studies from the UnitedStates and looked only at annual water yieldchanges as a result of timber harvesting.Their main focus was on the effect of thepercentage of area treated the ability todetect changes in streamflow. Differenthydrologic areas were defined based ontemperature and precipitation regimes and it was concluded that:
• in general, changes in annual water yieldfrom forest cover reductions of less than20% of the catchment could not bedetermined by streamflow measurement
• the rationalisation of data suggested thisvalue might change depending on thetemperature and precipitation of thearea. With a measurable increase instreamflow being observed fortreatments of 15% of the catchmentareas in the Rocky Mountains,compared with the Central Pains wheretreatment is required over 50% of thearea before changes in water yield canbe detected.
Sahin and Hall (1996) used a similarapproach to Bosch and Hewlett (1982) inthere analysis of 145 experimentalcatchments, dividing the vegetation typesinto broad categories (hardwood, conifer,conifer-hardwood, eucalypts, rainforest,scrub and grassland). However, instead ofusing the maximum increase in water yield inthe first five years after treatment, they usedthe average water yield changes in up to thefirst five years after treatment. Using fuzzylinear regression analysis they concluded thatfor a ten per cent reduction in:
• conifer-type forest, water yield increasedby 20-25 mm
• eucalypt forest, water yield increased by 6 mm
• scrub, water yield increased by 5 mm
• deciduous hardwoods gave a 17-19 mm increase in water yield.
These estimates are lower than those fromBosch and Hewlett’s (1982) review. In theBosch and Hewlett (1982) analysis adoptedthe maximum change in water yield in thefirst five years after treatment, this will leadhigher estimate of the reduction in water yieldas opposed to the same analysis performedwith average increases. However the use ofthe average of up to the first five years aftertreatment may be impacted by regrowth ofvegetation after clearing.
The results of these reviews are limited bythe use of regrowth experiments to buildrelationships about annual increases in wateryield after vegetation change. As discussedin Section 2.3, Vertessy (1999) highlights thelimitations associated with the use ofregrowth experiments for developingrelationships between percentage ofvegetation cover and water yield. The resultsof subsequent studies, that look at change inwater yield as a function of vegetation agehave shown that the maximum change inwater yield may not occur in the first fiveyears after treatment. The results from apaired catchments studies in mountain ashforests of Australia indicate that themaximum water yield changes, when oldgrowth forest is replaced by regrowthvegetation is not see until approximately 20years after treatment as shown by Kuczera(1987). The vigorous regrowth in mountainash forests will in fact cause a decrease inwater yield compared to old growth forests.This concept is further discussed in Section 4.2.
In reviewing paired catchment studies bothStednick (1996) and Sahin and Hall (1996)concluded that in summarising the result ofcatchment experiments, difficulties wereexperienced because of the lack of certainkey statistics from the reported results (Sahinand Hall 1996) or insufficient detail of the sitecharacteristics (Stednick 1996). This mayaccount for the lack of general discussionabout the impacts of land use change on
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inter-annual water yield (the change in wateryield with change in vegetation age) andseasonal flows. While the informationcontained in previous reviews may be usefulfor determining the short-term changes inwater yield, it does not allow for the likelylong-term impact of permanent land usechange or the inter and intra annual changesto be investigated.
Taking these factors into account and thelimitations of regrowth experiments (Section2.3) the remainder Section 4 considers theimpact of vegetation changes on water yieldand flow regime at different temporal scales.
4.2 Mean annual and annualwater yield
The main process responsible for changes inwater yield as a result of vegetation changesat the mean annual scale isevapotranspiration (Zhang et al. 2001,Holmes and Sinclair 1986, Turner 1991).Holmes and Sinclair (1986) used therelationship between mean annualevapotranspiration and mean annual rainfallto predict the increase in water yield whenconverting from a forested catchment tograss. Their results were based on a series ofcatchments in Victoria, Australia. Asdiscussed in Section 3, when assessing themean annual changes in water yield therecharge and change in storage terms in thewater balance are small compared to theother terms, hence the change in runoff canbe predicted through the prediction ofchange in evapotranspiration.
The concept that under mean annualconditions it is reasonable to assume the thatrecharge and change in soil water storage arenegligible compared to the rainfall, streamflowand evapotranspiration was further explored byZhang et al. (1999, 2001). They expanded onthe work by Holmes and Sinclair (1986) andincluded results from 250 studies worldwide asopposed to a small number of localcatchments. Using a pair of curves to illustratethe difference in evapotranspiration underdifferent vegetation types along a rainfallgradient, Zhang et al. (2001) developed asimple two parameter model to estimate themean annual evapotranspiration at thecatchment scale for different vegetation types.Figure 3 shows the Holmes Sinclair
relationship (HSR) and the Zhang model. Thedifference between the grass and forest curverepresents the increase in mean annual wateryield for a 100% change in vegetation for agiven mean annual rainfall. It should be notedthat both paired catchments and time-trendstudies were used in the derivation of Zhangcurves.
Vertessy and Bessard (1999) adapted the HSRto predict the impact of afforestation on wateryield in the Middle Murrumbidgee basin.Equations, based on the HSR were defined toestimate the mean annual runoff fromgrassland and eucalypt forest and were alsoadapted to predict the mean annual ET of pineplantations. These were then used in predictthe large scale impacts of afforestation onwater yields.
The nature of most paired catchment studiesdoes not allow for the long term effects (>10 years) of permanent vegetation changesto be investigated. While the mean annualresults based on the HSR or the Zhangmodel provide a means to assess the impactof permanent land use changes on meanannual flows, they do not provide a methodfor the assessment of inter-annual variabilityor the length of time it takes for a catchmentto adjust to changes in vegetation type.Using paired catchment data Hornbeck et al.(1993) looked at the long term effects of
Figure 3: Relationship between land cover, meanannual rainfall and mean annual evapotranspiration,as predicted by Holmes and Sinclair (1986) andZhang et al. (2001). Note the HSR is based on localcatchments mainly in Victoria, Australia, while theZhang Model is based on a worldwide database.
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forest treatment on water yield in the USAunder a range of climatic conditions. Theyfound a variety of responses in water yieldincluding:
• initial increases occurring promptly afterforest clearing
• increases could be prolonged bycontrolling the regrowth (analogous withpermanent land use change), whenregeneration of forest cover waspermitted the increase in streamflowdiminished rapidly in about three to ten years
• a small increase or decrease in wateryield may persist for at least a decade.
Figure 4 shows the impact of vegetationchanges for four catchments in the USA. The differing responses are consistent withthe treatments undertaken for example in theHubbard Brook experimental forest (HB2),100% of the catchment was clear-cut andregrowth was then permitted. In this case aninitial increase in water yield is observed (dueto reduced interception and transpiration), as regrowth are permitted the water yieldincrease is reduced. The observed reductionin water yield about 15 years after treatmentis due to the increased evapotranspiration ofthe regrowth compared to the old growth
forest. For the Fernow experimental forest(F7) the increase in water yield is morepersistent than in Hubbard Brook. In the F7catchment clearing was undertaken in twostages, with the clearing of the upper half ofthe catchment in year 0 and the clearing ofthe lower half the catchment in year 4.Herbicides were applied to the catchment toprevent regrowth until year 7. After this pointthe effect of the regrowth on water year canbe seen with water yield returning to pre-treatment levels by year 27 (Hornbeck et al.1993).
While the regrowth experiments of the typesshown in Figure 4 are useful for looking atthe initial increase in water yield and the timetaken for a catchment to return to its pre-disturbance state. It provides very limitedinformation on the long-term impact ofpermanent vegetation changes that mayoccur under deforestation or afforestation,where the water yield will not return to itspre-treatment state.
There are limited examples of pairedcatchment studies looking at the impact ofpermanent land use changes on water yield.A number of paired catchments studied insouth Western Australia have focussed on thedeforestation of native forest for agriculturalland. Figure 5 shows the results of four
Figure 4: Change in annual water yield for four paired catchment studies in the USA. M4–100% Basal area cut.F7, upper half clear-cut (year 0), herbicides on upper half (2-7), lower half cut (year 4), herbicide on entirecatchment (5-7). LR2–Lower 24% clear-cut (year 0), mid slope 27% clear-cut (years 4-5), herbicide on lower andmid slope (Year 7) 40% Upper slope clear-cut (year 8-9), herbicide all catchment (Year 10). HB2–100% clear felled(Year 0), herbicide on entire catchment (Years 2-4). After Hornbeck et al. (1993).
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different paired catchments in the Collie Basinin Western Australia. The catchments in theCollie Basin, experience a Mediterraneanclimate with a mean annual precipitationranging from 600 to 1400 mm. Predominatepre-treatment vegetation in these catchmentsare jarrah (Eucalyptus marginata) in the northand karri (E. diversicolor) in the south. March Road, Yarragil 4L, Wights and Lemonscatchments have mean annual rainfalls of 1050 mm, 1120 mm, 1200 mm, 750 mmrespectively.
Looking at the results for the deforestation inthe Wights catchment, it can be seen that aninitial increase in water yield is observed inthe first year after treatment (due todecreased interception andevapotranspiration). This is followed by asteady increase in water yield until a newequilibrium is reached (Ruprecht andSchofield 1989). The results of clearingfollowed by regrowth in the March Roadcatchment show a similar tend to theregrowth in Hubbard Brook Catchment 2USA (Figure 4), with an initial increasefollowed by a return to pre-treatment levels.
This highlights the limitations of regrowthstudies in predicting the long-term effects of deforestation as the initial increase afterclearing are not representative of the long-term increases in water yield. However,regrowth experiments have the potential to be used to investigate the likely changes
in evapotranspiration and streamflow withrelation to forest age. This has been the focusof a number of paired catchment studies insouth eastern Australia, where after clearingand subsequent regeneration, a decrease inwater yield occurs. This decrease is due tothe vigorous nature of the regrowth, whichtranspires more water compared with oldgrowth forests (Cornish and Vertessy 2001,Vertessy et al. 2001, Roberts et al. 2001).Through the use of paired catchment studiesinvolving regrowth, it may be possible topredict the impact of afforestation or tree‘plantations’ on inter-annual water yield.
The Mountain ash forests in southernAustralia provide an excellent example of thisreduction in water yield flowing theregeneration of vegetation after bushfire.Mountain ash forests are confined to thewetter parts of Victoria and Tasmania andgrow at altitudes of between 200 m and1000 m, where mean annual rainfall exceeds1200 mm. Fire is an infrequent but vitalcomponent of the life cycle of these forestswith the seedlings only growing on exposedsoil with direct sunlight (Vertessy et al. 2001).Following fire hundreds of seeds germinateper hectare, the intense competition betweenthe plants for light results in rapid tree growthand natural thinning of weaker trees. There isa significant body of empirical evidence toshow that the amount of water yield fromthese catchments is closely linked with standage (Langford 1976, Kuczera 1987,
Figure 5: Water yield increase for paired catchments, south Western Australia. Wights catchment (Ruprecht andSchofield, 1989), Yarragil (Stoneman, 1993), March Road (Bari et al. 1996), Lemons (Ruprecht and Schofield, 1991)
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Watson et al. 1999). The ‘Kuczera curve’ thatdescribes the relationship between stand ageand annual water yield is characterised bythe following features:
• the mean annual water from largecatchments covered with old growthmountain ash forest (>200 year) isapproximately 1195 mm for regionswhere mean annual rainfall is ~1800 mm;
• after burning and full regeneration ofmountain ash forest the water yield reducesto 580 mm at an age of ~ 27 years
• after 27 years of age the mean annualwater yield increases and returns to pre-disturbance levels, taking as long as 150 years to fully recover. (Vertessy et al. 2001)
The work by Cornish and Vertessy (2001)and Roberts et al. (2001) indicates that thismay be a more general behaviour foreucalypt forests in Australia and does notonly apply to mountain ash forests.
Examples of long-term response topermanent vegetation change from grass orpasture to tree plantations can be found inSouth Africa, New Zealand and Australia.Figure 5 showed the response of streamflow
to the clearing of native vegetation foragriculture for deforestation and regrowthexperiments in south Western Australia.South Africa has the longest and mostdetailed record of paired catchmentafforestation experiments, addressingpermanent land use change from grasslandto forest. Using data from South Africanafforestation experiments, Scott and Smith(1997) developed a series of generalisedcurves to predict the impact of afforestationon annual total flows and low flows as afunction of plantation age, species plantedand site suitability as shown in Figure 6.
The curves in Figure 6 are similar to thatobserved in Figure 5 (particularly for Wightscatchment), indicating that the response issimilar for both afforestation anddeforestation, with a period of transience untila new equilibrium is reached. Figure 7 showsthe results of a deforestation and afforestationexperiment in areas of similar rainfall. A similar change in water yield, under eitherdeforestation or afforestation in the long-termis observed. The time taken to reach thisequilibrium is dependent on the treatment,with a new equilibrium being establishedmore rapidly under deforestation thenafforestation.
Figure 6: Generalised curves from estimating the percentage reduction in total and low flow after 100%afforestation with pine and eucalypt afforestation (Scott and Lesch 1997).
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The results for the Biesievlei catchment,Jonkershoek, South Africa indicate that it willtake between 15 and 20 years for thecatchment to reach a new equilibrium underafforestation, while the deforestationexperiment from Wights catchments inWestern Australia, indicates that a newequilibrium is reach in eight to ten years. Thiscasts doubt on the use of the assumption ofBosch and Hewlett (1982) that the maximumreduction in water yield under afforestation isequivalent to the maximum increase in wateryield in the first five years after treatments forregrowth and deforestation experiments.
4.3 Annual flow regime
4.3.1 Flow duration curves
The impact of changes in vegetation type onflow regime can be depicted through the useof flow duration curves (FDC). The FDC for acatchment provides a graphical summary ofthe streamflow variability at a given location,with the shape being determined by rainfallpattern, catchment size and the physiographiccharacteristics of the catchment. The shape ofthe flow duration curve is also going to beinfluenced by water resources development(water abstractions, upstream reservoirs etc.)and land-use type (Smakhtin 1999).
The FDC (the cumulative distribution of theriver flows) has been used widely as a measureof the flow regime as it provides an easy way
of displaying the complete range for flows andhow they would be changed under differentland use scenarios in different climatic zones.
FDC can be constructed using multipletemporal scales of streamflow data: monthlyor daily flows and depicted either using allthe flows in a given year (annual flow durationcurve) or flows for subset of yearly flows(seasonal flow duration curve). Smakhtin(1999) adopted the following terminology andthis terminology has been adopted whendiscussion the effect of vegetation changeson the FDC for various vegetation changescenarios:
• One-day annual FDC—Constructed usingdaily data for a complete year
• One-month annual FDC—Constructedusing monthly data for a complete year
• One-day seasonal FDC—Constructedusing daily data for a given season
• One-month seasonal FDC—Constructedusing monthly data for a given season
One of the limitations of using FDC for acomparison of high and low flows underdifferent vegetation types is that the relativedistribution of high and low flows variesdepending on whether a particular year iswet or dry, therefore where possible it isimportant to compare years with similarprecipitation, to minimise the variations dueto climate (Burt and Swank 1992).
Figure 7: Change in water yield as a percentage of rainfall for deforestation (Wights catchment, Western Australia.Mean annual rainfall = 1200 mm after Ruprecht and Schofield 1989), afforestation and clear felling-replanting(Biesievlei catchment, South Africa. Mean Annual Rainfall, 1298mm, Scott et al. 2000).
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4.3.2 High and low flows
In discussing the impacts of vegetationchange on flow regime low and high flowneed to be defined. The most widely useddefinition of low flows are the flows within therange of the 70% to 99% time exceeded(Smakhtin 2001), hence this definition hasbeen adopted. High or peak flows have beentaken as the flows that occur one to ten percent of the time.
4.3.3 Impact of vegetation changes onannual FDC, high and low flows
The flow duration curves discussed below areone-day annual FDC, and have been plotted forcatchments in different climatic zones withdiffering vegetation changes. While data existsto plot such curves for a large number ofcatchment only three examples have beenchosen and discussed here. These examplesare the Redhill catchment in south easternAustralia, where a pine plantation wasestablished on pasture, Wights catchment insouth western Australia where pasture replacednative vegetation and the Glendhu catchmentin New Zealand, where a pine plantation wasestablished on tussock grassland.
Figure 8 depicts the change in flow regimefor the Redhill catchment in south eastern
Australia. The catchment is located in aabout 50 km west of Canberra, in theMurrumbidgee Basin and is part of the pairedcatchment study looking at the impact ofpine plantations on water yield. Redhill has acatchment area of 195 hectares while thecontrol catchment Kylies Run is 135hectares. Both catchments range in altitudefrom 590 m to 835 m. The climate of thearea is highly variable with a winter dominantrainfall. The mean annual rainfall of the Redhillcatchment is 876 mm (Hicke 2001). There isno pre-treatment data available for this pairedcatchment study and due to differences insoil properties between the two catchments,there was also marked difference betweenthe flow regimes even before the pines arewell established at the beginning of thetreatment period. It was therefore decided tocompare the FDC for years of similar annualrainfall for the treated catchment only. FDCfor one and eight year old pines (based on awater year from May to April) have been usedto quantify the relative changes in the highand lows flows as a results of vegetationschange. The one-year and eight-year oldpines were chosen as these years havesimilar rainfalls, 887 mm and 879 mmrespectively. The FDC indicated that there isapproximately a 50% reduction in high flowswhile there is 100% reduction in low flows.
Figure 8: Flow Duration curves for the Redhill catchment, near Tumut, NSW 1 year old pines and 8 year old pines.(after Vertessy 2000).
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Figure 9 depicts the response to conversionof native forest to pasture in the Wightscatchment in south Western Australia. As discussed in Section 4.2 the Wightscatchment is part of a series on pairedcatchment studies in south WesternAustralia. These catchments have twoimportant local characteristics,
• an increasing soil salinity storage withdistance inland; and
• a local groundwater system.
The interplay between the groundwater andvegetation plays in important role in thehydrological response of these catchments tovegetation change. The hydrological responseto replacement of native forests by pastures isrelated to an increase in groundwaterdischarge area (Schofield 1996).
As with Figure 8, it can be seen that allsections of the flow regime are affected bythe change in vegetation type. Comparingthe FDC for native vegetation (1974-1976)with a period of similar climatic conditions ofpasture (1983-1985). We can see that youwould expect a 50% reduction in high flowswhen going to pasture to forest and a 100%reduction in low flows.
Figure 10 depicts an alternate response tothe establishment of pine plantations in theGlendhu experimental catchments in New
Zealand (169˚45’E, 45˚50’S). The control andtreated catchments has mean annual rainfallsof 1310mm and 1290mm respectively. The treatment involved the planting 67% ofthe catchment with Pinus radiata (McLean2001). Unlike the Redhill and Wightscatchments the control and treated FDC forthe control and treated catchments aresimilar during the calibration period.Therefore the changes in high and low flowshave been assessed through comparison thecontrol to the treated catchment at variousstages after treatment. The reductions in lowand high flows as similar for all sections ofthe flow regime with approximately 30%reduction in both low and high flows. Thisresponse is typical of many catchmentsincluding the mountain ash catchments inVictoria (Watson et al. 1999) and theBiesievlei catchment in South Africa.
Figure 8, Figure 9 and Figure 10 depict twopossible responses in flow regime as a resultof vegetation change. The response seen inthe Redhill and Wights catchments are typicalof areas were annual evapotranspiration offorests approaches annual precipitation, whilethe response seen in Glendhu is typical ofareas where annual precipitation is greaterthan the annual evapotranspiration. In theMountain ash catchments in southernAustralia, Watson et al. (1999) noted that inwetter catchments all flows respond to
Figure 9: Flow Duration curves for the Wights catchment in southwestern Australia. (Based on a water year fromApril to March).
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Figure 10: Flow duration curve from the Glendhu experimental catchments New Zealand. 1980—during thecalibration period (both catchments tussock). 1989—6 years after pine plantation established. 1999—16 yearsafter pine plantation established. (McLean 2001)
climatic and vegetation changes in unisonwith the changes in the mean flow, howeverin the drier parts of the study area changes inlow flows are accentuated.
4.4 Seasonal water yield andflow regime
As noted earlier, information on the impact ofland use change on seasonal or monthly flowis seldom reported in quantitative detail in theliterature and generalisations about seasonalityof yield changes under different land use havenot been made. The majority of the previouswork of land use change and water yield hashad an emphasis on annual or mean annualwater yield. The impact of land use change onseasonal yield can be as important as theimpact on annual water yield, particular whereflows during the dry season are of importanceto downstream water users.
Johnson and Kovner (1956) noted thatannual streamflow and evapotranspiration donot tell the complete story because ofseasonal interactions of factors affecting thewater balance, such as soil moisture content.While on an annual basis the changes in soilmoisture between one year and the next canbe assumed to be negligible, this is not thecase on a seasonal basis. This section willaim to provide a summary of the literature onseasonal water yield.
The analysis of paired catchment data in theUSA in the 1970s and early 1980s commonlyused regression by least squares on bothannual and monthly data (Hibbert 1969,Hornbeck et al. 1987, Rich and Gottfried1976, Johnson and Kovner 1956). Thisallowed for the impact of annual water yieldas well as seasonality to be assessed. Theresults of these studies indicate thatvariations can be found in seasonal yield butlack quantitative data on the water yieldchanges. This lack of quantitative datamakes it difficult to generalise the results onseasonal water yield between sites.
Hornbeck et al. (1997) looked at annual andseasonal flows for the first year after clear fellingin the Hubbard Brook experimental forest.Separating annual yields into growing anddormant seasons allowing contrasting oftreatment effects between periods of full leafand maximum evapotranspiration, and periodwhen deciduous forests are dormant andminimum evapotranspiration. They observedthat most of the increases and decrease inannual yield occur during the growing seasonas shown in Figure 11. They concluded thatwater yield increases were a result ofdecreased transpiration and primarily occurredas augmentation to low flows, as illustrated bythe flow duration curves in Figure 11. While thisanalysis is an obvious thing to do for deciduouscatchments the definition of seasons is lessobvious for evergreen vegetation.
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Using a similar approach to the analysis ofHornbeck et al. (1997), McLean (2001)produced flow duration curves to assess the hydrological response during Winter(July–September) and Summer(December–February) due to the conversionof tussock to pine plantations in New Zealand.From this study it was concluded that:
• the differences in summer flows weremore variable than the winter differences,due to the high variability in the rainfallover the summer months
• the seasonal effects of land usemodifications are not easily identifiedthrough the use of flow duration curves.
The difference in the results between theUSA catchment, where notable seasonaldifferences were observed, and those in NewZealand, where seasonal changes could notbe detected, can be attributed to thedeciduous nature of the vegetation in theUSA compared with the evergreen vegetationof the pine plantations in New Zealand. Thedistinct dormant season in the USA wherethere are no leaves on the trees results inlower interception and transpiration ratesmaking the evapotranspiration rates offorested areas very similar to those of shortcrops. However where there is no dormantseason, such as in eucalypt or pineplantations, the seasonal changes in wateryield are limited more by climatic conditions
of the seasons than by changingevapotranspiration rates.
Sharda et al. (1998) used a monthly averagedataset to look at the seasonal nature ofwater yield changes at Glenmorgan ResearchFarm, south India. It was observed that themajor reduction in mean annual flow causedby the blue gum plantation occurred duringthe months from July through to October,when 60% of the mean annual rainfalloccurred (Table 1). These results indicatethat the major reductions in flow volumeoccurred during the monsoon (July–October),however the percentage reductions in flowsindicate that significant reductions occur in allmonths of the year. It was also noted thatalthough the reduction in flow in the dryperiod was small on a volume basiscompared to the wet season the percentagereduction in flow is significant in all months.The early and late monsoon periods showdifferent responses in water change yield,which may be related to soil moisturedynamics introducing delays in response time.
Figure 11: Flow duration curves for the first year after the clear-felling treatment—Hubbard Brook experimentalforest (after Hornbeck et al. (1997)).
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TABLE 1. Average monthly reduction in total run-off due to bluegum plantation in the secondrotation (after Sharda et al. 1998).
Flow in Catchment B (mm)Deficit
Month Rainfall (Computed—Observed) Percentage(mm) Observed Computed (mm) reduction in flow
Apr 71.3 3.8 4.9 1.1 22
May 111.1 7 9 2 22
Jun 166.4 16.3 21.8 5.5 25
Jul 233 60.9 78.6 17.7 23
Aug 221.2 61.3 78.4 17.1 22
Sep 133.6 27.3 37.7 10.4 28
Oct 165.1 40.4 58.6 18.2 31
Nov 70 24.1 33.8 9.7 29
Dec 64.9 20.5 27.9 7.4 27
Jan 9.9 7.4 9.6 2.2 23
Feb 5.9 3.9 4.6 0.7 15
Mar 17.9 3.2 4 0.8 20
Similar analysis was carried out on the Glendhucatchment in New Zealand and the Cathedral
Peak II catchment in South Africa. The resultsof this analysis are presented in Figure 12.
Figure 12: Average monthly reductions in streamflow from the Glendhu catchment (afforestation with pines 1980)and Cathedral Peak II, South Africa (afforestation 1950-1955).
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Other studies reporting monthly or seasonalresults include;
• Lane and McKay (2001) who concludedthat there was no clear or persistentseasonal influence on either totalstreamflow or base flow change followinglogging
• Ruprecht et al. (1991) observed that themajor increase in streamflow occurredfrom July to October, however increaseswere still significant in June, Novemberand December. After thinning there wassignificant flow over the summer and earlyautumn
• Stoneman (1993)—observed that thelargest increases in streamflow occurredbetween June and October (Winter) withsmall increases in November andDecember (Summer).
As stated by Vertessy (1999), the informationon the seasonal variations in water yield islimited and rather confusing. The way inwhich the data on seasonal yield arepresented in the literature is generallydescriptive in nature, making it hard togeneralise between the results of differentstudies. While on an annual basis the resultsof the studies seem to be easily generalisedaccording to vegetation type, this is not thecase on a seasonal basis.
Jones and Grant (2001a, 2001b) noted thatthe nature of the analysis undertaken couldimpact on the results. This was displayed bythe original analysis of peak flow responsesto clear cutting and roads in small and largebasins, western Cascades (Jones and Grant1996) and the subsequent reanalysis of thesame data by Thomas and Megahan (1998)where the use of differing methods on thesame data set yielded different results. Theinterpretation of the results from the twoanalyses has resulted in Jones and Grant(2001a, 2001b) concluding both analysesshowed that forest harvest has increasedpeak discharges in small basins by as muchas 50% and 100% in large basins. Thomasand Megahan (2001) agreed that peak flowincreases (of up to 100%) in small eventsmay occur, but argued that that no evidenceexisted to suggest that this was the case forall event sizes including large floods.
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5. Summary of limitation of paired catchments studies
In the following section the limitations ofpaired catchment experiments are discussed.These limitations are divided into twosections. The first section summarises thelimitations in the analysis of paired catchmentdata in terms of what has been reported inthe literature. The second deals with thelimitations associated with the application ofpaired catchment results to time trend studies.
5.1 Limitations in reportedliterature
During the review of literature three majorlimitations were highlighted in relations to theprevious analysis of paired catchment data.These have been disused in Section 4and are:
• generalisations about annual increases inwater yield (Bosch and Hewlett 1982,Stednick 1996, Salin and Hall 1996) aregenerally only based on short term resultsof regrowth experiments (maximumchange in the first five years aftertreatment, or first year increases). Theresults of permanent land use changeexperiments indicate that it may takelonger than five years for the maximumchange to be observed and for a newhydrologic equilibrium to be established
• changes in vegetation type will effect notonly mean annual flow, but also thevariability of annual flow. Peel et al. (2001)noted that the continental differences inthe variability of annual runoff were due totwo factors, the continental differences inthe variability of annual precipitation andthe distribution of evergreen anddeciduous vegetation
• most studies do not evaluate theseasonal changes in water yield. Where seasonal analysis is carried out the results reported are generally of a descriptive nature
• in order to assess the impacts ofvegetation changes on seasonal wateryield, a method needs to be established
that can be applied to a large number ofcatchments, so when comparing resultsbetween sites, the generalisations are notcomplicated by conflicting results fromdifferent analysis methods.
5.2 Application to largecatchments
The major advantage of using pairedcatchment studies in investigating the impactof vegetation changes on water yield is thatthe control catchments provide a means ofseparating out the changes in yield as aresult of climate from those due to land use.However, if the results of small experimentalcatchments cannot be applied to larger non-paired catchments with some degree ofconfidence, then their application is limited.
5.2.1 Spatial issues
Paired catchment studies provide a goodmethod for determining the relationshipsbetween percentage vegetation change andwater yield in relatively small catchments.However, methods are needed for scalingthese results to larger catchments where thearea of subject to land use change is likely tobe patchy and relatively small compared tothe overall catchment size.
The results summarised in Section 4 indicatethat for any impact of land use change to bedetected, at least 20% of the catchmentneeds to be treated (Bosch and Hewlett1982). This result is derived from the researchon small experimental catchments. Munday et al. (2001) developed a model to simulatethe temporal changes in streamflowassociated with reafforestation of existinggrassland and the subsequent managementof the forest for timber harvesting for theAdjungbilly catchment (389 km2) in New SouthWales using results from paired catchmentstudies of Redhill (for pine plantations) andKaruah (for eucalypt forest). The resultsindicated that while the trend in streamflow
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY23
changes are statistically insignificant, themodel did satisfactorily simulate themagnitude and nature of the changes in meanannual yield from the catchment given thehistorical changes in vegetation type.
In determining the impact of forest conversionto agriculture in a large river basin in Thailand,Wilk et al. (2001) commented that the resultsof small scale studies have shown that a largereduction in forest cover increases the annualstreamflow and raised the issue of whethersimilar results would emerge from large, partlydeforested catchments with variable vegetativepattern at different growth stages. Their studyconcluded that despite a reduction in forestcover from 80% to 30% in the Nam Pong riverbasin, no change in river discharge could bedetected. This is likely to be due to the factthat land use change in large river basins is notuniform in space or time.
At present the impacts on water yield at thewhole of catchment or regional scale arelimited to mean annual investigations. Scottet al. (1998) used the generalised curves ofScott and Smith (1997) to determine thelikely change in water yield on total run-offand low flows at regional scale as a result ofafforestation in South Africa. This is the bestexample of prediction of water yield changesat a regional scale.
There are limited examples of theextrapolation of the generalisations gainedthrough the used of small experimentalcatchments to the regional scale and howtreatments over less than 20% of thecatchment impact on water yield. In terms ofmaking predictions it is reasonable toassume that 0% land use change will notcause any change in water yield. Thusforcing the linear relationships suggested byBosch and Hewlett (1982) thorough theorigin would allow predictions to be made ifless than 20% of a catchment is subjected tochanges in vegetation. The ability to detectthe change in water yield is of lessimportance than how well the magnitude ofthe water yield change can be predicted.Once the predictions have been made theimportance of regional effects can beassessed. Another scale issue that couldpotentially be significant is the change ingeomorphology as you move down fromuplands to lowlands.
5.2.2 Climatic Variability
One of the advantages of paired catchmentstudies is that they allow the removal ofclimate variability through the comparison oftwo catchments subject to the same climaticconditions, under different land uses. Theseparation of climatic variability effects fromthe water yield changes as a result of landuse alterations is a key problem for timetrend studies.
In cases where paired catchments areavailable, the separation of land use impactsfrom climatic factors can be achievedthrough the comparison of the twocatchments. This can be done not only forannual and mean annual totals, but also forflow regime as depicted by the annual flowduration curves in Figure 8, Figure 9 andFigure 10. There is also the potential to usepaired catchments to determine the seasonalimpacts of vegetation change.
For example, in Figure 10 a change in theflow duration curve for the tussock catchment(control), between 1980 and 1989 can beseen, despite the fact that no land use changehas occurred. The most likely explanation forthis is the climate differences between 1980and 1989, causing a change in the amount ofrunoff. Where both the flow duration curves forthe control catchment and the treatedcatchment are available, separation of theimpact of land use on flow regime is possible.However, for Figure 7, where all the flowduration curves have been plotted for thesame catchment, how does one separatequantitatively the changes due to land usefrom the fluctuations due to climate.
Three possible methods that could be usedfor the removal of climatic variability fromnon-paired catchments are:
1. the use of a generalised additive model toseparate the climate signal from land usefrom the percentiles of the flow durationcurves, either annual or seasonal
2. removing exogenous variable so trendscan be more easily identified in thevariable of interest (land use change inthis case)
3. the use of a rainfall runoff model todetermine flows under different land usesfor the same climatic period.
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY24
6. Summary and conclusions
This review highlights the lack of informationavailable in the literature for examining theimpacts of vegetation changes on seasonalyield and flow regime. While the effect ofvegetations change on a mean annual basisis well understood, research on seasonalwater yield reported in the literature is limitedand confusing and is primarily of adescriptive nature.
The processes affected by land use changeare reasonably well understood at a meanannual or annual basis, however changes ona seasonal basis and in flow regime are notas well understood, particularly in relation tosoil water storage. On a mean annual basis,changes in soil water storage can beassumed to be insignificant in relation to theother terms in the water balance equation;however, this is not the case at a seasonaltime scale.
The previous reviews of paired catchmentstudies have focused mainly on regrowthexperiments, where changes in water yield areonly observed in the first couple of yearsfollowing treatment before returning to pre-treatment levels. Given the transient nature ofthe water yield changes in regrowthcatchments the applications of these results topermanent land use changes are questionable.In terms of future land use changes inAustralia, the increase in afforestation due tothe 2020 Vision is likely to be of a morepermanent nature leading to permanentchange in water yield and flow regime.
This review raises a number of issues relatingto land use change and water yield that needfurther investigation. These include:
• Can the results of regrowth studiesprovide relevant information on the effectsof permanent land use change or thelikely changes in evapotranspiration withtime in tree plantations?
• How will the effect of permanent land use change alter over time? Do thegeneralisations made by Scott and Smith(1997) in Figure 6 apply to other areasaround the world?
• How will vegetation changes affect flowregime? Will these effects vary betweenregions or will the major changes in wateryield be reflected in low or high flows?
• Can the generalisations drawn frompaired catchment studies be applied tolarger catchments and at regional scales?
• Can the impacts of climatic variability beseparated from the effects of land usechange?
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Vertessy, RA, Watson, FGR & O’Sullivan, SK 2001, Factors determining relations between stand age and catchmentwater balance in mountain ash forests. Forest Ecology and Management, 143 (1-3), 13-26.
Ward, AD & Elliot, WJ 1995, Environmental Hydrology. Lewis Publishers, New York.
Watson, F, Vertessy, R, McMahon, T, Rhodes, B & Watson, I 2001, Improved methods to assess water yieldchanges from paired-catchment studies: application to the Maroondah catchments. Forest Ecology andManagement, 143 (1-3), 189-204.
Watson, FGR, Vertessy, RA, McMahon, TA & Watson, IS 1999, The hydrologic impacts of forestry on theMaroondah catchments. Report 99/1, Cooperative Research Centre for Catchment Hydrology, Melbourne.
Whitehead, PG & Robinson, M 1993, Experimental basin studies—an international and historical perspective offorest impacts. Journal of Hydrology, 145 (3/4), 217-230.
Wilk, J, Andersson, L & Plermkamon, V 2001, Hydrological impacts of forest conversion to agriculture in a large riverbasin in northeast Thailand. Hydrological Processes, 15 (14), 2729-2748.
Wright, KA, Sendek, KH, Rice, RM & Thomas, RB 1990, Logging effects on streamflow: Storm runoff at CasparCreek in Northwestern California. Water Resources Research, 26 (7), 1657-1667.
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Zhang, L, Dawes, WR & Walker, GR 2001, Response of mean annual evapotranspiration to vegetation changes atcatchment scale. Water Resources Research, 37 (3), 701-708.
Appendix A
A CRITICAL REVIEW OF PAIRED CATCHMENT STUDIES WITH REFERENCE TO SEASONAL FLOWS AND CLIMATIC VARIABILITY31
Kar
uah,
NS
W, A
ustr
alia
Bar
ratta
36.4
1577
258
82Lo
ggin
g w
ithou
tre
gene
ratio
n bu
rn19
76-1
983
Bol
lygum
15.1
1499
250
02Lo
ggin
g w
ithou
tre
gene
ratio
n bu
rn19
76-1
983
Coa
chw
ood
37.5
1444
237
32Pl
anta
tion
esta
blis
hed
afte
r tra
ctor
cle
arin
g19
76-1
983
Cor
kwoo
d41
.116
392
5032
Logg
ing
plus
rege
nera
tion
burn
1976
-198
3
Jack
woo
d12
.513
682
3132
Logg
ing
plus
rege
nera
tion
burn
1976
-198
3
Koka
ta97
.4
450-
9401
Moi
st w
arm
tem
pera
tecl
imat
e.
tall w
etsc
lero
phyll
fore
stR
egro
wth
1562
251
82
Cra
bapp
le/
Sass
afra
s14
.7/
25.3
450-
940m
116
37/1
4292
456/
3072
Plan
tatio
n es
tabl
ishe
daf
ter t
ract
or c
lear
ing
1976
-198
3
Cor
nish
(199
3);
Cor
nish
and
Ve
rtess
y (2
001)
Lids
dale
, NS
W, A
ustr
alia
L-6
9.4
12SW
Sub
Trop
ical
Euca
lypt F
ores
tPi
nus
Rad
iata
755
L-5
Feb
1978
100
% c
lear
edan
d w
indr
owed
, and
then
burn
t in
April
197
8.D
urin
g w
inte
r 197
8ca
tchm
ent w
as p
lant
edw
ith P
. rad
iata
.
1967
-197
8Pu
tuhe
na a
ndC
orde
ry (2
000)
Tant
awan
galo
Cre
ek, N
SW
, Aus
tral
iaW
illbob
85.6
30%
of a
rea
logg
ed19
86-1
989
Lane
et a
l. (2
001)
Wic
ksen
d68
.280
0-95
01
Reg
iona
l rai
nfal
lis
reas
onab
lyun
iform
thro
ugho
ut th
eye
ar.
Ope
nSc
lero
phyll
fore
stR
egro
wth
1100
Ceb
21.7
1100
38%
of a
rea
logg
ed19
86-1
989
Lane
et a
l. (2
001)
Tum
ut, N
SW,
Red
hill
195
9
Tem
pera
te w
ithhi
ghly
varia
ble
and
win
ter
dom
inan
tra
infa
ll.
Past
ure
Pine
s87
6Ky
lies
Run
135
1287
6
50ha
affo
rest
ed in
198
8an
d th
e re
mai
ning
are
a(1
45ha
) affo
rest
ed in
1989
Non
eH
icke
l (20
01)
Aust
ralia
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
1 El
evat
ion
Ran
ge2 P
re-t
reat
men
t mea
n (1
976/
77 to
198
2/83
)
Yam
bula
Sta
te F
ores
t, N
SW
, Aus
tral
ia
Gee
bung
Cre
ek80
.215
7-33
13E
Inte
grat
ed H
arve
st (J
an -
April
198
7)Po
st L
oggi
ng B
urn
(Jun
e-Ju
ly, 1
987)
1979
-198
6
Pepp
erm
int
Cre
ek12
7.5
230-
4763
Wild
Fire
- Ja
n 19
79In
tegr
ated
Har
vest
(Dec
1986
- Ju
ne 1
987)
1977
-197
9
Gre
ville
aC
reek
92.5
Wild
fire
- Dec
197
2W
ildfir
e - J
an 1
979
1977
-197
9
Strin
gyba
rkC
reek
140
230-
4763
Inte
grat
ed h
arve
stin
g -
May
197
8-Ja
ne 1
979)
Wild
fire
Jan
1979
1977
-197
8
Ger
man
sC
reek
225.
123
0-47
63
Tem
pera
te ra
iny
clim
ate,
moi
st in
all s
easo
ns w
ithw
arm
sum
mer
s
Dry
Scle
roph
yllfo
rest
Reg
row
th90
0Po
mad
erris
Cre
ek75
.990
0
Wild
fire
- Jan
197
9Sa
lvage
Log
ging
: Ju
ne- D
ec 1
979
1977
-197
9
Mac
Kay
and
Cor
nish
(198
2);M
oore
et a
l.(1
986)
; Cra
pper
et
al. (
1989
); R
ober
ts(2
001)
; Rob
erts
et a
l.(2
001)
Wyv
uri e
xper
imen
tal c
atch
men
ts, B
abin
da, Q
ueen
slan
d, A
ustr
alia
Nor
th C
reek
18.3
34W
Mes
ophy
ll vin
efo
rest
4239
2873
Sout
h C
reek
25.7
1971
-197
3 67
% a
rea
logg
ed, c
lear
ed ra
ked
and
plou
ghed
; bar
e 2
year
s
1969
-197
1
Cas
sells
et a
l.(1
982)
; Gilm
ore
etal
. (19
82);
Bon
ell e
tal
. (19
83);
Cas
sells
et a
l. (1
985)
Brig
alow
Res
earc
h S
tatio
n, Q
ueen
slan
d, A
ustr
alia
C2
11.7
Cro
ps68
639
C1
16.8
699
20C
ropp
ing
1985
1965
-198
3
C3
12.7
subh
umid
on
the
coas
t and
exte
ndin
g to
sem
i arid
tow
ards
the
wes
t
Nat
iveB
rigal
owfo
rest
.Pa
stur
e69
532
C1
16.8
699
20Pa
stur
e 19
8319
65-1
983
Law
renc
e an
dSi
ncla
ir (1
986)
;La
wre
nce
and
Thor
burn
(198
9)
Cro
pper
Cre
ek, V
icto
ria, A
ustr
alia
Cle
m C
reek
46.4
ED
ry s
cler
ophy
lleu
calyp
t for
est
Pinu
sR
adia
ta14
00El
laC
reek
/Bet
syC
reek
113.
0/4
4.3
E14
00
Dec
embe
r 197
9ve
geta
tion
rem
oved
leav
ing
30m
buf
fer s
trip
arou
nd s
tream
. Are
apl
ante
d w
ith ra
diat
a pi
ne
1975
-197
9B
ren
and
Papw
orth
(199
1)
3 El
evat
ion
Ran
ge
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Nor
th M
aroo
hdah
exp
erim
enta
l Are
a, V
icto
ria, A
ustr
alia
Bla
ck S
pur 1
177.
1SW
Reg
row
th16
62B
lack
Spu
r 49.
817
.216
6250
% B
asal
are
a re
mov
edby
cle
ar fe
lling
smal
lpa
tche
s
71/7
2-75
/76
(4 Y
ears
)
Bla
ck S
pur 2
9.6
14.6
SER
egro
wth
1662
Bla
ck S
pur 4
9.8
17.2
1662
40%
Bas
al a
rea
rem
oved
by u
nifo
rm th
inni
ng1/
7/19
70-
20/1
2/19
76
Bla
ck S
pur 3
7.7
SER
egro
wth
1662
Bla
ck S
pur 4
9.8
17.2
1662
50%
uni
form
thin
ning
(14-
3-19
77 -
2-5-
1977
)1-
7-19
70 -
14-3
-197
7
O'S
haug
hnes
sy e
tal
. (19
89);
Jaya
suriy
a an
dO
'Sha
ughn
essy
(198
8); N
anda
kum
ar(1
993)
; Wat
son
et a
l.(1
999)
; Wat
son
et a
l.(2
001)
Ette
rcon
111
.67
Reg
row
thEt
terc
on 3
15.0
1Ja
n-19
82 to
Mar
198
239
% s
trip
thin
ning
28/7
/197
1-Ja
n198
2
Ette
rcon
28.
83R
egro
wth
Ette
rcon
315
.01
Und
erst
orey
rem
oved
-19
79In
fest
ed w
ith P
syllid
s -
1998
5/8/
1971
-19
79
Ette
rcon
49.
03R
egro
wth
Ette
rcon
315
.01
35%
stri
p th
inne
d Ja
n19
82-M
ar-1
982
15-7
-197
1 -
Jan-
1982
Wat
son
et a
l. (1
999)
;W
atso
n et
al.
(200
1)
Mon
da 1
6.31
Reg
row
thM
onda
46.
31
79%
cle
arfe
lled
and
rege
nera
ted
with
200
0se
edlin
gs /h
a5/
12/7
7-26
/4/7
8cl
earfe
lled
20/3
/78
- bur
nt17
:5-7
8 - S
eedl
ings
11-6
-197
0 -
5-12
-197
8
Jaya
suriy
a an
dO
'Sha
ughn
essy
(198
8); W
atso
n et
al.
(199
9); W
atso
n et
al.
(200
1)
Mon
da 2
3.98
Reg
row
thM
onda
46.
31
75%
cle
arfe
lled
and
rege
nera
ted
with
500
0se
edlin
gs/h
a5/
12/1
977-
26/4
/197
8cl
earfe
lled
6/3/
1978
Bur
nt17
/5/1
978
Plan
ted
11/6
/197
0-5/
12/1
977
Mon
da 3
7.25
1939
E.
Reg
nans
Reg
row
thM
onda
46.
31
5/12
/197
7-26
/4/1
978
80%
cle
arfe
lled
and
rege
nera
ted
with
500
seed
lings
/ha
21/4
/197
8 B
urnt
9/5/
1978
Pla
nted
11/8
/197
0-5/
12/1
977
Myr
tle 2
30.8
Med
iterra
nean
-co
ol w
et w
inte
rsan
d ho
t dry
sum
mer
s
1759
E.
Reg
nans
Reg
row
thM
yrtle
125
.21
74%
cle
arfe
lled
4/12
/198
4-9/
3/19
85B
urnt
- 20
/3/1
985
Seed
ed. W
inte
r 198
5
13/7
/197
1-4/
12/1
984
Wat
son
et a
l. (1
999)
;W
atso
n et
al.
(200
1)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Cor
ande
rrk
expe
rimen
tal a
rea,
Vic
toria
, Aus
tral
ia
Blu
e Ja
cket
64.8
36.6
SW
Med
iterra
nean
-co
ol w
et w
inte
rsan
d ho
t dry
sum
mer
s
1850
E.
Reg
nans
& E
.O
bliq
uaR
egro
wth
Slip
62.3
40.3
SSe
lect
ive c
ut N
ov 1
972-
Mar
197
311
/8/1
958-
8/11
/197
2
Wat
son
et a
l. (1
999)
;N
anda
kum
ar (1
993)
;N
anda
kum
ar a
ndM
ein
(199
7);
Picc
anin
ny52
.837
.8S
Med
iterra
nean
-co
ol w
et w
inte
rsan
d ho
t dry
sum
mer
s
1850
E.
Reg
nans
& E
.O
bliq
uaR
egro
wth
Slip
62.3
40.3
SC
lear
fellin
g of
85%
of
vege
tatio
n in
Nov
197
1-Ap
r 197
2
7/3/
1956
-16
/11/
1971
Wat
son
et a
l. (1
999)
;N
anda
kum
ar (1
993)
;N
anda
kum
ar a
ndM
ein
(199
7);
Ste
war
ts C
reek
, Vic
toria
, Aus
tral
ia
CA2
4.0
8.3
NE
Bar
e gr
ound
(196
9-19
75)
Past
ure
1976
-
1120
(196
0-19
80)
CA1
4.3
9.0
NE
1400
(196
0-19
67)
Cle
ared
196
9, B
are
Gro
und:
Apr
il 196
9-19
75 P
astu
re 1
976
1960
-196
9
CA5
17.6
7.6
NW
Med
iterra
nean
-co
ol w
et w
inte
rsan
d ho
t dry
sum
mer
s
Mix
ed S
peci
esEu
calyp
t for
est.
Mix
ed S
peci
esEu
calyp
t for
est.
Bar
egr
ound
:Ju
ne 1
969-
April
197
0.Pi
nes:
Apr
il19
70
1120
(196
0-19
80)
CA4
25.3
6.0
NW
1120
(196
0-19
80)
Cle
ared
May
196
9, B
are
grou
nd: A
pril 1
969-
1970
. Gro
win
g Pi
nefo
rest
: Apr
il 197
0-
1960
-196
9
Mei
n et
al.
(198
8);
Nan
daku
mar
(199
3)
Par
wan
Exp
erim
enta
l Are
a, V
icto
ria, A
ustr
alia
Parw
an 1
1.6
NN
ative
Pas
ture
woo
dlan
d53
8Pa
rwan
31.
6N
538
Nat
ive P
astu
re (1
954-
1959
) Reg
ener
atio
n of
woo
dlan
d (1
959-
1968
)G
razin
g (1
968-
74)
1954
-195
9N
anda
kum
ar (1
993)
Parw
an 2
1.6
NN
ative
Pas
ture
impr
oved
past
ure
538
Parw
an 3
1.6
N53
8
Nat
ive P
astu
re (1
954-
1959
) Im
prov
ed P
astu
re(1
959-
1968
) Gra
zing
(196
8-74
)
1954
-195
9N
anda
kum
ar (1
993)
Parw
an 4
1.6
SN
ative
Pas
ture
woo
dlan
d53
8Pa
rwan
51.
6S
538
Nat
ive P
astu
re (1
954-
1959
) Reg
ener
atio
n of
woo
dlan
d (1
959-
1968
)G
razin
g (1
968-
74)
1954
-195
9N
anda
kum
ar (1
993)
Parw
an 5
1.6
S
Med
iterra
nean
type
clim
ate
Nat
ive P
astu
reim
prov
edpa
stur
e53
8Pa
rwan
51.
6S
538
Nat
ive P
astu
re (1
954-
1959
) Im
prov
ed P
astu
re(1
959-
1968
) Gra
zing
(196
8-74
)
1954
-195
9N
anda
kum
ar (1
993)
Ree
fton
Exp
erim
enta
l Are
a, V
icto
ria, A
ustr
alia
Ree
fton
170
.46
559
NM
edite
rrane
anTy
pe c
limat
eN
ative
Euca
lypt f
ores
tR
egro
wth
1233
180
Nat
ive fo
rest
unt
il 198
4,20
% o
f the
fore
st c
lear
edin
Apr
il 198
419
71-1
980
Nan
daku
mar
(199
3);
Ree
fton
276
.112
588
WM
edite
rrane
anTy
pe c
limat
eN
ative
Euca
lypt f
ores
tR
egro
wth
1250
258
Ree
fton
3-6
95.1
107.
215
6.2
521.
2
5 9 5 2
596
594
640
651
NW SW SW S
1265
1249
1308
1440
209
234
228
318
Nat
ive F
ores
t unt
il Apr
il19
84. U
nder
stor
ey b
urnt
in A
pril 1
984
1971
-198
0N
anda
kum
ar (1
993)
;
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Col
lie R
iver
Bas
in, W
este
rn A
ustr
alia
Don
sEu
calyp
t For
est
Agric
ultu
re72
0
Cle
arin
g of
nat
ive fo
rest
by s
trip
clea
ring,
soi
lcl
earin
g an
d pa
rkla
ndcl
earin
g
1974
-197
6R
upre
cht a
ndSc
hofie
ld (1
991b
)
Lem
ons
344
8Eu
calyp
t For
est
Agric
ultu
re75
0
Erni
e27
072
053
.5 %
of c
atch
men
tcl
eare
d be
twee
nN
ovem
ber 1
976
and
Mar
ch 1
977
1974
-197
6R
upre
cht a
ndSc
hofie
ld (1
991a
)
Wig
hts
94Eu
calyp
t For
est
Agric
ultu
re12
00Sa
lmon
100%
cle
ared
in S
umm
er19
76-1
977
1974
-197
6R
upre
cht a
ndSc
hofie
ld (1
989)
Bal
ingu
pB
rook
Trib
utar
y93
.3Ja
rrah
(Euc
alyp
tus.
mar
gina
ta)
Agric
ultu
re88
0Th
omso
nB
rook
/Lud
low
Rive
r
1020
0/10
10
950/
950
Affo
rest
atio
ns
1978
-198
0(fi
rst 3
yea
rssi
nce
refo
rest
atio
n)
Bor
g et
al.
(198
8)
Mar
ch R
oad
261
170-
230m
Nat
ive fo
rest
dom
inat
ed b
yE.
mar
gina
taan
d E.
calo
phyll
a an
dE.
dive
rsic
olor
E.di
vers
icol
or10
5010
50
Cle
ared
Jan
uary
198
2 -
Mar
ch 1
983.
Nur
sery
rais
ed k
arri
seed
lings
wer
e ha
nd-
plan
ted
in th
e sa
me
year
of lo
ggin
g.
1976
-198
2B
ari e
t al.
(199
6)
April
Roa
dN
orth
Euca
lypt F
ores
tre
grow
th10
70
April
Roa
dSo
uth
248
Cle
ar fe
lling
leav
ing
100m
buf
fers
Lew
in S
outh
Euca
lypt F
ores
tre
grow
th12
20Le
win
Nor
thSe
lect
ion
cut a
ndre
gene
ratio
n19
82-1
985
Wel
lbuc
ket
Euca
lypt F
ores
tre
grow
th
Sele
ctio
n cu
t and
rege
nera
tion.
Bas
al a
rea
redu
ced
from
16
m^3
/ha
to 1
1m^3
/ha.
Cro
wn
cove
r red
uced
from
55%
to 2
2%
1977
-198
1
Rup
rech
t and
Scho
field
(198
9)
Yarra
gil 4
L12
62.
0S
Euca
lypt F
ores
tre
grow
th11
204.
3Ya
rragi
l 4X
270
3.1
SE80
% o
f can
opy
cove
rre
mov
edSt
onem
an (1
993)
Yerra
min
nup
SEu
calyp
t For
est
regr
owth
850
Yarra
min
nup
Nor
thLo
ggin
g le
avin
g 50
mbu
ffer a
nd re
gene
ratio
n19
82-1
985
Rup
rech
t and
Scho
field
(198
9)
Han
sen
80
Med
iterra
nean
clim
ate
Euca
lypt F
ores
tLe
wis
80
1985
-86
inte
nsive
unifo
rm th
inni
ngtre
atm
ent w
as a
pplie
dac
ross
the
catc
hmen
tex
clud
ing
the
swam
p an
da
50m
buf
fer s
trip
1978
-198
4R
upre
cht e
t al.
(199
1)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Indi
a
Doo
n Va
lley
1.45
5.1
SED
erive
d sc
rub
with
sal
seed
lings
E. g
rand
isan
d E.
cam
aldu
lens
is
1167
Oye
band
e (1
988)
Gle
nmor
gan
B32
mon
tane
tem
pera
te h
umid
clim
ate
Gra
ssla
nd
Blu
egum
Rot
atio
n 1
then
Blu
egum
rota
tion
2
1380
Gle
nmor
gan
A32
1380
Con
vers
ion
from
nat
ural
gras
slan
d to
Blu
egum
plan
tatio
n (ro
tatio
n 10
year
s). B
lue
gum
harv
este
d in
198
2fo
llow
ed b
y a
seco
ndro
tatio
n of
cop
pice
dbl
uegu
m.
1968
-197
2Sh
arda
et a
l. (1
988)
;Sh
arda
et a
l. (1
998)
Japa
nTa
kara
gaw
a-Sh
ozaw
a11
810
67SW
60%
har
dwoo
d,40
% c
onife
rs21
5317
8319
48-1
954
- 50%
volu
me
sele
cted
cut
ting
Bos
ch a
nd H
ewle
tt(1
982)
Kam
abuc
hiN
o. 2
2.48
36SE
Artif
icia
lfo
rest
, sta
ndin
gas
gro
ups
inha
rdw
ood
stan
d, w
ithde
nse
grou
ndco
ver
Res
trict
edre
grow
th26
1720
75N
o.1
100%
cle
ar fe
lled
Dec
1947
1940
-194
7N
akan
o (1
967)
Ken
yaKe
richo
Sam
bret
702
4.5
2200
NW
Mon
tane
fore
stw
ith b
ambo
ote
a22
3678
919
59-1
963
87%
Cut
for
tea
gard
enO
yeba
ne (1
988)
Kim
akia
35.2
2438
SB
ambo
o fo
rest
Pine
2198
1104
1956
100
% c
lear
ed, p
ine
plan
ted
Oye
bane
(198
8)
Big
Bus
h, N
ew Z
eala
nd
DC
18.
5755
0N
W
83%
of a
rea
skid
der-
logg
ed b
etw
een
April
and
Dec
embe
r 198
0.P.
radi
ata
plan
ted
May
1981
1975
-198
0
DC
420
.19
550
NW
Even
lydi
strib
uted
rain
fall
thro
ugho
ut th
eye
ar, m
ean
annu
alte
mpe
ratu
re o
f10
.5 D
egC
Mix
edev
ergr
een
nativ
e fo
rest
rem
nant
s an
dpl
anta
tions
of
exot
ic s
peci
es
Pinu
sra
diat
a15
30D
C2
4.74
550
NW
94%
cle
arfe
lled
and
harv
este
d by
hau
ler
betw
een
May
198
0 an
dJu
ne 1
981,
Pla
nted
with
Pinu
s ra
diat
a in
Sept
embe
r 198
1
1975
-198
0
Fahe
y an
d Ja
ckso
n(1
997)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Gle
ndhu
Sta
te F
ores
t, N
ew Z
eala
nd
GH
231
046
0-67
0N
Rai
nfal
l occ
urs
as m
any
smal
lev
ents
of l
ong
dura
tion
and
low
inte
nsity
. Dry
spel
ls a
reco
mm
on in
sum
mer
Snow
-Tu
ssoc
kPi
nus
radi
ata
1350
GH
121
846
0-67
0N
1350
67%
Pla
nted
with
Pin
usra
diat
a in
198
2 at
123
0st
ems/
ha19
79-1
982
McL
ean
(200
1);
Fahe
y an
d Ja
ckso
n(1
997)
Mai
mai
, Wes
tland
, New
Zea
land
M13
4.25
3734
0SW
Reg
row
th26
5015
50M
152.
6434
335
SW26
5015
5095
% C
lear
felle
d,ve
geta
tion
left
in ri
paria
nzo
ne19
77
M14
4.62
3634
0SW
Reg
row
th26
5015
50M
152.
6434
335
SW26
5015
5010
0% C
lear
felle
d19
77M
52.
3136
290
SWR
egro
wth
2650
1550
M6
1.63
3628
5SW
2650
1550
100%
Cle
arfe
lled
1977
M8
3.84
3630
5SW
Supe
rhum
id,
mic
roth
erm
al,
with
ade
quat
era
infa
ll in
all
seas
ons
Ever
gree
nm
ixed
bee
ch-
podo
carp
-ha
rdw
ood
fore
st .
Reg
row
th26
5015
50M
61.
6336
285
SW26
5015
5090
% C
lear
felle
d,ve
geta
tion
left
in ri
paria
nzo
ne19
77
Row
e et
al.
(199
4)R
owe
and
Pear
ce(1
994)
Cat
hedr
al P
eak,
Sou
th A
fric
a
Cal
th_i
i19
00.
4518
45-
2454
NC
old
dry
win
ters
and
hot w
etsu
mm
ers
Gra
ssla
nd,
woo
dyco
mm
uniti
esal
ong
the
stre
ams
Pinu
s pa
tula
(with
20m
strip
of
ripar
ian
zone
on
eith
er s
ide
of th
est
ream
)
1400
807
Cat
h_iv
94.7
0.35
1845
-222
6N
1400
673
75%
affo
rest
ed19
49-1
952
Scot
t et a
l. (2
000)
Cal
th_i
ii13
8.9
0.38
1845
-23
17N
Col
d dr
y w
inte
rsan
d ho
t wet
sum
mer
s
Gra
ssla
nd,
woo
dyco
mm
uniti
esal
ong
the
stre
ams
Pinu
s pa
tula
1400
683
Cat
h_iv
94.7
0.35
1845
-222
6N
1400
673
86%
affo
rest
ed19
51-1
960
Dye
(199
6); S
cott
and
Smith
(199
7);
Scot
t et a
l. (2
000)
Jonk
ersh
oek
Fore
st R
esea
rch
Cen
tre,
Sou
th A
fric
a
Bie
siev
lei
27.2
0.35
372-
580
SW
hum
idm
esot
herm
alM
edite
rrane
anty
pe
tall o
pen
tocl
osed
fybo
ssh
rubl
and
Pinu
sra
diat
a12
9859
4La
ngriv
ier
245.
80.
4036
6-14
60SW
1653
98%
affo
rest
ed w
ithPi
nus
radi
ata
1938
-194
8va
n W
yk (1
987)
;D
ye (1
996)
; Sco
tt et
al. (
2000
)
Bos
bouk
oof
200.
90.
2627
4-10
67SW
hum
idm
esot
herm
alM
edite
rrane
anty
pe
tall o
pen
tocl
osed
fybo
ssh
rubl
and
Pinu
sra
diat
a11
2724
6La
ngriv
ier
245.
80.
4036
6-14
60SW
1653
57%
affo
rest
atio
n w
ithPi
nus
radi
ata
1938
-194
0
van
Wyk
(198
7);
Scot
t and
Van
Wyk
(199
0) D
ye (1
996)
;Sc
ott e
t al.
(200
0)
Lam
brec
htsb
os A
31.2
0.45
366-
1067
SW
hum
idm
esot
herm
alM
edite
rrane
an
tall o
pen
tocl
osed
fybo
ssh
rubl
and
Pinu
sra
diat
a11
4556
4La
ngriv
ier
245.
80.
4036
6-14
60SW
1653
89%
affo
rest
ed w
ithPi
nus
radi
ata
1946
-197
2va
n W
yk (1
987)
;Sc
ott e
t al.
(200
0)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Lam
brec
htsb
os B
65.5
0.46
300-
1067
SW
hum
idm
esot
herm
alM
edite
rrane
anty
pe
tall o
pen
tocl
osed
fybo
ssh
rubl
and
Pinu
sra
diat
a11
4551
8La
ngriv
ier
245.
80.
4036
6-14
60SW
1653
825
affo
rest
ed w
ithPi
nus
radi
ata
(20m
buffe
r on
stre
am b
anks
)19
47-1
964
van
Wyk
(198
7);
Scot
t and
Van
Wyk
(199
0); D
ye (1
996)
;Sc
ott a
nd S
mith
(199
7); S
cott
et a
l.(2
000)
Tier
kloo
f15
7.2
0.49
280-
1530
SW
hum
idm
esot
herm
alM
edite
rrane
anty
pe
tall o
pen
tocl
osed
fybo
ssh
rubl
and
Pinu
sra
diat
a13
1910
77La
ngriv
ier
245.
80.
4036
6-14
60SW
1653
36%
affo
rest
ed w
ithPi
nus
radi
ata
1938
-195
6va
n W
yk (1
987)
;Sc
ott e
t al.
(200
0)
Uits
oek
Sta
te F
ores
t, S
outh
Afr
ica
Mok
obul
aan
A26
.20.
2312
92-
1433
EEu
calyp
ts11
6619
7M
okob
ulaa
n C
36.9
0.26
1341
-149
4E
1199
118
100%
affo
rest
ed w
ithEu
calyp
tus
gran
dis
1956
-196
8
Van
Lill e
t al.
(198
0); D
ye (1
996)
;Sc
ott a
nd L
esch
(199
7); S
cott
and
Smith
(199
7); S
cott
et a
l. (2
000)
Mok
obul
aan
B34
.60.
2213
18-
1486
E
Sub
Trop
ical
with
80%
of th
eav
erag
e an
nual
rain
fall o
f11
67m
m fa
lling
with
in th
esu
mm
er m
onth
sof
Oct
ober
toM
arch
. Rai
nfal
lru
noff
ratio
0.1
8
sub-
clim
axgr
assl
and,
Nor
th E
aste
rnM
ount
ain
Sour
veld
,m
aint
aine
d by
regu
lar b
urni
ngan
d gr
azin
gPi
nes
1197
196
Mok
obul
aan
C36
.90.
2613
41-1
494
E11
9911
8
100%
affo
rest
ed w
ithPi
nus
patu
la, J
anua
ry19
71.
1971
- 13
70 s
tem
s/ha
1979
- 65
0 st
ems/
ha
1956
-197
1
Van
Lill e
t al.
(198
0); D
ye (1
996)
;Sc
ott a
nd L
esch
(199
7); S
cott
and
Smith
(199
7); S
cott
et a
l. (2
000)
Wes
tfal
ia, S
outh
Afr
ica
Wes
tfalia
D39
.60.
3310
50-
1320
SESu
b Tr
opic
alw
ith S
umm
erra
infa
ll sea
son
trans
ition
albe
twee
nev
ergr
een
high
fore
st a
ndde
cidu
ous
woo
dlan
d
Euca
lyptu
sgr
andi
s12
5359
0W
estfa
lia B
32.6
0.42
1140
-142
0SE
1253
492
Rip
aria
n zo
ne (1
0%) o
far
ea c
ut in
198
1. 8
3%af
fore
sted
with
Euca
lyptu
s gr
andi
s in
1983
1975
-198
1Sc
ott a
nd S
mith
(199
7); S
cott
et a
l.(2
000)
Taiw
an
LHC
-45.
8640
SE
war
m a
ndhu
mid
, the
aver
age
mon
thly
tem
pera
ture
neve
r fal
ling
belo
w 1
5de
gree
s
War
m-
tem
pera
tem
onta
nera
info
rest
Reg
row
th21
0011
00LH
C-5
8.39
SW19
78-1
979
100%
cle
arcu
t19
70-1
977
Hsi
a an
d Ko
h (1
983)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Adi
rond
acks
, New
Yor
k, U
SA
Saca
ndag
a12
7000
575
Nor
ther
nha
rdw
oods
with
con
ifers
1143
770
1912
-195
0, b
asal
are
ain
crea
sed
from
17
to 2
8m
2 ha-1
Bos
ch a
nd H
ewle
tt(1
982)
Alu
m C
reek
, Ark
., U
SA
WS2
115
412
NE
pine
with
hard
woo
dun
ders
tory
1333
153
WS1
115
NE
1333
1970
, 45%
thin
ned,
unde
rgro
wth
kille
d by
herb
icid
e ap
plic
atio
n
WS3
115
412
NE
pine
with
hard
woo
dun
ders
tory
1333
153
WS1
115
NE
1333
7-19
70, 1
00%
Cle
arcu
tsp
raye
dan
nual
ly fo
r3
year
s
Rog
erso
n (1
971)
Bea
ver
Cre
ek, A
rizon
a, U
SA
WS
314
715
73W
Reg
row
th45
322
Bea
ver C
reek
251
1591
NW
466
2519
68 -
83%
of t
rees
kille
d by
her
bici
deB
aker
(198
4); B
aker
(198
6)
WS1
134
1595
-17
90W
Juni
per-
piny
onfo
rest
Gra
ss45
720
Bea
ver C
reek
251
1591
NW
466
25
1963
- la
rge
piny
on p
ine
and
juni
per t
rees
rem
oved
. Are
a pl
ante
dw
ith g
rass
1958
-196
2B
aker
(198
6)
WS
1218
421
50SW
617
150
WS
1336
921
95SW
609
9310
0% re
mov
al o
fov
erst
ory
Bak
er (1
986)
WS
1454
621
94S
650
117
WS
1336
921
95SW
609
9357
% s
trip
cutti
ng w
ithth
inni
ngB
aker
(198
6)
WS
1610
221
64SE
703
135
WS
1566
2103
S68
599
68%
stri
p-cu
t with
thin
ning
Bak
er (1
986)
WS
1712
121
15SW
726
206
Wat
ersh
ed 1
898
2054
S72
818
077
% re
mov
al o
fov
erst
ory
Bak
er (1
986)
WS
873
022
25W
679
174
WS
1336
921
95SW
609
9333
% re
mov
al o
fov
erst
ory
Bak
er (1
986)
WS
945
421
94W
Une
ven
aged
stan
ds o
fpo
nder
osa
pine
645
155
WS
873
022
25W
658
160
31%
stri
p cu
t with
thin
ning
Bak
er (1
986)
Cas
tle C
reek
, Ariz
ona,
US
A
Wes
t For
k36
4.2
12.6
2165
NW
Pred
omin
atel
ypo
nder
osa
pine
with
an
unde
rsto
ry o
fG
ambl
e oa
k
Reg
row
th68
650
.8Ea
st C
reek
471
13.8
2388
-261
6N
14W
686
76.2
one-
sixt
h ar
ea h
arve
sted
,re
mai
nder
thin
ned
for
optim
um g
row
ing
cond
ition
s
1955
-196
4R
ich
(197
2); B
aker
(199
9)
Wor
kman
Cre
ek, C
entr
al A
rizon
a
Nor
th F
ork
100.
420
10-
2356
SW
Col
d m
oist
win
ters
, dry
war
sprin
gs a
nd h
otm
oist
sum
mer
s.
Pond
eros
a pi
neO
n th
e m
oist
site
s, D
ougl
asfir
and
whi
te fi
rar
eim
porta
nt
gras
slan
d83
587
Mid
dle
fork
210.
920
10-2
356
835
86
1953
- rip
aria
n cu
t of
broa
dlea
f spe
cies
1958
- co
nver
t moi
st s
ite(m
ostly
Dou
glas
fir a
ndw
hite
fir t
o gr
assl
and.
1966
con
vert
dry
site
(mos
tly
1938
-195
2R
ich
and
Got
terfr
ied
(197
6)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Sout
h Fo
rk12
8.7
2010
-23
56SW
Pond
eros
api
ne83
5M
iddl
e fo
rk21
0.9
2010
-235
683
586
1953
- St
art s
ingl
e tre
ese
lect
ion
harv
est
1966
- co
nver
t to
pure
pond
eros
a pi
ne w
ithba
sal a
rea
of 9
.2 m
2/ha
1938
-195
2R
ich
and
Got
tfrie
d(1
976)
Cen
tral
New
Yor
k, U
SA
Col
d Sp
ring
Bro
ok39
156
5S
1030
616
1934
, 35%
refo
rest
edw
ith c
onife
rs
Sage
Bro
ok18
152
5SE
974
535
1932
, 47%
refo
rest
edw
ith c
onife
rsSh
ackl
amB
rook
808
520
S
Con
tinen
tal t
ype
clim
ate
mix
edha
rdw
oods
and
coni
fers
mix
edha
rdw
oods
and
coni
fers
1030
627
Albr
ight
Cre
ek18
30
1931
-193
9, 5
8%re
fore
sted
with
con
ifers
Schn
eide
r and
Aye
r(1
961)
Cos
hoct
on, O
hio,
US
A
172
1822
.830
6-39
3S
30%
har
dwoo
din
193
8
100%
hard
woo
d/w
hite
pin
e97
030
019
612
2.6
1427
6-36
1SE
970
1938
-193
9, 1
0%re
fore
sted
, mos
tly p
ine
Har
rold
et a
l. (1
962)
Cow
eeta
, Nor
th C
arol
ina,
US
A
Cow
eeta
116
.234
705-
988
SO
ak-h
icko
ryfo
rest
Whi
te P
ine
1727
787
1956
- en
tire
catc
hmen
tcl
ear-
cut a
nd w
hite
pin
ese
edlin
gs p
lant
ed19
44-1
953
Swan
k an
d M
iner
(196
8); S
wan
k et
al.
(198
7)
Cow
eeta
, 10
8624
742-
1159
SSE
Oak
-hic
kory
fore
stR
egro
wth
Expl
oitiv
e se
lect
ivelo
ggin
g du
ring
the
perio
d19
42-1
956
with
a 3
0%re
duct
ion
in to
tal
wat
ersh
ed b
asal
are
a
Swan
k an
d C
ross
ley
(198
7)
Cow
eeta
, 13
16.1
1972
5-91
2EN
E
Seco
ndre
grow
th s
tand
with
asc
atte
ring
ofov
erm
atur
etre
es
Reg
row
th18
2987
2W
S18
12.5
726-
993
NW
1939
1034
Sept
193
9-Ja
n 19
40 a
llw
oody
veg
etat
ion
was
cut (
no m
ater
ial
rem
oved
); 19
62R
egro
wth
cut
1936
/37-
1939
/194
0
Swan
k an
d H
elve
y(1
973)
; Sw
ank
et a
l.(1
987)
Cow
eeta
, 17
13.4
5776
0-10
21N
WO
ak-h
icko
ryfo
rest
Whi
te P
ine
1930
868
All s
hrub
and
fore
stve
geta
tion
cut J
an-M
arch
1942
Ann
ual s
prou
tgr
owth
cut
194
3-19
5519
56 w
hite
pin
e pl
ante
d
1938
-194
1Sw
ank
and
Min
er(1
968)
; Sw
ank
et a
l.(1
987)
Cow
eeta
, 19
28.3
3279
6-11
19N
W
mar
ine
with
coo
lsu
mm
ers,
mild
win
ters
, and
adeq
uate
rain
fall
durin
g al
lse
ason
s
Oak
-hic
kory
fore
stR
egro
wth
2032
1219
WS2
123
.982
3-11
74N
2083
1321
Dec
194
8-M
arch
194
9al
l laur
el a
ndrh
odod
endr
on c
ut c
lose
to g
roun
d (2
2% o
f bas
alar
ea)
1941
-194
8Jo
hnso
n an
dKo
vene
r (19
56)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Cow
eeta
, 22
3435
847-
1244
NO
ak-h
icko
ryfo
rest
All w
oody
veg
etat
ion
with
in a
ltern
ate
10m
strip
s de
aden
ed b
ych
emic
als
in 1
955;
redu
ced
tota
l bas
al a
rea
by 5
0%
Swan
k an
d C
ross
ley
(198
7)
Cow
eeta
, 28
144
3196
4-15
51E
Oak
-hic
kory
fore
st
Mul
tiple
use
dem
onst
ratio
nco
mpr
isin
g of
com
mer
cial
har
vest
with
clea
r-cu
tting
on
77ha
,th
inni
ng o
n 39
ha o
f the
cove
fore
st a
nd n
ocu
tting
on
28 h
a;pr
oduc
ts re
mov
ed
Swan
k an
d C
ross
ley
(198
7)
Cow
eeta
, 39.
232
739-
931
EO
ak-h
icko
ryfo
rest
1814
607
All v
eget
atio
n cu
t and
burn
t or r
emov
ed fr
omth
e w
ater
shed
in 1
940.
Unr
egul
ated
agr
icul
ture
on 6
ha fo
r a 1
2 ye
arpe
riod,
follo
wed
by
plan
ting
yello
w p
opla
ran
d w
hite
pin
e
Swan
k an
d C
ross
ley
(198
7)
Cow
eeta
, 37
43.7
3312
80N
E
Seco
ndre
grow
th s
tand
with
asc
atte
ring
ofov
erm
atur
etre
es
2244
1515
Cow
eeta
36
4910
21-1
542
ESE
2222
1675
100%
veg
etat
ion
cut i
n19
63 (n
o pr
oduc
tsre
mov
ed)
1943
/194
4-19
57/1
958
Swan
k an
d H
elve
y(1
973)
Cow
eeta
, 40
2042
1035
SEO
ak-h
icko
ryfo
rest
Com
mer
cial
sel
ectio
ncu
t with
22%
of b
asal
area
rem
oved
in 1
955
Swan
k an
d C
ross
ley
(198
7)
Cow
eeta
, 41
2946
1065
SEO
ak-h
icko
ryfo
rest
Reg
row
th
Com
mer
cial
sel
ectio
ncu
t with
35%
of b
asal
area
rem
oved
in 1
955
Swan
k an
d C
ross
ley
(198
7)
Cow
eeta
, 69
3579
3N
W
mar
ine
with
coo
lsu
mm
ers,
mild
win
ters
, and
adeq
uate
rain
fall
durin
g al
lse
ason
s
oak
- hic
kory
fore
stG
rass
1854
838
WS1
4/W
S18
61/1
370
7-99
2/99
3-72
6N
W/N
W18
76/1
939
988/
1034
1942
- 12
% o
f cat
chm
ent
alon
g st
ream
was
cut
(Reg
row
th) 1
958
-m
erch
anta
ble
timbe
rre
mov
ed 1
959
- rem
inde
rof
cat
chm
ent c
lear
ed a
ndgr
ass
sow
n. G
rass
herb
icid
e in
196
6 an
d19
67 G
rass
her
bici
de in
1966
and
196
7
Hib
bert
(196
9); B
urt
and
Swan
k (1
992)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Coy
ote
Cre
ek, O
rego
n, U
SA
Coy
ote
169
730-
1065
Dou
glas
fir,
mix
ed c
onife
rs12
3062
7C
oyot
e 4
4973
0-10
6550
% s
elec
tion
cut 1
971
1963
-197
0Jo
nes
(200
0)
Coy
ote
268
730-
1065
Dou
glas
fir,
mix
ed c
onife
rsC
oyot
e 4
4973
0-10
6525
-30%
Pat
ch c
ut 1
971
1963
-197
0Jo
nes
(200
0)
Coy
ote
350
730-
1065
Dou
glas
fir,
mix
ed c
onife
rs
Reg
row
th
Coy
ote
449
730-
1065
100%
cle
ar-c
ut 1
971
1963
-197
0Jo
nes
(200
0)
East
ern
Tenn
esse
e, U
SA
Whi
teH
ollo
w69
441
0SE
65%
mix
edha
rdw
oods
and
pine
in 1
934
Reg
row
th11
8446
019
34-1
942,
34%
refo
rest
ed w
ith p
ines
Bos
ch a
nd H
ewle
tt(1
982)
Fox
Cre
ek, O
rego
n, U
SA
FC1
5984
0-92
5R
egro
wth
2730
1750
Fox
225
384
0-19
8819
69-1
7, 2
5% c
lear
-cut
in 3
-4ha
uni
ts19
58-1
964
Sted
nick
(19
96);
Jone
s (2
000)
FC3
7184
0-95
0
The
mar
itim
ecl
imat
e ha
s w
et,
mild
win
ters
and
dry,
coo
lsu
mm
ers
Dou
glas
Fir,
wes
tern
hem
lock
Reg
row
th27
3017
50Fo
x 2
253
840-
1988
1970
-197
2, 2
5% c
lear
cut i
n 8-
10 h
a un
its19
58-1
969
Jone
s (2
000)
Fras
er E
xper
imen
tal F
ores
t, U
SA
Dea
dhor
seC
reek
-N
orth
For
k41
Reg
row
thLe
xen
Cre
ek12
430
02-3
536
Tim
ber r
emov
ed o
n 36
%of
land
are
a (1
977)
1970
-197
7Al
exan
der e
t al.
(198
5); T
roen
dle
and
King
(198
5)
Dea
dhor
seC
reek
-U
pper
Bas
in78
lodg
epol
e pi
neon
all l
ower
and
mid
-sou
thsl
opes
, and
alpi
ne tu
ndra
abov
e th
etim
ber l
ine
Reg
row
thLe
xen
Cre
ek12
430
02-3
536
30%
har
vest
ed in
irreg
ular
sha
ped
clea
r-cu
ts, v
aryin
g in
size
from
1 to
6 h
a (S
umm
ers
of19
83-1
984)
1975
-198
2Al
exan
der e
t al.
(198
5); T
roen
dle
and
King
(198
5)
Fool
Cre
ek28
928
96-
3505
SW
Coo
l and
hum
idw
ith lo
ng, c
old
win
ters
and
shor
t, co
olsu
mm
ers
lodg
epol
e pi
nean
d sp
ruce
-fir
Reg
row
th76
228
3Ea
st S
t Lou
isC
reek
803
2896
-371
919
54-1
956,
40%
com
mer
cial
cut
in s
trips
.19
43-1
953
Alex
ande
r et a
l.(1
985)
Gra
nt M
emor
ial F
ores
t, G
eorg
ia, U
SA
WS1
432
.516
5SW
Fully
fore
sted
pied
mon
t lan
dlo
blol
ly pi
neW
S15
42.5
100%
cat
chm
ent c
lear
edO
ct 1
974-
Jan
1975
. Jan
1976
Lob
lolly
pin
epl
ante
d
1973
-197
4H
ewle
tt et
al.
(198
4);
Hew
lett
and
Dos
s(1
984)
H.J
. And
rew
s Ex
perim
enta
l For
est,
US
A
HJ1
9628
460-
990
WR
egro
wth
2286
HJ
260
530-
1070
NW
2286
100%
cle
ar-c
ut (1
962-
1966
)19
52-1
961
Jone
s (2
000)
;R
otha
cher
(197
0)H
J10
10.1
425-
700
SR
egro
wth
2286
HJ
260
530-
1070
NW
2286
100%
cle
ar-c
ut (1
975)
1968
-197
4Jo
nes
(200
0)
HJ3
101
3249
0-10
70N
W
typi
cally
wet
inw
inte
r and
dry
insu
mm
er
Old
gro
wth
Dou
glas
fir
Reg
row
th22
86H
J 2
6053
0-10
70N
W22
8625
-30%
Pat
ch c
ut 1
963
1952
-195
8Jo
nes
(200
0);
Rot
hach
er (1
970)
HJ6
1388
0-10
10S
Reg
row
th22
86H
J 8
21.4
960-
1130
S22
8610
0% C
lear
-cut
(197
4)19
63-1
973
Jone
s (2
000)
HJ7
15.4
910-
1020
S
typi
cally
wet
inw
inte
r and
dry
insu
mm
er
Old
gro
wth
Dou
glas
fir
Reg
row
th22
86H
J 8
21.4
960-
1130
S22
8650
% C
lear
-cut
in 1
974
and
in 1
984
1963
-197
3Jo
nes
(200
0)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Hub
bard
Bro
ok E
xper
imen
tal F
ores
t
WS2
15.8
20-
30%
503-
716m
S31E
Reg
row
th12
19W
S342
.452
7-73
2S2
3W12
19C
lear
fellin
g an
dhe
rbic
idin
g (1
965-
1968
)7
year
s
WS4
3642
2-74
7S4
0ER
egro
wth
1219
WS3
42.4
527-
732
S23W
1219
Strip
cut
in th
ree
phas
esdu
ring
the
autu
mns
of
1970
, 197
2 an
d 19
7419
60-1
969
WS5
2248
8-76
2S
even
age
dde
cidu
ous
hard
woo
ds
Reg
row
th12
19W
S342
.452
7-73
2S2
3W12
19W
hole
tree
har
vest
ing
1983
-198
4.19
63-1
983
Mar
tin a
ndH
ornb
eck
(198
9);
Fede
rer e
t al.
(199
0);
Hor
nbec
k et
al.
(199
7); H
ornb
eck
etal
. (19
87)
Lead
ing
Rid
ge E
xper
imen
tal F
ores
t, U
SA
LR2
4336
0N
E10
0432
1
1967
- cl
earc
ut lo
wes
t9h
a19
71-1
972
Cle
arcu
t mid
-sl
ope
11ha
1974
Her
bici
de lo
wer
and
mid
-slo
pe a
reas
1975
-197
6 C
lear
cut 1
7ha
on u
pper
slo
pe19
77 -
Her
bici
de a
llcl
earc
ut a
reas
LR 3
104
340
Cen
tral
hard
woo
dsR
egro
wth
LR 1
123
800-
1450
SE
1976
-197
7 C
lear
cut
on
45 h
a
Die
tteric
k an
d Ly
nch
(198
9); L
ynch
and
Cor
bett
(199
0);
Hor
nbec
k et
al.
(199
3)
Nat
ural
Dra
niag
es, A
rizon
a, U
SA
A5
1420
SE45
234
1954
, 100
% c
hem
ical
lyco
ntro
lled
C5
1420
SE
Mar
gina
lch
apar
ral
Reg
row
th45
234
1954
100
% c
hem
ical
lyco
ntro
lled
Nor
ther
n M
issi
ssip
pi, U
SA
WSI
I1
E13
54W
SIII
1E
Mix
edha
rdw
ood
Reg
row
th
Als
ea R
iver
Bas
in, O
rego
n, U
SA
Dee
r Cre
ek30
331
260
% d
ougl
as fi
ran
d 40
% a
lder
Reg
row
th24
7419
06Fl
ynn
Cre
ek20
219
66-1
967,
25%
cle
ar-
cut i
n pa
tche
s, ro
ads
cons
truct
ed
1959
-196
5H
arris
(197
7)
Nee
dle
Bra
nch
7031
2
85%
ald
er a
ndm
aple
bef
ore
cutti
ng. 1
3ha
inth
e he
adw
ater
sha
d be
enpr
evio
usly
logg
ed in
ear
ly19
50's
Reg
row
th24
8318
86Fl
ynn
Cre
ek20
224
8319
7482
% c
lear
-cut
, bur
ned
in19
6719
59-1
965
Har
ris (1
973)
; Har
ris(1
977)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Pla
cer
Cou
ntry
, US
A
Wat
ersh
ed B
17.2
Oak
woo
dlan
dG
rass
Wat
ersh
ed A
19.1
1966
- C
onve
rsio
n to
gras
slan
d co
mm
ence
d19
56-1
965
Bur
gy a
ndPa
paza
firio
u (1
971)
Wat
ersh
ed C
516
8N
Oak
woo
dlan
dG
rass
635
145
Wat
ersh
ed A
19.1
Vege
tatio
n co
nver
sion
togr
assl
and
com
men
ced
in19
63.
1956
-196
2B
urgy
and
Papa
zafir
iou
(197
1)
San
Dim
as, C
alif.
, U
SA
Mon
roe
Can
yon
354
840
S
Cha
parra
l with
woo
dlan
drip
aria
nve
geta
tion
alon
g st
ream
s
Reg
row
th19
58 -
1.7%
cut
(Rip
aria
n ve
geta
tion)
Bos
ch a
nd H
ewle
tt(1
982)
Thre
e B
ar, U
SA
B1
910
80N
Cha
parra
lG
rass
582
1119
60 -
conv
erte
d to
gras
s19
56-1
959
Hib
bert
(197
1)
C3
911
60N
Cha
parra
lG
rass
638
5819
65 -
conv
erte
d to
gra
ss19
56-1
959
Hib
bert
(197
1)
F27
.741
168
NC
hapa
rral
Gra
ss68
136
D36
.234
1969
- ch
apar
ral c
lear
edgr
ass
plan
ted
1964
-196
9D
avis
(198
4)
Upp
er B
ear
Cre
ek A
la.,
US
A
XF1
5313
97
1971
-197
2, 3
0%cl
earc
ut, a
dditi
onal
22%
sele
ctive
cut
, bur
ned
resi
dual
poi
sone
d pi
nes
plan
ted
XF2
53
Pine
and
hard
woo
dPi
ne
1969
-197
0 85
%co
nver
sion
s to
pin
e af
ter
com
mer
cial
cle
arcu
t
Bos
ch a
nd H
ewle
tt(1
982)
Fern
ow E
xper
imen
tal F
ores
t, U
SA
Fern
ow 1
3075
5N
E15
2458
419
57-1
958
85%
bas
alar
eas
clea
rcut
Fern
ow 2
1578
0S
1500
660
1957
-58
36%
are
clea
rcut
Fern
ow 3
3480
5S
rain
y an
d co
olcl
imat
eM
ixed
hard
woo
dsR
egro
wth
1473
762
Fern
ow 4
39SE
1422
60
1957
-58
13%
bas
al a
rea
rem
oved
by
sele
ctio
n cu
t19
63 -
8% R
emov
ed b
ysa
me
met
hod
1968
- 6%
of b
asal
are
are
mov
ed, s
ame
met
hod
1969
-197
0 - 9
1%cl
earc
ut
Patri
c an
d R
einh
art
(197
1); H
ornb
eck
etal
. (19
93)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
Slop
eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Fern
ow 5
3678
0N
E15
0060
7
1957
-195
8 20
% b
asal
area
rem
oved
by
sele
ctio
n cu
t19
68 -
treat
men
tre
peat
ed (1
4%)
Fern
ow 6
2280
5SE
1469
788
1964
- lo
wer
50%
cut
,re
grow
th n
ot p
erm
itted
1968
- up
per 5
0% c
ut19
56-1
963
Fern
ow 7
2480
0N
E14
4049
3
1963
- up
per 5
0%cl
earc
ut.
1967
- lo
wer
50%
clea
rcut
1956
-196
3
Wag
on W
heel
Gap
, US
A
B8
131
10N
EAs
pen
and
Con
ifer
536
158
A90
3110
SE53
415
319
19 -
Cle
arcu
t of
wat
ersh
ed19
12-1
919
Van
Hav
eren
(198
8)
Wes
tern
Ten
ness
ee, U
SA
Pine
Tre
eB
ranc
h36
160
EM
ixed
hard
woo
dsR
egro
wth
1230
255
1946
- 75
% re
fore
sted
mos
tly p
ines
Bos
ch a
nd H
ewle
tt(1
982)
Whi
te S
par,
US
A
B3
911
60N
Cha
parra
lR
egro
wth
549
34
1967
- sh
rub
on 1
5%ch
emic
ally
treat
ed19
74-1
976,
shr
ub o
n20
% c
hem
ical
ly tre
ated
Bos
ch a
nd H
ewle
tt(1
982)
Cas
par
Cre
ek, C
alifo
rnia
, US
A
Sout
h Fo
rk42
437
-320
Med
iterra
nean
,dr
y su
mm
ers.
Nor
th F
ork
483
37-2
30R
oad
cons
truct
ion
1967
Logg
ing
1971
-197
319
63-1
967
Kepp
eler
and
Zie
mer
(199
0); W
right
et a
l.(1
990)
Ply
nlim
on, U
nite
d K
ingd
omSe
vern
1055
Wye
870
Kirb
y et
al.
(199
1)
Bal
quhi
dder
exp
erim
ent,
Uni
ted
Kin
gdom
Kirk
ton
685
540
Mon
oach
yle77
047
0Jo
hnso
n (1
991)
TREA
TED
CAT
CHM
ENT
CON
TRO
L CA
TCH
MEN
T
Mea
nM
ean
Mea
nM
ean
Mea
nPo
stAn
nual
Annu
alM
ean
Annu
alAn
nual
Area
Slop
eEl
evat
ion
Pre
Trea
tmen
tTr
eatm
ent
Rai
nfal
lSt
ream
flow
Cat
chm
ent
Area
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eEl
evat
ion
Con
trol
Rai
nfal
lSt
ream
flow
Cal
ibra
tion
Cat
chm
ent
(ha)
(%)
(m)
Aspe
ctC
limat
eVe
geta
tion
Vege
tatio
n(m
m)
(mm
)C
ontro
l(h
a)(%
)(m
)As
pect
(mm
)(m
m)
Trea
tmen
tpe
riod
Sour
ce o
f inf
o
Integrated catchment management in the Murray-Darling BasinA process through which people can develop a vision, agree on shared values and behaviours, make informeddecisions and act together to manage the natural resources of their catchment: their decisions on the use of land,water and other environmental resources are made by considering the effect of that use on all those resources and onall people within the catchment.
Our valuesWe agree to work together, and ensure that our behaviour reflects that following values.
Courage
• We will take a visionary approach, provide leadershipand be prepared to make difficult decisions.
Inclusiveness
• We will build relationships based on trust andsharing, considering the needs of futuregenerations, and working together in a truepartnership.
• We will engage all partners, including Indigenouscommunities, and ensure that partners have thecapacity to be fully engaged.
Commitment
• We will act with passion and decisiveness, takingthe long-term view and aiming for stability indecision-making.
• We will take a Basin perspective and a non-partisan approach to Basin management.
Respect and honesty
• We will respect different views, respect each otherand acknowledge the reality of each other’s situation.
• We will act with integrity, openness and honesty, be fairand credible and share knowledge and information.
• We will use resources equitably and respect theenvironment.
Flexibility
• We will accept reform where it is needed, be willingto change, and continuously improve our actionsthrough a learning approach.
Practicability
• We will choose practicable, long-term outcomesand select viable solutions to achieve theseoutcomes.
Mutual obligation
• We will share responsibility and accountability, andact responsibly, with fairness and justice.
• We will support each other through the necessarychange.
Our principlesWe agree, in a spirit of partnership, to use the followingprinciples to guide our actions.
Integration
• We will manage catchments holistically; that is,decisions on the use of land, water and otherenvironmental resources are made by consideringthe effect of that use on all those resources and onall people within the catchment.
Accountability
• We will assign responsibilities and accountabilities.
• We will manage resources wisely, beingaccountable and reporting to our partners.
Transparency
• We will clarify the outcomes sought.
• We will be open about how to achieve outcomesand what is expected from each partner.
Effectiveness
• We will act to achieve agreed outcomes.
• We will learn from our successes and failures andcontinuously improve our actions.
Efficiency
• We will maximise the benefits and minimise thecost of actions.
Full accounting
• We will take account of the full range of costs andbenefits, including economic, environmental, socialand off-site costs and benefits.
Informed decision-making
• We will make decisions at the most appropriate scale.
• We will make decisions on the best availableinformation, and continuously improve knowledge.
• We will support the involvement of Indigenouspeople in decision-making, understanding the valueof this involvement and respecting the livingknowledge of Indigenous people.
Learning approach
• We will learn from our failures and successes.
• We will learn from each other.