4. hydrological impact assessment green resources... · 2017. 6. 13. · ngr have also secured an...

4. HYDROLOGICAL IMPACT ASSESSMENT

Niassa Green Resources Plantation Project

DRAFT

Prepared for:

Green Resource Niassa

36, Trabalho Ave

Lichinga

Mozambique

Prepared by:

SJL Mallory and T Sawunyama

Coastal & Environmental Services

GRAHAMSTOWN

P.O. Box 934

Grahamstown, 6140

046 622 2364

Also in East London

www.cesnet.co.za

MAY 2012

http://www.cesnet.co.za/

Volume 2: ESIA Specialist Volume – Hydrological Impact Assessment

CES Hydrological Impact Assessment ii ii

This Report should be cited as follows: Mallory SJL and Sawunyama T, 2012. Assessment of the

hydrological impact of proposed forestry development in the Niassa Province, Mozambique. IWR

Water Resources, Pretoria, South Africa

COPYRIGHT INFORMATION

This document contains intellectual property and propriety information that is protected by

copyright in favour of Coastal & Environmental Services and the specialist consultants. The

document may therefore not be reproduced, used or distributed to any third party without the

prior written consent of Coastal & Environmental Services. This document is prepared

exclusively for submission to Niassa Green Resources, and is subject to all confidentiality,

copyright and trade secrets, rules intellectual property law and practices of Mozambique.


CES Hydrological Impact Assessment iii

TABLE OF CONTENTS 1 INTRODUCTION ..................................................................................................................... 1

1.1 Project background ........................................................................................................... 1

1.2 Terms of reference ........................................................................................................... 1

1.3 Structure of the report ....................................................................................................... 1

1.4 Study team ....................................................................................................................... 1

2 METHODOLOGY..................................................................................................................... 2 3 DESCRIPTION OF THE STUDY AREA ................................................................................... 7 4 IMPACT ASSESSMENT .......................................................................................................... 9

4.1 Reduction in runoff assessment ........................................................................................ 9

4.2 Assessment of low-flow reduction ................................................................................... 11

5 CONCLUSIONS AND IMPACT STATEMENT ....................................................................... 13 5.1 Impacts common to both project areas ........................................................................... 13

5.1.1 Issue 1: Planting of 4 374ha of forestry in the Ntiuile and Malica block project area . 13

6 REFERENCES ...................................................................................................................... 16 7 APPENDIX A: Paper on the methodology used ................................................................. 17

7.1 Introduction ..................................................................................................................... 17

7.2 Updates of the Gush tables to produce duration curves .................................................. 18

7.3 Application of the SFR method within a modelling framework ......................................... 20

7.4 Conclusion and recommendations .................................................................................. 22

7.5 References ..................................................................................................................... 24

8 APPENDIX B: STREAMFLOW REDUCTION DURATION CURVES FOR EUCALYPUTS AND PINE .............................................................................................................................. 25

LIST OF FIGURES

Figure 2.1: Runoff duration curves under various land use scenarios in January ............................ 3

Figure 2.2: Runoff duration curves under various land use scenarios in September ....................... 4

Figure 2.3: Reduction in runoff duration curves (January) ............................................................... 4

Figure 2.4: Reduction in runoff duration curves (September) .......................................................... 5

Figure 2.5: Reduction in runoff duration curves (annualised) .......................................................... 6

Figure 3.1: The Ntiuile and Malica Project Area .............................................................................. 7

Figure 4.1: Map showing sub-catchments used in yield modelling .................................................. 9

Figure 4.2: WReMP System diagram ............................................................................................ 10

LIST OF TABLES

Table 2.1: Gush duration curve for the X22D quaternary catchment in South Africa for the month of

January (Eucalyptus, Pine and Acocks vegetation) ......................................................... 3

Table 2.2: Gush duration curve for the X22D quaternary catchment in South Africa for the month of

September (Eucalyptus, Pine and Acocks vegetation) .................................................... 3

Table 2.3: Comparison of climatic variables in the SA catchment X22D and the Ntiuile and Malica

project areas in Mozambique .......................................................................................... 5

Table 3.1: Total areas to be acquired for re-afforestation ................................................................ 8

Table 4.1: Reference or natural hydrology .................................................................................... 10

Table 4.2: Streamflow reduction .................................................................................................... 11

Table 4.3: Impact of forestry on assurance of supply at sub-catchment level ................................ 12


CES Hydrological Impact Assessment 1

1 INTRODUCTION

1.1 Project background

Niassa Green Resources, S.A. (NGR) engages in tree planting in Niassa Province, Mozambique.

The company has already planted approximately 2 200ha of pines and eucalyptus and small areas

of local and exotic hardwoods in an area known as the Malulu Block. NGR already has the legal

title, a DUAT, for the Malulu block and, since environmental approval has been received, the

Malulu Block is not the subject of this report.

NGR have also secured an area of 4 374ha for potential reforestation, referred to as the Malica

and Ntiuile blocks, which are the areas that have been considered during this impact assessment.

The project involves clearing areas of degraded land (in other words, land that has been used for

subsistence agriculture and grazing). Following this, tree saplings from Green Resources’

nurseries will be gradually planted in blocks to establish stands of trees of the same age. These

will be grown to full maturity and then felled using heavy machinery. During the growing process,

fertilization, chemical or manual weed suppression, thinning, pruning and intermediate harvesting

may be undertaken. Since the operation is based on even-aged stands, large areas may be left

without forest cover for some years during regeneration. The project will also require workshops for

vehicle maintenance, nurseries for the propagation of seedlings, and administrative areas and

possibly staff accommodation. The harvesting of the plantation will be in accordance with

international best practice.

1.2 Terms of reference

This component of the study assesses the hydrological impact of the proposed afforestation. The

evaluation has focused on the 4 374ha referred to as the Malica and Ntiuile blocks but also

comments on the full proposed area of 32 133ha as indicated in Figure 3.1 (sourced from the

Terms of Reference). This full area of 32 133 ha includes the 2 200ha already planted in the

Malulua Block.

1.3 Structure of the report

The report firstly defines the study area in the context of river catchments, since the hydrological

impact will be dictated by the catchment delineation. The methodology used to estimate the

hydrological impact of the afforestation is then described, followed by a description and results of

the analyses carried out. The report concludes with a description of the potential impacts of the

afforestation as well as possible mitigation measures.

1.4 Study team

This hydrological analysis was carried out by Mr. SJL Mallory with assistance from Dr. T

Sawunyama, both from the company IWR Water Resources.



2 METHODOLOGY

In order to accurately assess the impact of afforestation on streamflow it is generally necessary to

firstly quantify the streamflow under natural conditions before the impact of the forestry can be

determined. The Niassa catchment, which is the subject of this evaluation, is challenging in that

there is no hydrological information available. Also, no research has been carried out in this region

to ascertain the extent to which afforestation reduces runoff. The approach taken in this study

hwas therefore to rely on the many years of research in South Africa and extrapolate this

knowledge and the methodologies developed in South Africa to estimate streamflow reduction due

to afforestation to the Ntiuile and Malica project areas.

The methodology used in this study is described in a paper by Mallory and Hughes (2011). The full

paper is attached as Appendix A, while a brief summary of the methodology is provided here.

The first steps taken in South Africa towards estimating the reduction in streamflow due to

commercial afforestation were through so-called Paired Catchment studies, commencing in

Jonkershoek in the Western Cape and then extending to Cathedral Peak in the Natal Drakensberg

(1945) and Mokobulaan (1955) near Lydenburg, followed by Westfalia (1975) and Witklip (1980).

The outcome of these Paired Catchment studies is encapsulated in methods such as the Nänni

curves , the Van der Zel curves and the CSIR (or Scott) curves (see Appendix A for references).

The shortcoming of all of these methods is that, while they were derived from actual

measurements of runoff reduction, the research was limited to a few catchments, all with relatively

high Mean Annual Precipitation (MAP) in the range of 1 100 to 1 400 mm/annum. These curves

are therefore not representative of all forestry regions in South Africa, and probably not for the

Ntiuile and Malica project areas either.

In order to overcome these shortcomings, the Water Research Commission (WRC), in conjunction

with the then Department of Water Affairs and Forestry (DWAF), commissioned a study using the

ACRU model (Schulze, 1995). The outcome of this study was runoff reduction tables - the Gush

tables - for each quaternary catchment, which take into account the local conditions, that is,

rainfall, evaporation, soil depth and type. (Gush et al, 2002). The Gush tables were a huge step

forward in streamflow reduction (SFR) estimation at that time, and were accepted by DWAF and

the forestry industry. The next logical step, taken by Mallory and Hughes (2011), was to

incorporate the Gush data into water resources models so that scenario modelling could be

undertaken. This work took place in 2006 and has been applied in South Africa for more than five

years. The methodology was made known widely through training of South Africa’s DWAF officials,

but only published recently in 2011.

Given the fact that the Gush tables (Gush et al, 2002) are widely accepted and are being used in

practice, steps were taken to incorporate these into a water resources model so that the impact of

the streamflow reduction on water availability could be estimated. The Gush tables are limited in

that they only produce a mean and low flow reduction in runoff, which cannot be readily applied in

a water resource model in order to determine the impact of the reduction in runoff on the

availability of water to other users. The first step towards utilising the Gush tables was therefore to

request the time series which lie behind these average and low flow values. These had, however,

not been retained by the Gush research team, so the researchers involved kindly re-ran the ACRU

simulation and generated monthly duration curves of the reduction in runoff. These duration

curves, or extended tables, were produced for three exotic genuses, namely Pine, Eucalyptus and

Wattle. Duration curves of the assumed baseline, that is, runoff under natural Acocks type

Commented [S1]: The approach is not ideal but considering the lack of observed data in this catchment is the only other solution. The conditions in the South African catchments in which the forestry research was carried out are similar to the catchment considered in this report.

Commented [NP2]: How reliable/good is this? Are the conditions in SA areas similar to NGR operation areas?

Commented [NP3]: Would be good to know the values to see if the conditions are comparable



vegetation, as defined by Acocks (Ackocks ????), were also produced. This is equivalent to the

concept of natural flow with which most hydrologists are familiar. This then allows water resources

modellers to produce time series of reduction in runoff, which is essential for any time series

modelling upon which licensing decisions should be based and impact assessments evaluated. An

example of these extended tables is given in Table 2.1 (wet season) and 2.2. (dry season)

The data in Tables 2.1 and 2.2 are presented graphically in Figures 2.1 (for the month of January

wet season) and 2.2 (for the month of September – dry season) for a catchment in South Africa

referred to as X22D. The streamflow under Eucalyptus and Pine has been included in Figures 2.1

and 2.2.These graphs shows that during naturally low flow conditions the runoff from this

catchment will be severely reduced compared with natural conditions if fully planted to either Pine

or Eucalyptus.of these types of commercial forestry common in South Africa.

Table 0.1: Gush duration curve for the X22D quaternary catchment in South Africa for the month of January (Eucalyptus, Pine and Acocks vegetation)

Land Cover % Exceedence

10% 20% 50% 80% 90%

Runoff (mm) with 100% cover

Natural 109.7 77.9 53.1 25.0 17.8

Eucalyptus 70.0 44.6 24.0 5.8 3.1

Pine 75.9 51.5 27.1 11.0 6.5

Table 0.2: Gush duration curve for the X22D quaternary catchment in South Africa for the month of September (Eucalyptus, Pine and Acocks vegetation)

Land Cover % Exceedence

10% 20% 50% 80% 90%

Runoff (mm) with 100% cover

Natural 17.6 13.6 8.4 2.9 2.3

Eucalyptus 9.9 6.9 2.7 1.0 0.4

Pine 13.8 9.3 5.7 2.1 1.6

Figure 0.1: Runoff duration curves under various land use scenarios in January

Commented [NP4]: ???not clear

Commented [NP5]: ???



Figure 0.2: Runoff duration curves under various land use scenarios in September

While it is obvious that the runoff from the catchment is less when planted with Eucalyptus and

Pine than under natural conditions, but it is not immediately obvious to what extent the runoff is

being reduced. This is shown in Figures 2.3 (January) and 2.4 (September).

Figure 0.3: Reduction in runoff duration curves (January)



Figure 0.4: Reduction in runoff duration curves (September) The advantage of expressing streamflow reduction in a dimensionless format as shown in Figures

2.3 and 2.4 is that these relationships can be applied to any natural hydrology. In other words, they

can be used in other catchments, the proviso being that the curves should be applied in a

catchment with similar rainfall, evaporation and soils. The X22D catchment, located in the

Crocodile catchment ins South Africa, was selected as similar to the catchments in Mozambique

under consideration A comparison of the X22D and the Mozambique catchment is shown in Table

2.3.

Table 0.3: Comparison of climatic variables in the SA catchment X22D and the Ntiuile and Malica project areas in Mozambique

Catchment MAP (mm/annum) A-Pan evaporation Altitude (mamsl)

X22D 1 178 1 640 900 to 1 300

Ntiuile and Malica

project areas 1 100 1 265 1 000 t0 1 400

Due to the apparently lower A-Pan evaporation in the Ntiuile and Malica project areas the

estimated streamflow reduction using the X22D catchment parameters will slightly overestimate

the streamflow reduction.

Figure 2.5 shows the streamflow reduction duration curves for the whole year while the curves for

each month are attached in Appendix B.

Commented [NP6]: Good as precaution.



Figure 0.5: Reduction in runoff duration curves (annualised)

Figure 2.5 reflects conventional wisdom (supported by many years of research) that streamflow

reduction is relatively more severe during dry periods than wet periods and that Eucalyptus

reduces runoff more that Pines. There is ample anecdotal evidence that catchments that are

completely covered by Eucalypts have no runoff at all during dry periods. It would appear that the

situation will not be as severe within the Ntiuile and Malica project areas due to the relatively high

rainfall and low evapotranspiration. Nevertheless, within the planted area, a streamflow reduction

of up to 80% is possible during droughts based on the modelling procedures used..

Commented [NP7]: ???Evidence??? how was estimated the reduction?



3 DESCRIPTION OF THE STUDY AREA

The study area is referred to further in this report as the Ntiuile and Malica project area. These

areas are indicated in Figure 3.1 (sourced from CES’s Terms of Reference).

Figure 2.1: The Ntiuile and Malica Project Area Source: CES Terms of reference

From the available literature for the project area (Greaves and Hallowes, 2006) as well as Google

EarthTM images, it was established that, other than domestic water use, there is no significant

water use in the Ntiuile and Malica project area or the extended area of the Lucheringo river

catchment, which includes the Malulu Block. Agriculture appears to be all dry-land supported by

the ample summer rainfall. An assumption was made that an individual person requires 25 litres of

potable water per day. Therefore, an estimate of the total population in Lucheringo River

catchment was obtained (Greaves and Hallowes, 2006). The water use estimated for Lucheringo

river catchment is approximately 0.45 million m3/annum which is not a significant abstraction.

Nevertheless, since water for domestic purposes is probably abstracted from run-of-river, reduced

river flow could impact on this domestic use. Provision of storage would alleviate this negative

impact.

Table 3.1 shows data on forestry areas already acquired (Malulu block) as well as the total area

that would like to be acquired in future. While the focus of this study is to assess the impact of the

4 374ha within the Ntiuile and Malica project areas, the impact of the full development has also

been assessed.

Commented [NP8]: Population size may have changed in 5 years how was this ccounted for in the calculations/water consumption estimates?

Commented [S9]: If this information was provided at the time of the study it could have been taken into account. The population and the domestic use is very limited relative to the runoff and not significant.

Commented [NP10]: ???



Table 3.1: Total areas to be acquired for re-afforestation

Block Name Forestry Area (ha)

Malulu 5787 (2 200 ha already acquired)

Impo2 2985

Bagarila1 2986

Bagarila11 1084

Majica 14251 (Ntiuile project area falls within this area)

Licole 5040 (Malica project area falls within this area)



4 IMPACT ASSESSMENT

4.1 Reduction in runoff assessment

A water resources yield model (WReMP model of Mallory et al 2010) was set up for the sub-

catchments shown in Figure 4.1. This sub-catchment of the Lucheringo River includes the Ntiuile

and Malica project areas as well as the Malbulu block. The sub-catchment delineation was

obtained from the report by Greaves and Hallowes (2006), who determined this delineation using a

digital terrain model.

Figure 3.1: Map showing sub-catchments used in yield modelling (Source: Greave and Hallowes, 2006)

The Water Resources Modelling Platform (WReMP) (Mallory et al, 2010) was chosen since this

has been used extensively in Southern Africa to model streamflow reduction activities due to

afforestation and invasive alien plants. This model, as with any water resources model, requires

reference or natural flow as basic input. Flow through the system is then simulated on a monthly

time step by applying the continuity equation within a node and channel system. Nodes represent

catchments or points in a catchment at which water is abstracted (or added) while channels are

used to define the connectivity of the nodes and can be thought as rivers. The node and channel

setup for the Niassa catchment is indicated in Figure 4.2

Formatted: Font: Italic



Figure 4.2: WReMP System diagram

Natural or reference hydrology was obtained from Greaves and Hallowes (2006) who used the

ACRU model to simulate runoff from the catchments indicated in Figure 4.1. It is important to note

that there is no observed flow data in the Ntiuile and Malica project areas or the Lucheringo River

catchment and hence there is a considerable uncertainty in these natural flow time series.

However, the methodology presented in section 3 is based on proportional and not absolute

reduction in runoff and is therefore not very sensitive to inaccurate natural flow time series.

The ACRU reference hydrology is at a daily time step. It was therefore necessary to aggregate

these time series to monthly flows. The reference hydrology is summarised on Table 4.1.

Table 4.1: Reference or natural hydrology

Catchment Name Catchment Area

(ha)

Mean Annual runoff (incremental)

(million m3/annum) mm/annum

Lucheringo1 39 290 190 484

Lussanhando 12 798 62 484

Lucheringo2 41 039 161 393

Luelele1 21 769 90 413

Luelele2 21 611 87 403

Luchiuma1 44 316 197 444

Luchiuma2 13 405 50 373

Luculece 38 764 164 422

Lucuise6 29 518 120 407

The unit runoff from these catchments appears to be somewhat on the high side, but since the

catchment is not gauged there is no basis for rejecting the hydrological analysis of Greaves and

Hallowes (2006). For comparison purposes, the natural unit runoff of the X22D catchment is 316

Commented [NP11]: Won’t this be adding to the already existing uncertainties?

Commented [S12]: No, aggregation reduces uncertainty while dissagregation increases uncertainty

Commented [NP13]: Gracindo? Temos dados de precipitacao para comparacao??



mm/annum under a similar rainfall regime. Given that the evaporation rates in the Niassa

catchment are lower than in South Africa’s X22D catchment, a higher unit unit runoff can be

expected in the Lucheringo River catchment.

Overlaying the proposed forestry areas given in Table 3.1 and shown in Figure 3.1, the proposed

forestry areas in each sub-catchment were estimated using Arcview 9.3. See Table 4.2. Applying

the WReMP Streamflow Reduction procedure, as described in Section 2, the stream flow reduction

in absolute terms, and as a percentage of the reference hydrology, is indicated in Table 4.2.

Table 4.2: Streamflow reduction

Catchment Forestry

Area (ha)

Natural Mean

Annual runoff

(million

m3/annum)

Streamflow reduction

million

m3/annum - mm/annum

% of

natural

MAR

Lucheringo1 190

Lussanhando 1 740 62 3.6 207 5.8%

Lucheringo2 2 183 161 3.6 165 2.2%

Luelele1 - 90

Luelele2 - 87

Luchiuma1

- Now 4 375 197 8.3 189 4.2%

- Future 21 389 197 40.4 189 20.5%

Luchiuma2

- Developed 2 200 50 3.2 146 6.4%

- Future 3 836 50 5.6 146 11.2%

Luculece 2 985 164 5.4 181 3.3%

Lucuise6 - 120

Table 4.2 shows that the proposed 4 375ha of forestry will reduce the runoff within the Luchiuma1

catchment by only 4.2% of the natural runoff. This is well within the acceptable norm of stream flow

reduction due to afforestation. If the catchment should be fully afforested to the final planned area

of 21 389ha, then the streamflow reduction would be 20.5% of the natural runoff. This is in excess

of the accepted upper limit of 20% reduction in runoff. Long-term development plans on this

catchment should therefore be limited to about 20 000a.

4.2 Assessment of low-flow reduction

Reduction in runoff is a fairly coarse method to assess the impact of forestry on a catchment. A

low-flow assessment is of more importance, especially where communities are abstracting water

directly from rivers and rely on these abstractions for their survival. A low flow assessment will

indicate whether or not rivers will dry up completely and, if not, estimate the minimum flow in the

river, before and after afforestation. See Table 4.3.

Commented [NP14]: ????



Table 4.3: Impact of forestry on assurance of supply at sub-catchment level

Catchment Name Forestry

Area (ha)

Run-of-river yield (million

m3/annum) Percentage change

Before

afforestation

With

afforestation

Lucheringo1 0 3.1 3.1 0%

Lussanhando 1 740 1.0 0.9 10%

Lucheringo2 2 183 6.7 6.5 3%

Luelele1 0- 1.3 1.3 0%

Luelele2 0- 2.6 2.6 0%

Luchiuma1

- Now 4 374 3.2 2.7 16%

- Future 21 389 3.2 1.9 41%

Luchiuma2

- Developed 2 200 14.0 13.0 7%

- Future 3 836 14.0 12.3 12%

Luculece 2 9850 2.6 2.5 8%

Lucuise6 0 1.6 1.6 0%

Commented [S15]: No, this is correct

Commented [NP16]: ???isn’t this too high??



5 CONCLUSIONS AND IMPACT STATEMENT

5.1 Impacts common to both project areas

5.1.1 Issue 1: Planting of 4 374ha of forestry in the Ntiuile and Malica block project area

Impact 1.1: Reduction in runoff (Mean)

Cause and comment

Exotic forests generally reduce surface runoff from a catchment because of increased rooting

depth and hence increased evapo-transpiration when compared to the indigenous vegetation that

they replace. Increased rainfall interception losses also contribute to a reduction in runoff. In the

Ntiuile and Malica block project area, the rainfall is relatively high and the mean reduction is

estimated to be about 60% within the immediate forestry footprint and assuming that Eucalyptus is

planted. See Figure 2.5.This impact will be somewhat less (52%) should Pine be planted rather

than Eucalyptus.

The impact of the proposed the Ntiuile and Malica forestry has been assessed on a catchment

basis. These blocks fall within the Luchiana 1 sub-catchment of the Lucheringo River. It is

estimated that the Ntiuile and Malica block project will reduce the MAR of this sub-catchment by

about 4% of the natural flow. This is not considered to be significant and local communities making

use of the rivers in this area are unlikely to be impacted in any noticeable way..

Mitigation measures

The most important mitigation measure when developing forestry is firstly not to plant in the

riparian zones of rivers or lakes and not to develop over wetlands. Forestry in these areas will use

up to three times as much water as forests in non-riparian zones. Another mitigation measure is

not to over-develop in any one sub-catchment. A rule of thumb is not to exceed a forestry area of

20% of the sub-catchment area. The proposed development within the Ntiuile and Malica project

area is will within this limit.

Impact statement:

Impact 1.1: Reduction in runoff (mean)

Without mitigation With mitigation

Temporal scale Spatial scale Certainty Severity Significance Severity Significance

Long term Local and Sub-

catchment

Definite Low Moderate Low Low

Impact 1.2: Reduction in Low Flow

Cause and comment

Commented [NP17]: Water quality assessments?? Baselines against which future monitoring exercises will take place? Recommendations???? Is water used by local populations likely to be affected (quantity/quality)? Recommendations in this regard??

Commented [NP18]: Considering an area with no tree cover as the ones suggested in the study, the increase in the tree cover will/may, at the same time, increase the amount of water the infiltrates on the soil increasing the ground water stocks. Was this also considered? Any analysis on the existing aquifers or any special features that should be take into account in the project areas? Anything that deserves special care apart from rivers?

Commented [NP19]: The areas proposed for the project implementation have been cleared from the original miombo trees which may have exposed them to higher evaporation rates and increased run off? Was this analysed?

Commented [NP20]: Is this considered a significant reduction? Can’t this be analysed in the whole catchment level to have an idea of the project impacts on the catchment level (look at the landscape level)? Any analysis done on the cumulative effects of the project vis –a-vis other existing uses?

Formatted: Font: Bold



The reduction in runoff due to afforestation is not uniform throughout the temporal flow regime but

tends to be more pronounced, proportionally, during low-flow periods. In dryer areas with rainfall

less than 1 000 mm/annum it is not uncommon for exotic forestry to completely eliminate low flow.

In the Ntiuile and Malica block project area the rainfall is relatively high and the reduction in low

flow (in the forested area) is estimated to be about 80% on the forested area based on Eucalyptus

and about 70% based on Pine. It is therefore important to limit the area of forestry developed within

a catchment. As a rule-of-thumb forestry development should be limited to 20% of any particular

catchment.

The impact of the proposed the Ntiuile and Malica forestry on the sub-catchment’s low flow,

expressed as run-of-river yield, is estimated to be a 16% reduction of the run-of-river yield

available under natural conditions.

Mitigation measures

The same mitigation measures that apply to the mean reduction in runoff apply to low flow

reduction. Construction of dams is the most common likely mitigation measure to cope with low

flow reduction but is not recommended since this also has an ecological impact. Another option is

to source water for local communities from groundwater or from neighbouring undeveloped

catchments.

Impact 1.2: Reduction in low flow



Long term Local and Sub-

catchment

Definite Moderate Moderate Moderate Moderate

Impact 1.3: Reduction in the water table

Cause and comment

The impact of afforestation on groundwater tables has not been well researched and there is

insufficient data within the Ntiuile and Malica project areas to assess this in detail. It is generally

accepted that the impact of forestry on the water table is negligible. There are exceptions, for

example, where forestry is planted over a dolomitic zone or on a perched water table. The geology

of the Ntiuile and Malica project area is however not dolomitic, so there is minimal chance of the

water table being significantly affected.

Mitigation measures

Provided forestry is kept out of riparian zones and wetlands, the impact on groundwater levels

should be negligible.

Impact 1.3: Reduction in water table


Commented [NP21]: Meaning??? Should we get worried?? What can we do apart from avoiding riparian areas?

Commented [NP22]: idem

Commented [NP23]: was option from sourcing water from boreholes considered??





Long term Local Possible Low Low Low Low

Impact 1.4: Increased erosion

Cause and comment

Forestry activities result in increased erosion from their immediate footprint and this will result in

increased turbidity of downstream rivers during high to moderate rainfall events. The reason for

this is that the trees are felled periodically (on a 10 to 15 year cycle) and for a short period the land

will be devoid of vegetation and prone to erosion should heavy rain fall during this period. The

roads that are constructed to access the timber are also a source of sediment load.

Mitigation measures

There are several ways to limit the erosion to within acceptable limits. Firstly, the harvesting of the

timber should be on a continuous cycle so that only a small portion of the full area is ever clear

felled at any one time. Secondly, this harvesting should take place during the dry season when

rainfall is less likely and replanting should take place as soon as possible after harvesting. Thirdly,

the litter remaining after clear felling should be left in place until the new trees are established so

as to reduce erosion.

Impact 1.4: Increased erosion



Long term Local and sub-

catcment

Probably Moderate Moderate Low Low

Impact 1.5: Deterioration in water quality

Cause and comment

The perceived deterioration in water quality due to forestry is mostly due to increased turbidity

which is the result of increased erosion, as discussed under Impact 1.4. However, the use of

phosphates to speed up tree growth can and does lead to water quality in downstream water

courses.

Mitigation measures

The measures to limit erosion are


Commented [NP24]: Soils in the area are currently poor in terms of trees/vegetation and during rains it is visible that topsoil is being removed from the area. with increased vegetation cover this is likely to reduce with the improved infiltration and reduced run-off . hence sedimentation may also be reduced . What exactly shoud NGR do to avoid the possiblenegative impacts or to increase the possible positive impacts of establishment of plantations to reduce erosion?

Commented [S25]: See ‘Mitigation measures’ below.


Formatted: Font: Not Bold



6 REFERENCES Greaves, K and Hallowes, J. Hydrological assessment for Malonda Forestry Concession Areas. Niassa Province, Northern Mozambique. Report prepared for Impacto (Lda) Schulze, RE. 1995. Hydrology and Agrohydrology: A text to accompany the ACRU 3.00 Agrohydrology Modelling System. WRC Report TT69/95. Water Research Commission, Pretoria, RSA. Gush, MB, Scott, DF, Jewitt, GPW, Schulze, RE, Lumsden, TG, Hallowes, LA and AHM Gorgens. 2002. Estimation of streamflow reductions resulting from commercial afforestation in South Africa. WRC report TT173/02. Water Research Commission, Pretoria, RSA. Mallory, SJL, Desai, A, Odendaal, P. 2010. The Water Resources Modelling Platform. Users

Guide. Available on www.waterresources.co.za.

Mallory, SJL and Hughes, DA. 2011. Application of streamflow reduction models within a water

resources simulation model



7 APPENDIX A: PAPER ON THE METHODOLOGY USED Application of streamflow reduction models within a water resources simulation model

Stephen JL Mallory. Institute for Water Research, Rhodes University

Denis A Hughes. Institute for Water Research, Rhodes University

Abstract:

There are numerous methodologies for estimating stream flow reduction (SFR) due to commercial

forestry. However, many of these methods produce results only in terms of mean, median or low

flow reduction while water resources models require SFR expressed as time series. The other

shortcoming is that SFR estimates are estimated based on specific hydrological datasets while

more detailed water resources models are often using improved hydrological datasets which

invalidate the SFR estimate, at least in absolute terms. A model has been developed which

overcomes some of these problems by expressing SFR in a dimensionless format using flow

duration curves. Given the natural streamflow time series being used within the water resources

simulation, an SFR time series can then be generated by assuming that the probability of

exceedence of the SFR at any point in time is equivalent to the probability exceedence of the

natural flow at the same point in time. This SFR concept has been applied widely in numerous

water resources studies within South Africa. This paper explains the statistical basis of this

methodology and presents an example of its applications within a typical catchment.

Keywords: Streamflow reduction, forestry, water resources modelling

7.1 Introduction

The expansion of the forest industry was accelerated by the Great Depression in the 1930s, which

raised awareness of the impact of large-scale commercial forestry plantations on water resources,

and prompted concerns about the associated use of water. Van der Zel (1995) suggests that the

debate over reduction in runoff due to afforestation in South Africa started as far back as 1915.

However, research was only initiated in 1935 with the establishment of long-term paired catchment

afforestation experiments at the Jonkershoek Forest Hydrological Research Station.

In 1960 a severe drought brought commercial forestry and its impact on water supply to the fore.

Committees were formed, and concerns for the protection of South Africa’s water resources led to

the legislative control of commercial afforestation and forestry practices. The eventual outcome of

this was an amendment to the Forest Act (Act 72 of 1968), which required timber growers to apply

for permits to establish new commercial plantations. This legislation became known as the

Afforestation Permit System (APS).

South Africa’s National Water Act (NWA) (Act no 36 of 1998) replaced the APS in that it declares

commercial forestry to be a streamflow reduction activity (SFRA) for which a water use licence is

required. Section 36 of the NWA states, inter alia,

(3) In making a declaration under subsection (2), the Minister must consider -

(a) the extent to which the activity significantly reduces the water availability in the

watercourse;

The important and often overlooked point of this clause is that it refers to ‘water availability’ and not

SFR. Several methodologies are available to estimate SFR but none of them determine the

reduction in water availability. This paper describes a methodology which uses the latest estimates

of SFR to ultimately determine the impact of this SFR on water availability.



Estimating Streamflow Reduction The first steps taken in South Africa towards estimating the reduction in streamflow due to

commercial afforestation were through so-called Paired Catchment studies, commencing in

Jonkerhoek in the Western Cape and then extending to Cathedral Peak in the Natal Drakensberg

(1945), and Mokobulaan (1955), near Lydenburg followed by Westfalia (1975) and Witklip (1980).

The outcome of these Paired Catchment studies is encapsulated in methods such as the Nänni

curves (Nänni, 1988), the Van der Zel curves (Van der Zel, 1987) and the CSIR or Scott curves

(Scott and Smith, 1997). The shortcoming of all of these methods is that while they were derived

from actual measurements, the research was limited to a few catchments, all with relatively high

Mean Annual Precipitation (MAP). These curves are therefore not representative of all forestry

regions in South Africa.

In order to overcome these shortcomings, the Water Research Commission (WRC) in conjunction

with the then Department of Water Affairs and Forestry (DWAF) commissioned a study using the

ACRU model (Smithers and Schulze, 1995). The outcome of this study was reduction in runoff

tables for each quaternary catchment which takes into account the local conditions, that is, rainfall,

evaporation, soil depth and type, etc. (Gush et al, 2002). While the Gush tables were a huge step

forward in SFR estimation at that time and were accepted by DWAF and the forestry industry, it still

did not address the crucial aspect of ‘water availability’ referred in section 36(3) of the NWA.

7.2 Updates of the Gush tables to produce duration curves

Given the fact that the Gush tables (Gush et al, 2002) are widely accepted and are being used in

practice, steps were taken to incorporate these into a water resources model so that the impact of

the SRF on water availability could be estimated. The Gush tables are limited in that they only

produce a mean and low flow reduction in runoff which cannot be readily applied within a water

resource model in order to determine the impact of the reduction in runoff on the availability of

water to other users. The first step towards utilising the Gush table was therefore to request the

time series which lie behind these average and low flow values. These had however not been

retained by the Gush research team so the researchers involved kindly re-ran the ACRU simulation

and generated monthly duration curves of the reduction in runoff. These duration curves, or

extended tables, were produced for three exotic genuses, namely Pine, Eucalyptus and Wattle.

Duration curves of the assumed baseline, that is runoff under Acocks type vegetation (Acocks,

1988), were also produced. This is equivalent to the concept of natural flow that most hydrologists

are familiar with. This then allows water resources modellers to produce time series of reduction in

runoff using the method described by Hughes and Mallory (2009), which is essential for any time

series modelling upon which licencing decisions should be based. An example of these extended

tables is given in Table 1.

The data in Table 1 are presented graphically in Figure 1 (for the month of January only) for

quaternary catchment X11A. The streamflow under Pine and Wattle has been included in Figure

1.This graph shows that during naturally low flow conditions, there will be no runoff from this

catchment if fully planted to any of the three types of commercial forestry common in South Africa.



Table 1: Gush duration curve for selected quaternaries for the months of January (Eucalyptus and Acocks vegetation)

Quaternary catchment % Exceedence

10% 20% 50% 80% 90%

Runoff (mm) with 100% Eucalyptus cover

X11A 10.7 5.7 0.3 0 0

X11B 5.7 2.9 0.1 0 0

X11C 8.9 5.7 0.6 0 0

X11D 11.2 8.3 0.8 0 0

X11E 11.5 8.5 0.8 0 0

X11F 15.2 12.1 1.3 0 0

X11G 18.7 12.9 2.1 0.4 0.2

X11H 25 17.6 3.2 0.2 0.1

X11J 41.9 32.8 5.4 1.1 0.6

Runoff (mm) with Acocks vegetation

X11A 32.8 23.4 10.7 4.3 1.8

X11B 29.4 21.1 9.8 2.2 0.7

X11C 30.4 24.3 12.2 3.1 1.5

X11D 36 28.7 14.9 5.6 3.1

X11E 38.4 31.2 16.2 6.8 3.4

X11F 41.8 36.4 16.8 6.4 3.5

X11G 50.7 41.4 19.7 10.4 5.7

X11H 61.2 42.8 18.3 11.2 8.3

X11J 77.4 59.8 27.6 15.8 12.3

Figure 7.1: Streamflow under various landcover scenarios for the month of January. However, of more relevance is the extent to which the runoff is reduced relative to the natural or

reference hydrology. This is calculated simply as the difference between the Acocks runoff and the

runoff under afforested conditions. Figure 2 plots the reduction in runoff as a percentage of the

natural or reference runoff.

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Figure 7.2: SFR expressed as a percentage of natural streamflow (month of January only) The reason for expressing the SFR as a proportion of the natural flow is that this can now be

applied to different ‘natural’ hydrology sequences. The water resource models generally used in

South Africa use monthly time step Pitman (Pitman, 1973) hydrology which has been calibrated

against observed flow. It would be incorrect to use the SFR produced by the ACRU model directly

in a water resources simulation based on different hydrological data, but accepting the proportional

runoff reduction duration curves a new time series of SFR can be produced that is relevant to the

hydrology used in the water resources models. This is the same technique used to generate time

series of ecological water requirements from duration curves used by Hughes and Mallory (2009)

and is repeated here in Figure 3 for clarity.

Figure 7.3: Diagrammatic illustration of the process of constructing a time series of Ecological Flow Requirement from natural flows (the example month is a December)

7.3 Application of the SFR method within a modelling framework

The methodology described in above has been incorporated into a water resources model referred

to as the Water Resources Modelling Platform (WReMP) (Mallory et al 2010). This allows the

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Eucalyptus Pine Wattle

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1980 1981 1982 1983 1984 1985

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Natural Flows

Environmental Flows



seamless modelling of streamflow reduction by commercial forestry. Rather than preparing a

streamflow reduction time series using other models, such as WRSM 2000 (Pitman et al, 2006),

the SFR is calculated at every time step during a WReMP simulation. The SFR is calculated for

each of the tree types and summed to give the total SFR at each time step. This calculation is

done on a quaternary catchment basis since the SFR duration curves derived from ACRU are

available for each quaternary catchment. More recent developments by Jewitt et al. (2009) have

resulted in daily time series SFR at quinary catchment scale and the intention is to incorporate

these new data into WReMP.

A typical input file for the WReMP forestry module is shown in Figure 4. Note that each catchment

requires a reference to a quaternary catchment. This is used as a database index to the relevant

duration curve produced by ACRU. Note also that catchment area is also a required input since the

ACRU streamflow data is expressed in mm and to calculate runoff (and SFR) as a volume requires

the catchment area. Should quaternary catchments be used as the spatial modelling resolution, the

catchment area can be referenced from a database, but the user may change this area if other

catchment boundaries are used, as would be the case when using this model outside of South

Africa or using catchment boundaries that deviate from the WR90 quaternary catchment definition.

Figure 7.4: WReMP Forestry data capture table Scenario analysis is possible simply by changing the areas of Eucalyptus, Pine and/or Wattle, re-

running the model and comparing yields at key points in the catchment under the various

scenarios. A graphical display of the calculation procedure is provided within the model, more as a

tool to explain the procedure to modellers. A typical SFR output screen from WReMP is shown in

Figure 5. Where this figure refers to WR90 this is actually a reference to the hydrology used in

WReMP.

Graph 1 (top left) plots the Acocks and WR90 (Midgley et al, 1994) natural runoff in mm as

duration curves. Note that in this particular example the Acocks hydrology indicated considerably

less flow than the WR90 hydrology. The red, green and blue lines represent the reduction in runoff

in mm due to Eucalyptus, Pine and Wattle respectively.

Graph 2 (top right) expresses the reduction in runoff as a percentage of the Acocks hydrology.



Applying the proportion reduction in runoff given in Graph 2 to the Pitman (WR90 or WR2005)

hydrology given in Graph 1, the reduction in runoff in mm for each tree species is given in Graph 3

(bottom left).

Applying these adjusted reductions in runoff for each tree species, given the afforested area, a

time series of the total reduction in runoff volume can be generated. This is shown in Graph 4

(bottom right) together with the hydrology used in WReMP.

7.4 Conclusion and recommendations

This paper presents a method to comprehensively and consistently model the impact of forestry on

the water resource within a modelling framework without having to use other models or refer back

to them for scenario analysis. The main features of this method are:

It uses duration curves of SFR generated by ACRU (Smithers and Schulze, 1995). This is

an update of the Gush tables published in 2002 (Gush et al, 2002). These curves represent

an advance on the Scott curves (Scott and Smith, 1997) in that curves are available for

each quaternary catchment and for three tree exotic types typically grown commercially in

South Africa.

The SFR is determined relative to the hydrology used within the water resources model.

This would typically be Pitman based hydrology such as WR90 (Midgley et al, 1994) or

WR2005 (Middleton and Bailey, 2005).

The reason for adopting this approach is that the Gush tables are widely accepted by the forestry

industry and have already been applied for CMA charges, while Pitman hydrology is the accepted

hydrology for licencing purposes. Improvements in the ACRU SFR estimates published by Jewitt et

al. (2009) need to be incorporated into the modelling procedure described in this paper.



Figure 7.5: Application of the Gush duration curves for forestry to determine a reduction in runoff time series



7.5 References

Acocks, JPH, 1988. Veld types of South Africa. Botanical Research Institute, Pretoria. Botanical Survey of South Africa, Memoirs 57, 146. Gush, M, Scott, D, Jewitt, G, Schulze, R, Lumsden, T, Hallowes, L and GÖrgens A, 2002. Estimation of streamflowreductions resulting from commercial afforestation in South Africa. Water Research Commission Report TT 173/02, Pretoria, South Africa. Hughes, DA and Mallory, SJL, 2009. Opportunities and constraints associated with implementing environmental flow requirements in South Africa. Paper presented at the International Conference on Implementing Environmental Water Allocation, Port Elizabeth, South Africa. Jewitt, GPW, Lorentz, SA, Gush, MB, Thornton-Dibb, S, Kongo, V, Wiles, L, Blight, J, Stuart-Hill, SI, Versfeld, D and Tomlinson K, 2009. Methods and Guidelines for the Licensing of SFRAs with Particular Reference to Low Flows. Water Research Commission Report No. 1428/1/09. Pretoria, South Africa. Mallory, SJL, Desai, A and Odendaal P, 2010. Water Resources Modelling Platform: User Guide Version 3.2. www.waterresources.co.za Middleton, BJ and Bailey, AK (2008) Water Resources of South Africa, 2005 study (WR2005), Water Research Commission Report, K5/1491, Pretoria, South Africa. Midgley, DC, Pitman, WV, and Middleton BJ, (1994) Surface Water Resources of South Africa 1990, Volumes I to VI. Water Research Commission Reports No. 298/1.1/94 to 298/6.1/94, Pretoria, South Africa. Nänni, UW, 1970. Trees, water and perspectives. South African Forestry Journal 75, 9-17. Pitman, WV, 1973. A mathematical model for generating monthly riverflows from meteorological data in South Africa. Report 2/73, Hydrological Research Unit, University of the Witwatersrand, South Africa. Pitman, WV, Kakebeeke, JP and Bailey, AK, 2006. Water Resources Simulation Model for Windows: Users Guide. Compiled for the Water Research Commission, Pretoria, South Africa. Scott, DF and Smith, DE, 1997. Preliminary Empirical Models to Predict Reductions in Total and Low Flows Resulting from Afforestation. Water SA 23, 135-140. Smithers, JC and Schulze, RE, 1995. ACRU Agrohydrological Modelling System: User Manual Version 3. Water Research Commission, Report No. TT70/95, Pretoria, South Africa. Van der Zel, DW, 1987. Hydrological Implications of the changing role of forestry in a catchment context. In: Proc. 3rd South African National Hydrological Symposium, Volume 2: 671 – 679. Van der Zel, DW, 1995. Accomplishment and dynamics of the South Africa Afforestation Permit System. South African Forestry Journal 172, 49 – 58.



8 APPENDIX B: STREAMFLOW REDUCTION DURATION CURVES FOR EUCALYPUTS AND PINE

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4. hydrological impact assessment green resources... · 2017. 6. 13. · ngr have also secured an...

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