Identifying hotspots for investment in ecological infrastructure within the uMngeni catchment, South Africa

Journal Pre-proof Identifying hotspots for investment in ecological infrastructure within the uMngeni catchment, South Africa S. Gokool, G.P.W. Jewitt PII:

S1474-7065(19)30028-2

DOI:

https://doi.org/10.1016/j.pce.2019.11.003

Reference:

JPCE 2807

To appear in:

Physics and Chemistry of the Earth

Received Date: 8 April 2019 Revised Date:

27 August 2019

Accepted Date: 1 November 2019

Please cite this article as: Gokool, S., Jewitt, G.P.W., Identifying hotspots for investment in ecological infrastructure within the uMngeni catchment, South Africa, Physics and Chemistry of the Earth (2019), doi: https://doi.org/10.1016/j.pce.2019.11.003. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

IDENTIFYING HOTSPOTS FOR INVESTMENT IN ECOLOGICAL INFRASTRUCTURE WITHIN THE UMNGENI CATCHMENT, SOUTH AFRICA S Gokool1 and GPW Jewitt1&2 1

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School of Agriculture, Earth and Environmental Sciences, Centre for Water Resources Research,

University of KwaZulu-Natal, Pietermaritzburg, South Africa 2

IHE Delft Institute for Water Education, Department of Water Science and Engineering, Netherlands

Corresponding Author: Shaeden Gokool, [email protected], University of KwaZulu-Natal, Pietermaritzburg, South Africa, 3201

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ABSTRACT

In recent times, there has been a growing recognition of the role which ecological infrastructure (EI) can play within the water resources management arena. However, practice has generally lagged conjecture regarding the integration of EI in the water resources management decision-making process. In this study, we aim to demonstrate how the state-of-the-art in ecosystem service modelling

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can be implemented to guide decision making with regards to investments in EI to improve the delivery of specific hydrological ecosystem services (HES) within the uMngeni catchment. For this purpose, the Resource Investment Optimization System (RIOS) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models were applied to identify priority areas for investment and to evaluate the HES benefits that can potentially be achieved. Several global and local

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data sets were used to provide the requisite inputs to perform alternative land management scenario simulations. The results of these simulations demonstrated that potential investments in EI within the uMngeni catchment can lead to substantial reductions in sediment export (≈ 47 %). Although, this may be accompanied by marginal decreases in the surface water yield (≈ 1.40 %), there is a net benefit associated with reducing sediment export. Despite, these investigations being limited in their

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representation of HES due to inter alia the lack of localized data sets and inherent model limitations, the study successfully demonstrated how the RIOS and InVEST models can be collectively applied to guide decision-making regarding investments in EI.

Keywords:

Ecological Infrastructure, Hydrological Ecosystem Services, Priority Areas, RIOS, InVEST

1. INTRODUCTION

Numerous countries are challenged by the need to secure future water supplies due to the growing demand for water to support and increase socio-economic development, coupled with the ever-expanding population and risks and uncertainties associated with the impacts of 5

land use and land cover changes (LULC), as well as climate change (Molle et al, 2010; Pittock and Lankford, 2010; Hedden and Ciliers, 2014). Typically, the societal response to securing water resources for present and future generations has been to invest in grey infrastructure (built infrastructure) (Ozment et al., 2015; Mander et al., 2017). Such investments have significantly contributed to enhancing the benefits that society receives

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from a catchment. However, from a South African perspective such an approach may no longer be viable, with approximately 98 % of available water resources supplies being fully allocated and limited dam sites available for future developments (Hedden and Ciliers, 2014). Several studies have demonstrated that unabated development within catchments to maximize socio-economic benefits through consumptive water use at the expense of the environment

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will inevitably result in the deterioration of these catchments ability to provide hydrological ecosystem services (HES) (Wester et al., 2005; Brauman et al., 2007; Venot et al., 2007; Falkenmark and Molden, 2008; Molle et al, 2010; Pittock and Lankford, 2010; Palmer et al., 2015; Jewitt et al., 2016). Furthermore, in an era of economic frugality coupled with uncertainty relating to the effects of anthropogenic induced climate and LULC changes,

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exclusively investing in grey infrastructure to enhance water supplies may not be the most feasible or sustainable management decision for the future (Hedden and Ciliers, 2014; Ozment et al., 2015). The emergence of science and policy that promotes natural resource management values into future economic development strategies (Kiesecker et al., 2010, 2011; Laurance et al., 2014),

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has led to a growing recognition both locally and internationally of the role which ecological infrastructure (EI) can play in securing and enhancing water supplies (TNC, 2015; Palmer et al., 2015; Maze and Driver, 2016; Hughes et al., 2018a).

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EI can be defined as naturally functioning ecosystems that are able to produce and deliver a suite of ecosystem services (ES) to society (SANBI, 2014; Jewitt et al., 2016) and can be considered ecologically equivalent to grey infrastructure, as it is able to provide various HES inter alia; flood attenuation, increase in dry season base-flows, improved water quality and a 5

reduction in soil erosion (MEA, 2005). It is now accepted that the restoration, protection (maintaining EI in good condition) and management of EI, will form a critical aspect of successful water resources and sanitation management in South Africa in the future (Figure 1). Furthermore, Cumming et al. (2017) states that, investment in EI will allow for key focus areas of the sustainable development goals and South Africa’s National Development Plan to

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come to fruition.

Figure 1

Graphical illustration of the National Water and Sanitation Master Plan Philosophy (NWSMP, 2018)

Despite the potential benefits of investing in EI, practice has generally lagged conjecture 15

regarding the integration of EI into the water resources management decision making process (Goldstein et al., 2017). This can be largely attributed to resistance regarding a shift in thinking away from solely investing in grey infrastructure for water supply. 2

Furthermore, it can also be challenging to demonstrate the benefits of investing in EI, as there are often difficulties associated with identifying; i) the EI interventions to implement and their optimal locations within the catchment, ii) the total expenditure and distribution of capital between interventions and iii) potential biophysical and socio-economic returns on 5

initial investments. Traditionally, sophisticated hydrological models have been used to demonstrate how the execution of particular management activities can improve the delivery of HES across a range of spatial and temporal scales (Goldstein et al., 2017; Luke and Hack 2018). However, these approaches are constrained by factors such as; application effort, limited data availability for model implementation or validation and their inability to identify

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priority areas within a catchment for investment in EI (Goldstein et al., 2017). In recent years the development and advancement of specialized ES models have facilitated landscape screening to identify likely priority areas for investment in EI, as well as to provide a rapid overview of the potential benefits a particular management activity may have on HES within the catchment (Bagstad et al., 2013a, 2013b; Vogl et al., 2016b; Luke and Hack,

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2018). These models are predicated on simplified hydrological process representation, differing from the more traditional hydrological simulation models, with an emphasis on rapid assessments using easily accessible model input data (usually from global databases). Thus, the focus of their development has been to enable decision makers to make more wellinformed planning and management decisions across spatial scales, through the use of tools

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that require minimal data and application effort (Luke and Hack, 2018). In this study, we aimed to test and demonstrate how the state-of-the-art in ES modelling can be implemented to guide decision making regarding investments in EI within the uMngeni catchment to improve the delivery of HES. This involved an iterative process consisting of identifying priority areas for investment in EI and evaluating the potential biophysical

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benefits these activities may have on HES in the rapidly developing uMngeni catchment in KwaZulu-Natal, South Africa.

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2. MATERIALS AND METHODOLOGY

2.1

Study site

The uMngeni catchment is located within the KwaZulu Natal (KZN) Province situated in the eastern part of South Africa (Figure 2). The uMngeni catchment is further sub-divided into 12 5

quaternary catchments. In South Africa, primary catchments are typically broken down to the 4th level i.e. quaternary catchments for water resources management purposes. In this study, we have adopted the quaternary catchment scale as an appropriate sub-catchment level. The catchment generally experiences a warm sub-tropical climate, with majority of the rainfall being received during the summer months (October to March). According to

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Warburton et al. (2010) the rainfall received within the catchment is highly variable both inter- and intra-annually, with mean annual precipitation (MAP) ranging from 1550 mm in the strategic water source areas to 700 mm in the drier middle reaches (Summerton and Schulze, 2009). Mean annual potential evaporation exceeds MAP and ranges from 1567 to 1767 mm. The mean annual temperatures vary across the catchment with cooler conditions

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experienced in the escarpment regions (≈ 12 °C) to warmer conditions in the eastern region (≈ 20 °C). The altitude within the catchment varies from 1913 m.a.s.l in the western escarpment to sea level at the catchment outlet, where the uMngeni river drains into the Indian Ocean (Warburton et al., 2010; Mander et al., 2017). There are numerous LULC components and activities distributed throughout the uMngeni

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catchment. The predominant LULC characterising the upper reaches of the catchment include; intensive commercial afforestation, agricultural land (predominantly livestock farming) and wetlands. Whereas the lower reaches of the catchment are characterized by densely-populated urban areas and industrial zones (Mander et al., 2017). Moreover, Hughes et al. (2018a) states that invasive alien plants (IAP’s) are extensively distributed within the

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catchment, particularly infestations of wattle (Accacia spp).

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Figure 2

Location of the uMngeni catchment, along with LULC (adapted from EKZNW, 2011), quaternary catchments and elevation (adapted from NASA JPL, 2013)

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Although, the uMngeni river catchment occupies a relatively small portion of KZN, it is a significant contributing source of water to the population of the province, including the major cities of Durban and Pietermaritzburg. The delivery of water to the various water users within the province is currently facilitated by four major dams and an inter-basin transfer scheme (Umgeni Water, 2015).

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Presently, the uMngeni catchment is able to yield

approximately 1050 Ml d-1 at a 98 % assurance level of supply, which is inclusive of additional support in the form of transfers from the Mooi River (UW, 2014). However, it is 5

projected that the demand for water to sustain the growing population and economic development will soon exceed available supplies (UW, 2014), as illustrated in Figure 3.

Figure 3 5

A time-series comparison of supply versus demand in the uMngeni catchment as per, whereby the blue, red and black dotted lines represent the actual demand, projected demand and the existing system yield at a 98 % assurance level of supply, respectively (UW, 2014)

According to the UW (2014) Infrastructure Master Plan, substantial investments into grey infrastructure are being considered to address this growing demand for water in the 10

foreseeable future. Although grey infrastructure remains essential for the provision of water to the population at large, the current extent of degradation of EI and the potential risk of further degradation if a grey infrastructure focused development agenda is pursued will significantly compromise the ability of the natural system within the uMngeni catchment to optimally deliver a suite of HES (Jewitt et al., 2016).

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In light of this situation, relevant stakeholders within the catchment have established the uMngeni Ecological Infrastructure Partnership (Hughes et al., 2018a), which aims to demonstrate the potential role which EI can play in supporting, enhancing or in some cases substituting for existing grey infrastructure, through various research initiatives (Jewitt et al., 2016). Considering, the importance given to investment in EI within the uMngeni catchment, 6

this study site provides the ideal opportunity to demonstrate how the state-of-the-art in ES modelling can be implemented to guide decision making, regarding investments in EI. 2.2

Model overview

There are various decision-support tools that have been designed specifically for the purpose 5

of catchment scale ES assessments as detailed by Bagstad et al. (2013a; 2013b). In this particular study, the Resource Investment Optimization System (RIOS) and the Integrated Valuation of Ecosystem Services and Tradeoffs model (InVEST), developed by the Natural Capital Project were selected for application to model HES within the uMngeni Catchment. This decision was largely predicated upon their extensive application internationally,

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relatively low data requirements and practical application effort. According to Luke and Hack (2018), these models have been designed to work in tandem in order to facilitate the rapid determination of activity priority areas and assessment of the impacts these activities will potentially have on HES delivery (Guswa et al., 2014; Vogl et al., 2016a). An overview of this process is depicted in Figure 4.

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Figure 4

A conceptual representation of the combined implementation of RIOS and InVEST (adapted from Vogl et al., 2016a)

Vogl et al. (2016a) and Sharp et al. (2018) provide detail on the structure and 5

conceptualization of RIOS and InVEST respectively, however a brief narrative is provided herein. RIOS is an open-source standalone software tool that is operable independent of scale or location for annual or longer-term periods and can be run on any Windows operating system. The RIOS model is predicated on a scientific approach that aims to identify priority areas within a catchment for the investment in EI that will likely provide the greatest benefit

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to the environment and population at the lowest possible cost (Vogl et al., 2016a). The model consists of two sub-modules i.e. an Investment Portfolio Advisor and a Portfolio Translator, which produce various outputs that can be used to inform how investments should be made. The Investment Portfolio Advisor uses biophysical and socio-economic data to produce an investment portfolio. The portfolio is a map that indicates where particular activities should

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be undertaken within the catchment in order to acquire the greatest return on investment (ROI) for individual or multiple objectives (Vogl et al., 2016a). Once the Investment 8

Portfolio Advisor has been run, the Portfolio Translator module is implemented to generate scenarios that demonstrate the potential changes to the condition of the catchment, resulting from the implementation of the investment portfolio. Three major scenarios are created and displayed as LULC maps (Vogl et al., 2016a). These 5

essentially include; i) a baseline scenario reflecting the current LULC, ii) a transitioned scenario reflecting new LULC combinations and protected (conservation) areas (T1) and iii) a transitioned scenario reflecting new LULC combinations, with areas that were previously protected now being allowed to degrade (T2). The objective of this third scenario is to demonstrate the benefits of protection activities on HES (Vogl et al., 2016a). These scenarios

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can then be used as direct inputs to the InVEST suite of models to estimate the potential HES ROI associated with each of the scenarios. Similar to the RIOS model, InVEST is an open-source standalone software tool that has been developed to explore how LULC or climate changes will affect ES delivery at local, regional or global scales. The spatially explicit InVEST suite of models are predicated upon

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production functions to quantify and value ES delivered by the environment based on its condition and processes, using simplified representations of common hydrological relationships (Vigerstol and Aukema, 2011; Sharp et al., 2018). The requisite inputs to the model include raster and vector data, as well as tables containing biophysical coefficients pertaining to a particular LULC. Simulations within the model are

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performed on an annual time-step and results are calculated on a grid basis. This is achieved by disaggregating the catchment into pixels in accordance to the spatial resolution of the input data (Vigerstol and Aukema, 2011; Sharp et al., 2018). The InVEST suite of models can be used to quantify and map ES for terrestrial, freshwater or marine environments (Sharp et al., 2018).

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2.3

Model Configuration and Data Inputs

The first step in implementing the RIOS Investment Portfolio Advisor was to define the objectives of the study and provide the requisite data for these specific objectives. The model can then determine priority areas within the catchment to meet a particular objective. Additionally, objective weights, activity preference areas, and relative cost effectiveness are 9

taken into consideration to determine priority areas so that multiple objectives can be addressed simultaneously (Vogl et al., 2016a). The selected objectives for the presented study were informed by the research undertaken by Jewitt et al. (2016) and include; i) water yield enhancement, ii) dry-season baseflow 5

enhancement, iii) erosion control, iv) flood mitigation and v) water quality enhancement. To achieve these objectives, changes in LULC or management activities are required, which will ultimately influence ES delivery within the catchment. Different activities may elicit the same desired outcome, however the cost and location of these activities will differ. Subsequently, RIOS determines where these particular activities should be undertaken to

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provide the greatest ROI. Although, several transition activities are included in RIOS, only the “keep native vegetation” (protection), “assisted revegetation” (restoration) and “riparian management” options were selected. It should be noted that RIOS does not assist in the selection of activities (Vogl et al., 2016a), therefore the aforementioned activities were chosen based on the objectives detailed in Jewitt

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et al. (2016). RIOS utilizes a ranking model to determine the priority areas for investment. This approach is based on the premise, that a narrow set of biophysical and ecological factors will influence the efficacy of a transition achieving the designated objective, with much of this impact dependent on the conditions of the surrounding landscape. Subsequently, ranking scores which designate the potential effectiveness of a particular

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activity are assigned contingent to the condition of a particular pixel and its adjacent pixels (Vogl et al., 2016a). Although RIOS allows for modelling objectives and transitions to be weighted relative to each other, default weighting values available in RIOS were selected for our model simulations. The selection of these values ensures all objectives are considered equally when determining the transition scores and all transitions contribute equally to

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fulfilling the specified objectives (Vogl et al., 2016a). The user may change the relative weighting between objectives if they wish to prioritize a particular objective during the determination of priority areas. Similarly, the relative weights of the transition scores can be adjusted if a particular transition is more effective at achieving the desired objective. For example, Vogel et al. (2016) states that previous research may

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show that protection/maintenance is more effective than restoration with regards to increasing 10

low flows in the dry season. Therefore, a higher weighting would be assigned to protection activities within the portfolio. Furthermore, an option exists to limit activities to a particular region within the catchment using constraining layers (GIS polygon shapefile). This enables the user to prioritize regions 5

based on socio-political or environmental constraints, rather than being driven purely by economic efficiency. For our modelling simulations, we developed a layer to prevent riparian management activities from being undertaken in regions that are greater than 40 m from the active stream channel. RIOS provides two options to determine how available funds should be distributed among the

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transition activities (Vogl et al., 2016a). These include, the “floating budget” (solely based on economic efficiency) and “per-activity allocation” option (specify the expenditure for a particular transition activity) (Vogl et al., 2016a). The latter option was selected in this study, as it potentially allows for maximum expenditure on a particular activity, while still considering the transition scores, thus creating a diversified portfolio. The budget allocation

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per transition activity and the cost of implementation and management per hectare used during model simulations are provide in Table 1.

According to Vogl et al. (2016a) there are several factors that will influence the ensuing impacts from the implementation of the transition activities. A set of options which explicitly 20

account for these factors are provided to guide decisions regarding the development of LULC scenario maps, using the outputs from the Investment Portfolio Advisor. During this phase the following information is captured in the Portfolio Translator module; (i) the time taken for the change in LULC or management practice to occur, (ii) associated changes to the biophysical response of a particular area resulting from the implementation of a particular

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transition activity, (iii) as well as the degree of transition. For example, during our simulations it was assumed that 10 % of the natural vegetation would be replaced by bare soils if left unprotected and areas identified for restoration would elicit the biophysical response of the closest and most abundant natural LULC (determined within the model) if assisted revegetation activities were undertaken.

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Furthermore, it was assumed that 50 % of the original LULC would be transitioned to the new LULC in a year. Essentially, this means that within a period of one year, only 50% of the LULC will be modified by the proposed management activities. For example, only 50% of the bare soil LULC will be revegetated with indigenous vegetation. 5

During our analysis three LULC scenario maps were developed, these included; i) a baseline scenario depicting the current LULC, ii) transitioned scenario (T1) depicting new LULC combinations resulting from the implementation of management activities and iii) transitioned scenario which demonstrates new LULC combinations, with former protected areas being allowed to degrade (T2) (Vogl et al., 2016a). The resultant LULC maps, as well

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as the associated biophysical coefficient tables were then used as direct inputs to the InVEST suite of models to evaluate the influence of the potential changes in LULC on HES delivery. Although, multiple objectives were selected for the study, only the changes in water yield and sediment retention that are likely to occur if the investment portfolio is implemented, were evaluated. This decision was largely based on the availability of data, as all requisite data

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used to drive the InVEST Sediment Delivery (SDR) and Surface Water Yield models were either collected prior to the implementation of RIOS or output by RIOS. In contrast, there was a lack of input data needed to run the InVEST suite of models for evaluation of the remaining objectives. The biophysical spatial data layers used during the RIOS and InVEST modelling analyses are

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provided in Table 1. Global databases and datasets were used, where local or national data was unavailable. Furthermore, several of these biophysical spatial data layers required further processing to derive inputs such as the upslope source, downslope retention, riparian continuity and slope indices required by the RIOS Investment Portfolio Advisor (Vogl et al., 2016a).

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The derivation of these inputs was undertaken using the RIOS pre-processing toolbox (compatible with ArcGIS 10.4.1) included with the RIOS installation package. The coordinate system of all the spatial data layers were projected to the UTM coordinate system WGS 84 for the Southern Hemisphere (Zone 36). Due to the differences in spatial resolution associated with the various spatial data layers, all data sets were resampled to the resolution

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of the DEM (30 m) in order to homogenize the model results (Luke and Hack, 2018). 12

It should be noted that due to the limited biophysical information available during the study, generic values provided by Vogl et al (2016a) were used during the implementation of the Investment Portfolio Advisor. Subsequently, LULC data acquired from EKZNW (2011) was grouped into generalized classes, which fell within the range of classes detailed in Vogl et al. 5

(2016). This in turn influenced the choice of economic data used during model simulations, as generic cost values associated with the proposed management interventions were used instead

of

more

detailed

13

LULC

specific

costs.

Table 1

Summary of biophysical, social and economic input data

Biophysical and Social Data Digital Elevation Model LULC Soil Mean Annual Precipitation Mean rainfall of wettest month Rainfall Erosivity Mean annual actual and potential evapotranspiration LULC Biophysical Coefficients LULC Classification with Activities Location and number of beneficiaries Economic Data

Protection Activities

Restoration Activities

Riparian Management

Source NASA JPL (2013) Ezemvelo KZN Wildlife GeoTerrImage (2013) Wieder et al. (2014) Lynch (2004) Lynch (2004) Panagos et al. (2017)

Format Raster

Spatial Resolution (m) 30

Data set characteristic Global

Model RIOS and InVEST

Raster

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Local

RIOS and InVEST

Raster Raster Raster Raster

30 arc seconds (≈ 1000 m) 1 arc minute 1 arc minute 30 arc seconds (≈ 1000 m)

Global National National Global

RIOS and InVEST RIOS and InVEST RIOS RIOS

Raster

1000 m

Global

RIOS

.csv

N/A

Global

RIOS and InVEST

.csv Vector

N/A resampled to 30 m

RIOS RIOS

Budget Allocation

Cost per ha

Local Local Data set characteristic

and

Running et al. (2011) Adapted from default values provided in Vogl et al. (2016a) Adapted from Jewitt et al. (2016) Adapted from Stats SA Census (2011) Source Value based on the cost of rehabilitation and management of moderately degraded grasslands (Jewitt et al., 2016) Value based on the cost of rehabilitation and management of severely degraded grasslands (Jewitt et al., 2016) Post clearing/rehab management cost (Jewitt et al., 2016). According to Jewitt et al. (2016) this cost can be used as a surrogate for the management of healthy systems.

Assuming an initial total budget allocation of R10 000 000 per activity as a reference amount

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Model

R237.00

Local

RIOS

R7836.00

Local

RIOS

R150.00

Local

RIOS

3. RESULTS

The results of the RIOS modelling simulations (Figure 5), illustrates the most suitable locations for investment across a range of objectives. Furthermore, RIOS outputs a budget report indicating the expenditure and the total area that has been converted for a particular 5

activity (Table 2). This information can then be further summarized to determine the total area converted and costs associated with the recommended management activities by LULC type (Table 3).

Figure 5 10

Modelling results of the RIOS Investment Portfolio Advisor, indicating activity preference areas for investment in EI within the uMngeni catchment

The results of our RIOS modelling simulations show that the proposed management activities are primarily located in six sub-catchments, with the distribution of recommended management activities varying considerably between the various LULC types. Following the identification of priority areas for investment, the effects of the potential changes in LULC on 15

sediment and water yield were evaluated by undertaking simulations using the InVEST SDR and Surface Water Yield models, respectively. For this purpose, the sediment and water yield associated with T1 and T2 were determined and compared relative to the sediment and water yield associated with the present LULC (Figures 6 and 7). The two scenarios T1 and T2 were considered to determine the total benefit that can be achieved from the implementation of the

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activities. 15

Table 2

Summary of modelling results of the RIOS Investment Portfolio Advisor for the uMngeni catchment Distribution of activities within each sub-catchment (ha) Sub-catchment Protection Restoration Riparian Management U20A 3393 337 281 U20B 1862 6 530 U20C 1978 66 300 U20D 2205 4 597 U20E 3213 107 434 U20F 2184 566 679 U20G 5145 95 683 U20H 1842 92 154 U20J 6743 1 637 U20K 3528 0 303 U20L 5953 0 152 U20M 4147 2 176 Summary of total area and cost associated with the recommended activities Activity Total Budget Total Expenditure Area Converted (ha) Protection R10 000 000.00 R9 999 983.00 42194

Table 3

Restoration

R10 000 000.00

R9 999 404.00

1276

Riparian Management Total

R10 000 000.00 R30 000 000

R738 874.00 R20 738 261

4926 48396

Summary of total area and cost associated with the recommended management activities by LULC type Total area (ha) of recommended activities by LULC

LULC Natural Forest Grassland Wetlands Shrubland Commercial Agriculture Commercial Forestry Bare Soil Total (ha)

Protection Restoration Riparian Management 35679 0 1189 3508 0 1062 2993 0 201 14 0 611 0 0 771 0 0 862 0 1276 229 42194 1276 4926 Total Cost (R) of recommended activities by LULC LULC Protection Restoration Riparian Management NaturalForest 8455856 0 178283 Grassland 831495 0 159336 Wetlands 709408 0 30222 Shrubland 3224 0 91712 Commercial Agriculture 0 0 115600 Commercial Forestry 0 0 129340 Bare Soil 0 9999405 34382 Total (R) 9999983 9999405 738874

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Total (ha) 36867 4571 3195 625 771 862 1505 48396 Total (R) 8634139 990831 739630 94936 115600 129340 10033787 20738262

First, sediment export and surface water yield were modelled for scenario T1. The results from these modelling simulations indicate that if the LULC changes associated with scenario T1 were implemented, there would be a total reduction of 15905 tons year-1 (≈ 0.15 % reduction) of sediment that would be exported to the stream across the entire catchment. 5

Whereas, there would be a total reduction of 390841 m3 year-1 (≈ 0.13 % reduction) of surface water yield generated over the entire catchment. While these results represent the relative change from the present LULC, they are only able to highlight the impacts of implementing restoration and riparian management activities and are unable to demonstrate the additional benefits of protection, subsequently underestimating the total HES benefits that

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can be achieved. In order to further highlight the additional benefits of protection, a scenario was developed (T2) to reflect the changes that may take place in the catchment in the absence of protection. In this instance, the degradation of natural vegetation coverage may result in an increase in bare soils coverage across the catchment, eliciting an increase in sediment and water yield

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delivered to the stream due to the reduction in vegetation coverage and infiltration (Hughes et al., 2018a). Subsequently, the absence of protection may diminish the overall HES returns of the investment portfolio. The total benefit of land management activities and the additional benefit of protection was then calculated as: Total Benefit =

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(T1 -Present LULC) + (T1 – T2)

(1)

The inclusion of protection in the investment portfolio further reduces the magnitude of sediment exported to the stream across the entire catchment by approximately 47.00 %. Whereas, the surface water yield generated over the entire catchment would approximately be reduced by a further 1.25 %. In this particular modelling exercise, the impact of protection/maintenance is substantially greater than the impact of restoration and riparian

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management, particularly regarding the export of sediment within the catchment. However, this is largely a consequence of the degradation of the landscape from a relatively good condition to bare soils (vegetation anchors and protects the soil, limiting the amount of erosion that may take place). It should be noted that this may not always be the case and is dependent on what the present LULC may transition to in the future.

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Figure 5

A comparison of the average annual sediment export per sub-catchment for the three SDR modelling scenarios

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Figure 6

A comparison of the average annual surface water yield per sub-catchment for the three surface water yield modelling scenarios 4. DISCUSSION

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The results of the SDR modelling simulations reflect the anticipated outcome of implementing the investment portfolio, as there potentially will be a substantial reduction in the sediment being exported to the stream across the entire catchment. It should be noted that the SDR model does not account for processes, inter alia such as; gully and rill erosion, landslides or channel scour. Subsequently, the model is only simulating a portion of the total

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load (Vogl et al., 2016b). Nevertheless, the model shows that the implementation of the investment portfolio positively impacts soil conservation within the study area.

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Despite the activities being targeted to positively impact a range of objectives, there exists a trade-off between reducing sedimentation and increasing surface water yield. The results demonstrate that there will be a marginal decrease in surface water yield across the entire catchment if the investment portfolio is implemented. This occurrence can be largely 5

attributed to the increase in natural vegetation coverage as result of the restoration activities, as well as the prevention of degradation in the natural vegetation coverage. Vogl et al. (2016b) reported similar findings, however it was noted that while there was a marginal decrease in the annual surface water yield, it is paramount to take cognisance of seasonal dynamics of water flow within the catchment. The potential improvements to the

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extent of natural vegetation coverage is expected to increase the capture and storage of water that potentially would have been lost during wet periods in regions dominated by bare soils or vegetation in poor condition (Hughes et al., 2018b). Subsequently, this may result in additional water being made available to contribute to low flow volumes during the dry season (Vogl et al., 2016b). Although the implementation of the investment portfolio may not

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directly increase surface water yields, the potential improvements in water quality (due to a reduction in sediment export), as well as baseflow will enhance existing water supplies without directly increasing streamflow. Furthermore, the potential reductions in the magnitude of sediment yield are far greater than the decrease in surface water yield and offers the added benefit of maintaining or improving

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upon the ability to supply water at a particular assurance level in the future. Focusing exclusively on the dam catchments, the reduction of sediment that is exported within the catchment (≈ 51.00 %) reduces the degree of sedimentation that would otherwise occur in the absence of land management activities. Subsequently, the lifespan of the dam will be prolonged as its capacity to store water is enhanced due to a reduction of sediment that is

25

deposited within the dam. This implies that, the dam would potentially supply more water than it would have been able to if the status quo remained. Subsequently, leading to an improvement in the security of available water supplies. It should be noted that it was not possible to verify the results of the SDR and surface water yield modelling simulations due to the lack of observed data. Therefore, the aforementioned

30

results should only be considered in terms of relative change, as uncertainties associated with model input data or estimated parameters could not be quantified. 20

In spite of, the relatively minimal data requirements of RIOS and InVEST, the lack of localized data sets were a significant limitation to the modelling efforts conducted in this study. Subsequently, the findings presented in this study provide a limited representation of the current system, as well as its potential future condition. 5

For example, we were unable to demonstrate the benefits of removing alien invasive species and revegetating these areas using indigenous vegetation, due to the lack of geospatial data regarding the location and density of alien invasive species. Such an investment in EI has been shown to increase water yields (including surface water) within catchments (Hughes et al., 2018a, 2018b). However, this alternative land management scenario could not be

10

represented within our modelling scenarios. According to Vogl et al. (2016b), the RIOS investment portfolios are strongly influenced by the input LULC data and the preferred locations of the proposed management activities. Therefore, it is imperative to acquire archetypical local data sets pertaining to LULC in order to improve the representativeness of the RIOS investment portfolios. Furthermore, there were

15

numerous instances in which coarse resolution open-source global data sets and databases were used to derive the requisite inputs required by the models. The aforementioned data sets can prove to be extremely beneficial when using the RIOS and InVEST models to guide the decision-making process in data limited circumstances. However, it is important to take cognisance that the coarse resolution of this data may not

20

adequately reflect the localized heterogeneity of the landscape or processes being modelled. Therefore, even though these models have been designed to utilize global datasets so that modelling applications can be undertaken worldwide, the use of local data sets (if available) is advocated to improve the overall quality of the model (WAVES, 2015). A further limitation to the study, was the spatio-temporal scale at which the modelling was

25

undertaken. The InVEST suite of models were developed to provide a quick overview of various HES across the landscape on an annual basis. Subsequently, the results produced are only representative of HES at a sub-catchment scale and are unable to account for seasonal variability in HES (Luke and Hack, 2018). In such circumstances the use of more spatially and temporally explicit hydrological models may be better suited to capture the seasonality of

30

hydrological processes at finer spatial resolutions (Luke and Hack, 2018). 21

Notwithstanding the abovementioned limitations, Guswa et al. (2014) states that the success of a model used to demonstrate the benefits of investing in EI should be gauged by its ability to assist the decision making process so that management decisions can be more well informed leading to improved outcomes compared to those made without the use of a model 5

(Guswa et al., 2014). Although, the scenarios that have been detailed in this study may not have achieved all of the desired objectives, the results of the simulations provide a foundation for improved decisions to be made. For example, it is now evident that additional interventions may have to be considered in order to improve water quality and increase water yields. Given the limitations imposed upon

10

the study, it was not possible to demonstrate how an iterative process can be adopted to identify the optimal interventions which will potentially lead to a decision-maker achieving their desired outcome. However, one of the major strengths of these models is that a relatively quick and simple evaluation of alternate land management scenarios can be undertaken if the data that was required to perform such an evaluation were to become

15

available. 5. CONCLUSION

The recognition of the role which EI can play in the water resources management arena is becoming increasingly prominent, particularly as environmental conservation values are being integrated into future economic development activities. 20

However, the challenge

remains to shift thinking away from solely investing in grey infrastructure to improve the security of supplying water resources and to demonstrate the benefits of investing in EI. In order to ensure that sustainable economic and land management decisions are mutually inclusive and successfully executed in the future, it is essential that there exists a robust and meticulous approach to quantify the value of investing in EI. For this purpose, we aimed to

25

highlight the potential of how targeted land management can improve the delivery of a range of HES within the uMngeni catchment, using the state-of-the-art in ES modelling. The results of the analysis demonstrated that through the coupled application of the RIOS and InVEST models, it is possible to identify priority areas for investment in land management activities that will provide the highest HES return on investment and assess how the proposed land

30

management changes will impact the delivery of HES within the catchment. It was shown 22

that investments in EI within the uMngeni catchment can lead to substantial reductions in sediment export. Although, this was accompanied by marginal decreases in the surface water yield across the catchment, the net benefit associated with reducing sediment export is expected to be far 5

greater. Considering, the lack of localized data sets and inherent model limitations inter alia, the results of the study are limited in its representation of HES, both seasonally and spatially. However, the purpose of this study was to demonstrate how specialized ES models can be used to guide and inform decision making regarding investments in EI. Given that model and data limitations can be overcome; future modelling efforts can provide an improved

10

representation of how investments in EI can enhance HES delivery in the uMngeni catchment. For this purpose, it may prove to be useful to adopt an iterative process whereby RIOS is implemented to provide an indication of where to undertake particular land management activities. Whereas, InVEST or a more spatially and temporally explicit hydrological model can be implemented to evaluate how the proposed changes in land

15

management will influence the spatio-temporal delivery of HES within the catchment. Overall, this study has demonstrated how specialized ES models can be implemented to provide a quick overview of the landscape to identify priority areas for investment and evaluate the influence of a particular management activity on HES delivery within a catchment, subsequently highlighting how these decision support tools can be used to make

20

improved land management decisions. Acknowledgements The authors would like to express their gratitude to the South African National Biodiversity Institute (SANBI) for the provision of funding required to complete this study. The authors would also like to extend their gratitude to The Natural Capital Project Team for their

25

technical assistance regarding the application of RIOS and InVEST, with special mention to Stacie Wolny, Rich Sharp and James Douglas. We would also like to extend our gratitude to Leo Quayle from the Institute of Natural Research for his assistance with deriving the location and number of beneficiaries’ data set used during the RIOS modelling. The authors wish to also acknowledge the support for the wider project from the Water Research

23

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HIGHLIGHTS •

Ecological infrastructure (EI) is central to good water resources management.

•

Investments in EI can increase hydrologic ecosystem service delivery.

•

Ecosystem service models can be used to demonstrate benefits of investing in EI.

•

Overall, ecosystem service models can be useful to guide management decisions.