Predicting Wetland Occurrence in the Arid to Semi— Arid 1 Interior of the Western Cape, South Africa, for Improved 2 Mapping and Management

Predicting Wetland Occurrence in the Arid to Semi— Arid 1 Interior of the Western Cape, South Africa, for Improved 2 Mapping and Management Donovan Charles Kotze ( [email protected] ) University of KwaZulu-Natal https://orcid.org/0000-0001-9048-1773 Nick Rivers-Moore University of KwaZulu-Natal Michael Grenfell University of the Western Cape Nancy Job South African National Biodiversity Institute Research Article Keywords: drylands, hydrogeomorphic type, logistic regression, probability, vulnerability Posted Date: August 17th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-716396/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Predicting wetland occurrence in the arid to semi—arid 2 interior of the Western Cape, South Africa, for improved 3 mapping and management 4 5 D. C. Kotze1*, N.A. Rivers-Moore1,2, N. Job3 and M. Grenfell4 6 1Centre for Water Resources Research, University of KwaZulu-Natal, Private Bag X01, 7 Scottsville, 3209, South Africa 8 2Freshwater Research Centre, Cape Town, South Africa 9 3Kirstenbosch Research Centre, South African National Biodiversity Institute, Private Bag X7, 10 Newlands, Cape Town, 7945, South Africa 11 4Institute for Water Studies, Department of Earth Science, University of the Western Cape, 12 Private Bag X17, Bellville, 7535, South Africa 13 *Corresponding author: [email protected] 14 15 Abstract 16 As for drylands globally, there has been limited effort to map and characterize such wetlands in 17 the Western Cape interior of South Africa. Thus, the study assessed how wetland occurrence and 18 type in the arid to semi-arid interior of the Western Cape relate to key biophysical drivers, and, 19 through predictive modelling, to contribute towards improved accuracy of the wetland map layer. 20 Field-verified test areas were selected to represent the aridity gradient, rainfall seasonality, 21 hydrogeomorphic (HGM) types and physiographic zones encompassed in the study area. The arid 22 areas of the Karoo physiographic zones had: (1) a low (<1%) proportional area of wetland; (2) an 23 almost complete absence of seepage slope wetlands; (3) ephemeral depressions, all non-vegetated; 24 and (4) much of the wetland associated with valley bottoms confined within a channel. The less 25 arid mountain zones had: (1) a much higher (>3%) proportional area of wetland; and (2) wetlands 26 being predominantly hillslope seepages, but also including valley bottom wetlands. 27 28 A spatial probability surface of wetland occurrence was generated based on the statistical 29 relationship of verified wetland presence and absence data points with a range of catchment-scale 30 predictor variables, including topographic metrics and hydrological/climatic variables. This layer 31 was combined with raster images of most likely HGM type within the landscape to provide a final 1 32 product of wetland occurrence, attributed by HGM type. Vulnerabilities of the wetlands were 33 identified based on key attributes of the different wetland types, and recommendations were 34 provided for refining the wetland map for the Western Cape. 35 36 Keywords: drylands; hydrogeomorphic type; logistic regression; probability; vulnerability 37 38 Declarations 39 Funding: the research was supported by the South African National Biodiversity Institute 40 Conflicts of interest/Competing interests: none are known 41 Availability of data and material: available from authors on request 42 Code availability: NA 43 44 Acknowledgements 45 The South African National Biodiversity Institute (SANBI) is thanked for supporting and funding 46 this research and Nacelle Collins is thanked for his assistance with refining the wetland mapping 47 recommendations arising from this study. 48 49 50 51 Introduction 52 53 Globally, much of the effort to map and characterize wetlands at a landscape scale has been in 54 humid temperate areas, where wetlands tend to be more extensive than in drylands (Hu et al. 2017). 55 In sub-Sharan Africa, the wetlands in drylands literature has largely focused on either the large 56 fluvial systems (Tooth and McCarthy 2007; Ellery et al. 2009) or endorheic depressions (pans) 57 (Goudie and Thomas 1985; Allan et al. 1995). Landscape-level examinations of the occurrence of 58 the full range of different wetland types represented in drylands appear to have been limited to a 59 few studies, notably that of Melly et al. (2016; 2017). This also holds true in the Western Cape, 60 South Africa, where much of the mapping effort and field verification has focused on the relatively 61 high precipitation areas near the coast, with the result that the extensive arid to semi-arid interior 62 of the province, especially to the east, has the lowest level of confidence in wetland spatial extent 2 63 and hydrogeomorphic (HGM) type (Van Deventer et al. 2020). However, wetlands in drylands 64 tend to be hydro-geomorphologically distinct from wetlands in humid areas (Tooth and McCarthy 65 2007) and have an especially important role to play within the landscape in terms of the supply of 66 provisioning services for cultivators, pastoralists and other natural resource users (Scoones 1991). 67 This, coupled with the relatively poor level of mapping in these areas, identifies a clear research 68 gap. Although the occurrence of wetlands is expected to be generally lower in the interior of the 69 Western Cape compared with the coastal areas, there are areas of the interior where climate and 70 other environmental drivers are locally conducive to a higher wetland occurrence. Describing this 71 heterogeneity and the underpinning landscape-level drivers is valuable for baseline mapping and 72 characterization of wetlands and to assist in predicting how land-use change, climate change and 73 other stressors might affect wetlands. 74 75 The practical outcomes of the low accuracy of mapping include either delay in projects because of 76 false positives of wetlands, or wetlands being lost to other land developments because they have 77 not been mapped. Furthermore, it results in the true value of natural capital not being accurately 78 audited. There is thus a pressing need for improving the accuracy of South Africa’s national 79 wetland map, particularly for the extensive areas of the country which have lacked field 80 verification, including those in the present study. 81 82 Wetland HGM types show variable levels of vulnerability to different forces of degradation 83 (Rivers-Moore and Cowden 2012). Since the supply of ecosystem services can typically be 84 inferred based on HGM types, it is important for a wetland occurrence map to be correctly 85 attributed. Mapping wetland presence on its own typically produces a polygon layer that is 86 unattributed. Such maps only become truly useful as the product of integrated ancillary data layers 87 which provide probability values for occurrence, type, and ecological condition, from which 88 wetland practitioners will be able to make statements such as “at this location, there is an 85% 89 probability of a wetland occurring, which is five times more likely to be a seep than a floodplain” 90 (Rivers-Moore et al. 2020). 91 92 This study sought to better understand how the occurrence of wetlands generally (and specific 93 wetland HGM types) in the Western Cape interior relate to key biophysical drivers, and, through 3 94 predictive modelling to contribute towards cost-effectively improving the accuracy of the wetland 95 layer for the province. The study further demonstrates how the enhanced understanding of 96 biophysical drivers affecting wetlands generally (and specifically of different wetland types) can 97 be used to improve future mapping effort for the province and for dryland environments across the 98 globe. 99 100 Methods 101 Study area 102 The study area was defined as the central and western interior of the entire Western Cape Province. 103 This coincides with a mean aridity index of 0.133±0.04, calculated from the ratio of mean annual 104 precipitation (MAP) to mean annual evapotranspiration, where values < 0.2 are classified as arid 105 conditions (Trabucco and Zomer 2019). The boundary was defined according to hydrologically 106 correct sub-catchments rather than strictly along the administrative boundary (Figure 1), provided 107 by the South African National Biodiversity Institute (SANBI) and based on the sub-quaternary 108 catchments derived from the National Freshwater Ecosystem Priority Areas (NFEPA) (Nel et al. 109 2011) dataset. This translated into 547 catchment polygons with a median area of ~70km2. Field 110 test sites were selected to represent the aridity gradient, main rainfall seasons (i.e. winter rainfall 111 vs. summer rainfall), main hydrogeomorphic (HGM) types and physiographic zones represented 112 in the study area. 113 The study area comprised five primary physiographic zones, which encompass diverse geology 114 and climate (Table 1). The zones represent three mountain ranges, each with relatively high mean 115 annual precipitation (MAP; > 400mm), and two areas of flatter terrain in between, the first being 116 part of the Great Karoo and the second the Little Karoo, both with relatively low MAP (< 200mm; 117 Table 1). 118 119 Data collection 120 Foundational to the approach of the study was the identification of field-verified test areas within 121 the overall study area. In this study, a test area refers to a geographically-delimited area (preferably 122 >20 km2) within which wetlands have been classified according to hydrogeomorphic (HGM) type 123 and mapped with at least an intermediate level of confidence. This means that at least 20% of the 124 mapped wetlands have been observed in the field close enough to identify whether the dominant 4 125 plant species are hydrophytes. In addition, most of the wetlands which have not been observed in 126 the field appear to have the same HGM type and a similar landscape setting and spectral signature 127 to a comparable wetland which has been observed in the field (and from which inferences can be 128 drawn).

Load more