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Climate Change Vulnerability Assessment for Riparian Based Livelihoods in Semi Arid Parts of Zimbabwe (A Geotechnological Approach)
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Journal of Environmental Hazards

ISSN: 2684-4923

Open Access

Research - (2021) Volume 5, Issue 4

Climate Change Vulnerability Assessment for Riparian Based Livelihoods in Semi Arid Parts of Zimbabwe (A Geotechnological Approach)

Francisca Kunedzimwe1*, Olga Laiza Kupika and Samuel Kusangaya
*Correspondence: Francisca Kunedzimwe, Zimbabwe, Email:
Chinhoyi, Zimbabwe
Department of Geography and Environmental Science, University of Zimbabwe, Harare, Zimbabwe, Zimbabwe

Introduction

Global warming is leading to climate change, and is now scientifically widely accepted as a key global challenge [1]. Narratives of climate change are now central not only to the development discourse, but are also increasingly framing the understanding of other key global such as food security, deforestation, desertification, health, high energy demand and poverty, which are rooted in climate, among others challenges [2-4]. Global Climate change is an issue with potential impacts on the developing world far more severe than those predicted for the developed [5]. Climate change impact is severe in Africa, where it has brought about serious disturbances in ecosystems, reduction in water resources and decline in agricultural and food production [6]. Thus, climate change is a major threat to both human and natural ecosystems in the African region [7-9]. Climate change is already imposing additional stresses to most ecosystems which are already faced with a reduced capacity to deliver essential services to society [10,11]. The Intergovernmental Panel on Climate Change [12] technical paper on climate change and water, highlights on the vulnerability of fresh water resources to climate change impacts which are increasing in frequency and intensity and the associated consequences for human societies and ecosystems [13] noted that climate change will affect hydrologic and thermal regimes of rivers resulting in direct impact on freshwater ecosystems and human water use. In trying to address these issues, the (UNFCCC, 2011) commits nations to “develop appropriate and integrated plans for coastal zone management and water resources management for the protection and rehabilitation of areas affected by drought, desertification and floods”. Similarly, the United Nations Framework recognize that climate change is one of the greatest threats to biodiversity and Africa is considered to be one of the most vulnerable regions to climate change impacts, mainly due to its dependence on natural resources and rain-fed agriculture [14-17]. Moreover, the impacts of climate change are expected to be more severe the next century and become one of the major drivers for the loss of African biodiversity [18,19].

A riparian zone/ ecosystem is defined as the interfaces between terrestrial and aquatic ecosystems [21]. They can also be defined as those ecosystems occurring in semi-terrestrial areas adjacent to water bodies and influenced by fresh waters [20]. The riparian ecosystems extend to the continuum from headwaters to the mouths of streams and rivers, the vertical dimension that extends upward into the vegetation canopy and extends to the limits of flooding on either side of a stream [21]. Riparian ecosystems provide goods and ecosystem and human services including those ‘with use’ as well as ‘with non-use’ values [22]. Apart from this, growing population problems are the root cause of river degradation coupled with threats from impoundments, inter-basin transfers, catchment degradation, water abstraction, pollution and introduced species (ibid).

In Africa, riparian ecosystems are of crucial importance since they contribute to biodiversity and human wellbeing [23]. Thus, riparian based ecosystems are a major biodiversity hub contributing to human livelihoods. However, they are not spared from climate change related impacts. Southern Africa is prone to multi-year droughts spanning over decadal time scales (Strauch, 2009). The impacts of climate change on riparian ecosystems depend on several factors including the rate and magnitude of change relative to historical climate [24,25] noted that riparian ecosystems in the 21st century are likely to play a critical role in determining the vulnerability of natural and human systems to climate change, and influencing the capacity of these systems to adapt. Rivers provide a special suite of fresh water goods and services depending on changes on the environmental flow regime [20,26,27]. However, rivers face multiple stressors ranging from anthropogenic activities such as infrastructure development, dams, or extractive uses and natural disasters [28-30]. The aims of this climate change and vulnerability assessment for riparian based livelihoods is therefore: to a) Determine the level of vulnerability for riparian based livelihoods b) Asses the adaptive capacity for riparian based livelihoods to climate change, c) Model climate change condition for adaptive capacityin order to prioritise needs for action and justify certain actions. Climate change interacts with these anthropogenic stressors resulting in the magnification of risks that are already present through changes in rainfall, temperature, runoff patterns, and disruption of biological communities and severing of ecological linkages [23,31].

Methods

Study area

The arid and semi-arid areas of Zimbabwe include those found in natural regions 4 and 5 (Mbire, Chiredzi and Mwenezi) of the Zimbabwe natural regions classification. These are the areas most vulnerable to climate change related extreme events as they receive less rainfall and also experience higher temperatures. Any slight change in these climate elements increases the areas’ vulnerability significantly.

Mwenezi district lies in Zimbabwe’s agro-ecological regions four (7%) and five (93%), whilst Chiredzi is wholly in region 5. On the other hand, Mbire district lies in regions 3 (5%) and region 4 (95%). Thus, the majority of the area of the three districts lies in agro-ecological regions four and five. Region four receives around 450-650 mm of rainfall per annum. However, the rainfall subject to frequent seasonal droughts and severe dry spells during the rain season (Moyo, 2000; Vincent and Thomas, 1961). In Mwenezi, region four is confined to wards 1, 2, 5 and parts of wards 13. In these wards, small holder farmers grow drought-tolerant varieties of maize, sorghum, pearl millet (mhunga) and finger millet (rapoko). All the other wards are in Region 5, which receives less than 450 mm of rain per annum. The same applies to all the wards of Chiredzi district which lie in region five and thus depend on very erratic rainfall, hence most people indicated that they depend on borehole water. Generally, agro-ecological region five is suitable for extensive cattle production and game-ranching. However, small holder farmers in region five also grow drought-tolerant varieties of maize, sorghum, pearl millet (mhunga) and finger millet (rapoko).

This assessment employed a mixed methods approach to allow for the gathering of multiple data sets, to collect primary and secondary data at district and ward levels, thus a combination of qualitative and quantitative data collection methods for the collection of biophysical, agro-economic and socioeconomic data. These included desk study of relevant documents, archives and secondary material, questionnaires, digital mapping and modelling techniques. Gis and Satellite imagery were used to place the project beneficiary communities in a landscape context. The baseline data collected were then used to benchmark the current status in terms of demographic information and quantifiable indicators on respective communities, both direct and indirect segregated on gender, current land use practices, understanding of climate change, adaptation needs, rangeland condition, among others.

Demographic profile of study districts

For the demographic profile of the people in the study areas a total of 608 questionnaires were administered in Mwenezi, Mbire and Chiredzi districts (Figure 1). Questionnaires were administered in wards 2, 3, 6, 7, 9 and 11 (Mbire district); wards 2, 3, 7, 8 in Mwenezi District; and wards 6, 7, 8, 11, 13 and 15 for Chiredzi district.

Environmental-Hazards-semi-arid

Figure 1. Map showing the arid and semi-arid parts of Zimbabwe Chiredzi, Mbire and Mwenezi.

Bioclimatic modelling

In this study, Maximum Entropy software (MaxENT version 3.4) was used to analyse current and future shifts in lands suitable for the cultivation of maize, sorghum, millet and pastures. MaxENT is chosen based on the following reported advantages: it performs well with presence only data and a small number of records and also can utilize continuous and categorical data (Elith et al. 2006). Secondly it is superlative analytical and precision in predicting distribution of different species (Garcia et al 2013) and lastly it is resistant to spatial errors (Graham et al. 2008, Phillips et al. 2006). Using MAXENT for the bioclimatic modelling of crop and pasture suitability, it was assumed that people grow crops (maize, sorghum and millet) in the same fields on a rotational basis. Based on that, 19 bioclimatic factors, slope and elevation were used to determine the current and future crop suitability. The 19 bioclimatic factors are derived from the monthly temperature and rainfall values in order to generate more biologically meaningful variables. The bioclimatic variables represent annual trends (e.g., mean annual temperature, annual precipitation) seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors (e.g., temperature of the coldest and warmest month, and precipitation of the wet and dry quarters). A quarter is a period of three months (1/4 of the year) (Table 1).

BIO1 = Annual Mean Temperature
BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
BIO3 = Isothermality (BIO2/BIO7) (×100)
BIO4 = Temperature Seasonality (standard deviation ×100)
BIO5 = Max Temperature of Warmest Month
BIO6 = Min Temperature of Coldest Month
BIO7 = Temperature Annual Range (BIO5-BIO6)
BIO8 = Mean Temperature of Wettest Quarter
BIO9 = Mean Temperature of Driest Quarter
BIO10 = Mean Temperature of Warmest Quarter
BIO11 = Mean Temperature of Coldest Quarter
BIO12 = Annual Precipitation
BIO13 = Precipitation of Wettest Month
BIO14 = Precipitation of Driest Month
BIO15 = Precipitation Seasonality (Coefficient of Variation)
BIO16 = Precipitation of Wettest Quarter

Table 1. Bioclimatic Variables used for suitability modelling.

In addition to the above 19 bioclimatic variables (derived from rainfall and temperature), additional variables (Table 2) were also used to determine suitability for crops and pasture (grasslands).

DEM = Digital Elevation Model
Slope = Slope
K = Potassium
P = Phosphorus
N = Nitrogen
Cattle = Cattle Density
LC = Landcover
Soils = Soils

Table 2. Bioclimatic Variables used for suitability modelling.

Slope was derived from the STRM 30m digital elevation model downloaded from the bio climatic website (https://worldclim.org/data/worldclim21.html). The potassium, phosphorus and nitrogen were downloaded from the International Soil Reference and Information Centre (ISRIC) website https://files.isric.org/soilgrids/latest/. Cattle density data were downloaded from Gridded Livestock of the World (GLW3) is a spatial dataset website (https://www.livestockdata. org/contributor/gridded-livestock-world-glw3), and land cover data layer was obtained from Zimbabwe forestry commission. Soils data layer was obtained from the European Soil Data Centre (ESDAC) website. (https://esdac.jrc. ec.europa.eu/content/soil-map-soil-atlas-africa). Current status in terms of demographic information and quantifiable indicators on respective communities, both direct and indirect segregated on gender, current land use practices, level of degradation, understanding of climate change, adaptation needs, rangeland condition, biodiversity status (flora, fauna and aquatic).

Climate data analysis

A total of six downscaled Global Climate Models (GCMs) from the Coupled Model Inter comparison Project Phase 6 (CMIP6) data sets were used in assessing the likely impacts of climate change in Mwenezi District. CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). The downscaled GCM data were downloaded from the following website: https://www.worldclim. org/data/cmip6/cmip6_clim10m.html.

Temperature and precipitation data for the immediate future (2021-2040) were processed for six downscaled global climate models: BCC-CSM2- MR, CNRM-CM6-1, GFDL-ESM4, IPSL-CM6A-LR, MIROC-ES2L, MRIESM2- 0, and for two Shared Socio-economic Pathways (SSPs): ssp126 and ssp585 corresponding to two Representative Concentration Pathways (RCPs) RCP2.6 and RCP8.5 as in the Firth Assessment Report (AR5). (These updated scenarios are called SSP1-2.6 and SSP5-8.5, each of which result in similar 2100 radiative forcing levels as their predecessor in AR5) (Table 3).

Model Name Institution
BCC-CSM2-MR Beijing Climate Center, Beijing 100081, China
CNRM-CM6-1 Centre National de Recherches Meteorologiques, Toulouse 31057, France
GFDL-ESM4 Geophysical Fluid Dynamics Laboratory. Princeton University, USA
IPSL-CM6A-LR Institut Pierre Simon Laplace, Paris 75252, France
MIROC-ES2L Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan
MRI-ESM2-0 Meteorological Research Institute Tsukuba Ibaraki 305-0052 Japan

Table 3. Bioclimatic models and Institutions.

The RCP2.6 emission and concentration pathway is representative of the literature on mitigation scenarios aiming to limit the increase of global mean temperature to 2°C. This scenario forms the low end of the scenario literature in terms of emissions and radiative forcing. On the other hand, the RCP8.5 emission and concentration pathway combine assumptions about high population and relatively slow income growth with modest rates of technological change and energy intensity improvements, leading in the long term to high energy demand and GHG emissions in absence of climate change policies. Compared to the total set of Representative Concentration Pathways (RCPs), RCP8.5 thus corresponds to the pathway with the highest greenhouse gas emissions. Results for the vulnerability analysis are mostly based on the RCP8.5 representing the possible worstcase scenario.

Conclusion

Findings from the research indicate that climate change related extreme events such as drought, cyclones and floods and its associated impacts have influenced on riparian based ecosystems and livelihoods in the three study areas. A variety of ecosystems services have been affected, both positively and negatively, including provisioning services of food production and water supply, regulating services supporting flood prevention and health; supporting services related to primary productivity and cultural services relating to ecotourism. Ultimately, a range of approaches is needed to address climate change impacts to ensure that resilience building efforts and sustainable development can continue.

References

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