Climate change impact on water availability in the olifants catchment (South Africa) with potential adaptation strategies

Olabanji, Mary Funke; Ndarana, Thando; Davis, Nerhene; Archer, Emma

doi:10.1016/j.pce.2020.102939

Cited by 19 publications

(10 citation statements)

References 27 publications

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“…A multi-stage sampling technique was employed in this study, where a combination of sampling techniques was used to select the catchment and households in the catchment for interviews. In the first stage, the Olifants catchment was purposively selected from other catchments in South Africa because it is one of the most severely affected by climate change (Olabanji et al 2020). In the second stage, five districts were purposively selected in the Olifants catchment because they are the highest crop-producing districts with homogenous climate.…”

Section: Sampling Technique and Sample Sizementioning

confidence: 99%

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Olabanji

Davis

Ndarana

et al. 2021

Journal of Water and Climate Change

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Climate change is expected to affect the livelihood of rural farmers in South Africa particularly the smallholder farmers, due to their overwhelming dependence on rain-fed agriculture. This study examines smallholder farmers' perception of climate change, the adaptation strategies adopted and factors that influences their adaptive decisions. The unit of data collection was household interview and focus group discussion. Climate data for the Olifants catchment (1986–2015) were also collected to validate farmers' perception of climate change with actual climate trend. Data collected were analysed using descriptive statistics, Mann–Kendall trend, Sen's slope estimator and multinomial logit regression model. Results revealed that smallholder farmers are aware of climate change (98%), their perception of these changes aligns with actual meteorological data, as the Mann–Kendall test confirms a decreasing inter-annual rainfall trend (−0.172) and an increasing temperature trend (0.004). These changes in temperature and precipitation have prompted the adoption of various adaptation responses, among which the use of improved seeds, application of chemical fertilizer and changing planting dates were the most commonly practised. The main barriers to the adoption of adaptation strategies were lack of access to credit facility, market, irrigation, information about climate change and lack of extension service. The implication of this study is to provide information to policy-makers on the current adaptation responses adopted by farmers and ways in which their adaptive capacity can be improved in order to ensure food security.

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Section: Sampling Technique and Sample Sizementioning

confidence: 99%

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Olabanji

Davis

Ndarana

et al. 2021

Journal of Water and Climate Change

Self Cite

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“…In general, values of R 2 if higher than 0.50 were considered as significant and acceptable [ 44 , 45 ] and values of R 2 higher than 0.70 were considered as strong [ 46 , 47 ]. In case of E , we considered only the positive estimates as negative estimates would indicate that the observed mean would be a better predictor in this context [ 48 ]. Consequently, we opted to use r as a representative for estimating the measures of association for the entire datasets.…”

Section: Resultsmentioning

confidence: 99%

“…would indicate that the observed mean would be a better predictor in this context [48]. Consequently, we opted to use r as a representative for estimating the measures of association for the entire datasets.…”

Section: Plos Onementioning

confidence: 99%

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

et al. 2022

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Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson’s coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area.

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“…The NSE is one of the most widely used statistics for validating water models, and many studies have found that NSE values of ≥0.6 are satisfactory, while values ≥0.75 are considered very good. [28][29][30][31][32][33][34][35] The NSE determines how closely the observed and the simulated data fit the 1:1 line and, similarly, the R 2 measures variance between observed and simulated which indicates the fit of the model. 28 Models such as the Hydrological Simulation Program FORTRAN (HSPF) had NSE values of between 0.6 and 0.7 for analysis of monthly water temperatures in tropical rivers of southern Malaysia.…”

Section: Discussionmentioning

confidence: 99%

Modelling water temperature in the lower Olifants River and the implications for climate change

Adlam

Chimimba

Retief³

et al. 2022

S. Afr. J. Sci.

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Freshwater systems in southern Africa are under threat of climate change, not only from altered flow regimes as rainfall patterns change, but also from biologically significant increases in water temperature. Statistical models can predict water temperatures from air temperatures, and air temperatures may rise by up to 7 °C by 2100. Statistical water temperature models require less data input than physical models, which is particularly useful in data deficient regions. We validated a statistical water temperature model in the lower Olifants River, South Africa, and verified its spatial applicability in the upper Klaserie River. Monthly and daily temporal scale calibrations and validations were conducted. The results show that simulated water temperatures in all cases closely mimicked those of the observed data for both temporal resolutions and across sites (NSE>0.75 for the Olifants River and NSE>0.8 for the Klaserie). Overall, the model performed better at a monthly than a daily scale, while generally underestimating from the observed (indicated by negative percentage bias values). The statistical models can be used to predict water temperature variance using air temperature and this use can have implications for future climate projections and the effects climate change will have on aquatic species.

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Climate change impact on water availability in the olifants catchment (South Africa) with potential adaptation strategies

Cited by 19 publications

References 27 publications

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Assessment of smallholder farmers’ perception and adaptation response to climate change in the Olifants catchment, South Africa

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

Modelling water temperature in the lower Olifants River and the implications for climate change

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