Department of Earth Sciences
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Browsing Department of Earth Sciences by Author "Chikoore, H."
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Item Open Access Drought in Luvuvhu River Catchment - South Africa: Assessment, Characterisation and Prediction(2020-09) Mathivha, Fhumulani Innocentia; Chikoore, H.; Sigauke, C.; Odiyo, J. O.Demand for water resources has been on the increase and is compounded by population growth and related development demands. Thus, numerous sectors are affected by water scarcity and therefore effective management of drought-induced water deficit is of importance. Luvuvhu River Catchment (LRC), a tributary of the Limpopo River Basin in South Africa has witnessed an increasing frequency of drought events over the recent decades. Drought impacts negatively on communities’ livelihoods, development, economy, water resources, and agricultural yields. Drought assessment in terms of frequency and severity using Drought Indices (DI) in different parts of the world has been reported. However, the forecasting and prediction component which is significant in drought preparedness and setting up early warning systems is still inadequate in several regions of the world. This study aimed at characterising, assessing, and predicting drought conditions using DI as a drought quantifying parameter in the LRC. This was achieved through the application of hybrid statistical and machine learning models including predictions via a combination of hybrid models. Rainfall and temperature data were obtained from South African Weather Service, evapotranspiration, streamflow, and reservoir storage data were obtained from the Department of Water and Sanitation while root zone soil moisture data was derived from the NASA earth data Giovanni repository. The Standardised Precipitation Index (SPI), Standardised Precipitation Evapotranspiration Index (SPEI), Standardised Streamflow Index (SSI), and Nonlinear Aggregated Drought Index (NADI) were selected to assess and characterise drought conditions in the LRC. SPI is precipitation based, SPEI is precipitation and evapotranspiration based, SSI is based on streamflow while NADI is a multivariate index based on rainfall, potential evapotranspiration, streamflow, and storage reservoir volume. All indices detected major historical drought events that have occurred and reported over the study area, although the precipitation based indices were the only indices that categorised the 1991/1992 drought as extreme at 6- and 12- month timescales while the streamflow index and multivariate NADI underestimated the event. The most recent 2014/16 drought was also categorised to be extreme by the standardised indices. The study found that the multivariate index underestimates most historical drought events in the catchment. The indices further showed that the most prevalent drought events in the LRC were mild drought. Extreme drought events were the least found at 6.42%, 1.08%, 1.56%, and 4.4% for SPI, SPEI, SSI, and NADI, respectively. Standardised indices and NADI showed negative trends and positive upward trends, respectively. The positive trend showed by NADI depicts a decreased drought severity over the study period. Drought events were characterised based on duration, intensity, severity, and frequency of drought events for each decade of the 30 years considered in this study i.e. between 1986 – 1996, 1996 – 2006, 2006 – 2016. This was done to get finer details of how drought characteristics behaved at a 10-year interval over the study period. An increased drought duration was observed between 1986 - 1996 while the shortest duration was observed between 1996 - 2006 followed by 2006 - 2016. NADI showed an overall lowest catchment duration at 1- month timescale compared to the standardised indices. The relationship between drought severity and duration revealed a strong linear relationship across all indices at all timescales (i.e. an R2 of between 0.6353 and 0.9714, 0.6353 and 0.973, 0.2725 and 0.976 at 1-, 6- and 12- month timescales, respectively). In assessing the overall utilisation of an index, the five decision criteria (robustness, tractability, transparency, sophistication, and extendibility) were assigned a raw score of between one and five. The sum of the weighted scores (i.e. raw scores multiplied by the relative importance factor) was the total for each index. SPEI ranked the highest with a total weight score of 129 followed by the SSI with a score of 125 and then the SPI with a score of 106 while NADI scored the lowest with a weight of 84. Since SPEI ranked the highest of all the four indices evaluated, it is regarded as an index that best describes drought conditions in the LRC and was therefore used in drought prediction. Statistical (GAM-Generalised Additive Models) and machine learning (LSTM-Long Short Term Memory) based techniques were used for drought prediction. The dependent variables were decomposed using Ensemble Empirical Mode Decomposition (EEMD). Model inputs were determined using the gradient boosting, and all variables showing some relative off importance were considered to influence the target values. Rain, temperature, non-linear trend, SPEI at lag1, and 2 were found to be important in predicting SPEI and the IMFs (Intrinsic Mode Functions) at 1, 6- and 12- month timescales. Seven models were applied based on the different learning techniques using the SPEI time series at all timescales. Prediction combinations of GAM performed better at 1- and 6- month timescales while at 12- month, an undecomposed GAM outperformed the decomposition and the combination of predictions with a correlation coefficient of 0.9591. The study also found that the correlation between target values, LSTM, and LSTM-fQRA was the same at 0.9997 at 1- month and 0.9996 at 6- and 12- month timescales. Further statistical evaluations showed that LSTM-fQRA was the better model compared to an undecomposed LSTM (i.e. RMSE of 0.0199 for LSTM-fQRA over 0.0241 for LSTM). The best performing GAM and LSTM based models were used to conduct uncertainty analysis, which was based on the prediction interval. The PICP and PINAW results indicated that LSTM-fQRA was the best model to predict SPEI timeseries at all timescales. The conclusions drawn from drought predictions conducted in this study are that machine learning neural networks are better suited to predict drought conditions in the LRC, while for improved model accuracy, time series decomposition and prediction combinations are also implementable. The applied hybrid machine learning models can be used for operational drought forecasting and further be incorporated into existing early warning systems for drought risk assessment and management in the LRC for better water resources management. Keywords: Decomposition, drought, drought indices, early warning system, frequency, machine learning, prediction intervals, severity, water resources.Item Open Access Dynamical processes in the evolution of Cut Off Lows over South Africa(2023-10-05) Khangale, Khuthadzo; Mathivha, F. I.; Ndarama, T.; Chikoore, H.This study presented deviations in the evolution of Cut-off lows (COLs) through four COLs events over the South African domain from a case study point of view. Furthermore, this study demonstrated COLs as cold-cored low-pressure systems estranged from westerly wind belts above . COLs are hazardous as they are mostly associated with unbearable precipitations and winds. The main aim of this study is to develop an understanding of dynamical processes during COLs. ERA5 data sets were used throughout the study to achieve objectives. Mean seal level pressure surfaces along with the 250 hPa geopotential heights were used to link the existence of COLs with ridging highs, the results shows that COLs that develop north of the subdivided study region (C and D) are mostly associated with Type-N ridging highs, whereas those that develop south (A and B) are associated with Type-S ridging highs. COLs were identified using 500 hPa geopotential height and the 330K potential vorticity. The results showed that high negative pool of potential vorticity is one of the principal factors during COL genesis. The small-scale jet associated with COLs tends to disappear as the COL dissipates. Some COLs extend to 850 hPa and further towards the surface and while others may not. COLs that extend towards the surface are associated with deeper PV intrusions. Cold cores of COLs are usually found at 500 hPa and uplifting of air during COLs occur further east from its centre which is filled with cold air mass. COLs tend to propagate east as soon as they are formed, but the speed (distance/time) is different from one COL to the next. The mechanism involved in COL development was investigated in detail using the eddy kinetic energy (EKE) budget applied to data from ERA5 reanalysis. This approach considers the most important processes involved such as the Ageostrophic flux convergence (AFC), Baroclinic convergence (BRC) and the Barotropic convergence (BRT). The results showed that the AFC together with the BRC are important for the EKE growth. Furthermore, AFC is responsible for the COL genesis, BRC is necessary for system maintenance and the BRT affects COLs negatively. The operational forecasting of these weather systems across the nation can be enhanced by the application of the knowledge about the evolution in the characteristics of COL events from this study.Item Open Access Management of water resources and impacts of climate change in the Upper Pungwe River Basin(2019-10) Gumbo, Anesu Dion; Chikoore, H.; Mathivha, F. I.; Kapangaziwiri, E.Developing countries are largely characterized by rural-based communities often settled in headwater catchments whose livelihoods are dependent on natural resources available. As the climate changes, hydrological regimes are also altered affecting these communities. Assessing available water resources and their management becomes crucial to inform sustainable resources management, planning and development. This study quantified water resources in ten selected headwater sub-catchments of the Pungwe River Basin using the Pitman model in (SPatial And Time Series Information Model) SPATSIM_V3 and ten statistically downscaled climate datasets from the Climate Systems Analysis Group forced with RCP 4.5 and RCP 8.5 emission scenarios. Rainfall and potential evaporation data were used to setup the model while streamflow data was used for model calibration. The calibrated model parameters were used to project future water resources using stochastic rainfall ensembles derived from the delta change method in SPATSIM. Interviews were also carried out with natural resources managers to understand how headwater sub-catchments were being managed using a case of Pungwe Sub-Catchment. The interviews revealed that headwater catchment management is not yet incorporated in the management procedures of water resources in the sub-catchment, but the principles of integrated water resources management are being fully implemented. The Pitman- SPATSIM showed that water resources in the headwater sub-catchments to be adequate to meet ecological and human needs. Near-future (2020-2060) and far-future (2061-2099) projections using RCP 4.5 varied from the current period (1960-2010) with a percentage difference in mean monthly flow within the range of -9% to 7% for all sub-catchments. Under RCP 8.5, the near and far-future had similar projections, with both periods showing a minor reduction in water availability with a few subcatchments showing a reduction as high as 71% (sub-catchment E72) which could possibly be attributed to streamflow datasets used for the calibration process. It was concluded that future water resources availability in the study area will be stable, with the key assumption that climate change is the sole variable driving water availability. To fully understand the water resources availability in the future, other factors such as land use changes need to be incorporated in the simulation of future water resources.