George M. Ochieng

Groundwater : characteristics, qualities, pollutions and treatments : an overview

Originally published in: International Journal of Water Resources and Environmental Engineering, 2012, 4(6): 162-170.

Data-based mechanistic modelling of stochastic rainfall-flow processes by state dependent parameter estimation

Due to the inherent nonlinearity in the process of transformation of rainfall into river flow, a simple direct input-output transfer function (TF) model may not sufficiently capture the catchments hydrological dynamics. This paper presents an application of state dependent parameter (SDP) models for nonlinear, stochastic dynamic system to identify the location and form of the nonlinearity in the rainfall-effective rainfall dynamics. The objective was to develop an effective rainfall input time series that was then used to improve the performance of an originally developed direct input-output TF model of daily rainfall-flow relationship. The CAPTAIN Toolbox in the MATLAB^(R) environment was used in the model identification in which the recursive filtering and smoothing procedures formulated within a stochastic state space setting were applied to the time series data in order to identify the location and form of nonlinearities within a generic TF model. The nonparametric estimation as well as the parametric optimisation of the resulting nonlinear models was done using the Curve Fitting Toolbox in MATLAB^(R). The results showed an improved and more parsimonious TF model. The model improved from explaining only 13% of the data to 56% presenting an improvement of 43% in the model fit. The study demonstrates that simple stochastic but robust tools can be successfully applied to develop and improve applicable hydrological models.

Use of remote sensing and geographical information system (GIS) for salinity assessment of Vaal-Harts irrigation scheme, South Africa

BackgroundSoil salinity is a critical environmental problem in many countries around the world especially the arid and semi-arid countries like South Africa. The problem has great impact on soil fertility which in turns has a great effect on soil productivity. This paper addresses the use of remote sensing and GIS in the assessment of salinity using Landsat enhanced thematic mapper plus (ETM+) data of the Vaal-Harts irrigation scheme acquired with other field data sets and a topographical map to show the spectral classes and salt-affected areas for the years under assessment (1991 to 2005).ResultsThe results of the study indicated that salinity problem exists and may get worse. The supervised classification maps show that most of the salinity problems are located along the entire scheme. The Normalized Difference Vegetation Index (NDVI) tends to be higher along the irrigation canals. A plot of NDVI values and temperature trend give a correlation of 67% this is an indication that temperature is a major factor in the build up of salinity in the study area. The low salinity class increased by 4, 8618 km2, while medium and high salinity classes decreased by 4,296.4 km2 and 485.4 km2, showing an increase in the salinity trend over the years.ConclusionsConsidering the trend of salinity development in VHS, there is an urgent need for management program to be established in order to control the spread of the menace and therefore reclaim the damaged land in order to make the scheme more viable.

Raw water quality weight factors: Vaal basin, South Africa

Weight factors (WFs) were developed for surface raw water pollution indicator variables in Vaal Rivers Upper and Middle Vaal sub-basins, in South Africa. The overall objective was to formulate a quantifiable ranking system to indicate importance of pollutant variables given their established effects on human and environmental health. Multi-criteria decision analysis (MCDA) was applied to qualitative data that were obtained from South Africas target water quality ranges as well as from literature which represented expert opinion. The human and environmental health effect choice sets were ranked from 1 to 5 on nine pollutant variable criteria: NH3/NH4+, Cl-, conductivity (EC), dissolved oxygen (DO), pH, F-, NO3-/NO2-, PO4(3-) and SO4(2-). The weighted-sum method (WSM) then assigned highest and lowest normalised weights (NWs) to F- and Cl-, respectively, for human health effects (ɛhh) alternative. Highest and lowest NWs were assigned to NH3/NH4+ and EC, respectively, for environmental health effects (ɛeh) alternative. After aggregating the ɛhh and ɛeh WFs, resultant values ranked the variables from highest to lowest as follows: F->NO3-/NO2->/NH3/NH4+>DO>pH>SO4(2-)>PO4(3-)>EC>Cl-. The results represented the importance of variables given their established effects on human and environmental health. It was concluded that WFs provided a quantifiable barometer which could signal harmful exposure to elucidate negative effects of using polluted surface raw water in the study area. The values could be incorporated into water quality models like water quality indices. The approach could be used to develop WFs for other sites, taking into account issues like the sites pollution variables of concern as well as using a ranking key constructed from established literature.

Data Reduction for Water Quality Modelling, Vaal Basin

Constructing models, comparing their predictions with observations, and trying to improve them, constitutes the core of the scientific approach to understanding complex systems like large river basins (Even et al., 2007). These processes require manipulation of huge historical data sets, which might be available in different formats and from various stakeholders. The challenge is then to first pre-process the data to similar lengths, with minimal loss of integrity, before manipulating it as per initial objectives. In the Upper and Middle Vaal Water Management Areas (WMAs) of the Vaal River, bounded by Vaal dam outlet and Bloemhof dam inlet, the overall objective of on-going research is to model surface raw water quality variability in order to predict cost of treatment to potable water standard. This paper reports on part of the overall research. Its objective was to show how a huge and non-consistent water quality data set could be downsized to manageable aspects with minimal loss of integrity. Within that scope, challenges were also highlighted. One of the more important forms of knowledge extraction is the identification of the more relevant inputs. When identified, they may be treated as a reduced input for further manipulation. In water quality data analysis, data collection, cleaning and pre-processing are often the most time-consuming phases. All inputs and targets have to be transferred directly from instrumentation or from other media, tagged and arranged in a matrix of vectors with the same lengths (Alfassi et al., 2005). If vectors have outliers and/or missing values these have to be identified for correction or to be discarded. More complex mathematical correlations are sometimes employed to identify redundant, co-linear inputs, or inputs with little information content (Alfassi et al., 2005). Sources and sinks of variables in hydrodynamics, also known as forcing functions, are the cause of change in water quality (Martin et al., 1998). To capture intermediate scale processes that are spotty in spatial extent, extensive sampling and averaging of the calibration data over sufficient spatial scales is done to capture that condition over time. Although many water constituents are non-conservative in nature, a few conservative ones that approach ideal behaviour under limited conditions, could be used for modelling and calibration.

Land Cover Change Assessment of Vaal Harts Irrigation Scheme using Multi-temporal Satellite Sata

Abstract Land cover change (LCC) is important to assess the land use/land cover changes with respect to the development activities like irrigation. The region selected for the study is Vaal Harts Irrigation Scheme (VHS) occupying an area of approximately 36, 325 hectares of irrigated land. The study was carried out using Land sat data of 1991, 2001, 2005 covering the area to assess the changes in land use/land cover for which supervised classification technique has been applied. The Normalized Difference Vegetation Index (NDVI) index was also done to assess vegetative change conditions during the period of investigation. By using the remote sensing images and with the support of GIS the spatial pattern of land use change of Vaal Harts Irrigation Scheme for 15 years was extracted and interpreted for the changes of scheme. Results showed that the spatial difference of land use change was obvious. The analysis reveals that 37.86% of additional land area has been brought under fallow land and thus less irrigation area (18.21%). There is an urgent need for management program to control the loss of irrigation land and therefore reclaim the damaged land in order to make the scheme more viable.

A PROCESS-BASED MODEL FOR FLUIDIZED BED IN SAND FILLED RESERVOIRS

Arid and semi-arid regions are prone to severe water inadequacies. They are characterized by little rainfall resulting in several seasonal rivers. Seasonal riverbeds provide opportunity for water to be stored in river’s sand-beds while their surfaces may appear dry. It is an important source of water in most rural areas under arid conditions. Several numerical models have been developed for solving sediment problems in alluvial rivers. However, rarely if ever were a model applied for sand filled reservoirs. This study presents a model to understand interacting factors through which physical water storage potential can be increased in sand filled reservoirs. Finite different method (FDM) has been applied to numerically solve mass balance continuity equation in sand filled reservoir. There were reasonable agreements between estimated results and experimental measurements from a laboratory setup. The study could provide economic and suitability data for increasing water supplies to a small community.