The various physical mechanisms governing the dynamics of streamflow processes act on a seemingly wide range of temporal and spatial scales; almost all the mechanisms involved present some degree of nonlinearity. Against the backdrop of these issues, in this paper, attempt was made to critically look at the subject of Autoregressive Conditional Heteroscedasticity (ARCH) or volatility of streamflow processes, a form of nonlinear phenomena. Towards this end, streamflow data (both daily and monthly) of the River Benue, Nigeria were used for the study. Results obtained from the analyses indicate that the existence of conditional heteroscedasticity in streamflow processes is no paradox. Too, ARCH effect is caused by seasonal variation in the variance for monthly flows and could partly explain same in the daily streamflow. It was also evident that the traditional seasonal Autoregressive Moving Average (ARMA) models are inadequate in describing ARCH effect in daily streamflow process though, robust for monthly streamflow; and can be removed if proper deseasonalisation pre-processing was done. Considering the findings, the potential for a hybrid Autoregressive Moving Average (ARMA) and Generalised Autoregressive Conditional Heteroscedasticity (GARCH)type models should be further explored and probably embraced for modelling daily streamflow regime in view of the relevance of statistical modelling in hydrology

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In a world where excessive use and degradation of water resources are threatening the sustainability of livelihoods dependent on water and agriculture, increased food production will have to be done in the face of a changing climate and climate variability. There is a need to make optimal use of the available water resource to maximize productivity. Climate-smart irrigation is aimed at increasing per unit production and income from irrigated cropping systems without having negative impacts on the environment or other water users and uses. This paper developed a water allocation model using Genetic Algorithm to equitably allocation available water to the various sectors in Kano River Irrigation Scheme yielding an optimal as well as equitable water release with a 96.44% demand met. An average relative supply of 0.94 was obtained indicating the there was even supply of water to all the sectors. The model is robust and relatively easy to apply and can be employed by farm managers to achieve equity and optimal use of the available water resource.

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The choice of a particular Artificial Neural Network (ANN) structure is a seemingly difficult task; worthy of relevance is that there is no systematic way for establishing a suitable architecture. In view of this, the study looked at the effects of ANN structural complexity and data pre-processing regime on its forecast performance. To address this aim, two ANN structural configurations: 1) Single-hidden layer, and 2) Double-hidden layer feed-forward back propagation network were employed. Results obtained revealed generally that: a) ANN comprised of double hidden layers tends to be less robust and converges with less accuracy than its single-hidden layer counterpart under identical situations; b) for a univariate time series, phase-space reconstruction using embedding dimension which is based on dynamical systems theory is an effective way for determining the appropriate number of ANN input neurons, and c) data pre-processing via the scaling approach excessively limits the output range of the transfer function. In specific terms considering extreme flow prediction capability on the basis of effective correlation: Percent maximum and minimum correlation coefficient (R max % and R min %), on the average for one-day ahead forecast during the training and validation phases respectively for the adopted network structures: 8 7 5 (i.e., 8 input nodes, 7 nodes in the hidden layer, and 5 output nodes in the output layer), 8 5 2 5 (8 nodes in the input layer, 5 nodes in the first hidden layer, 2 nodes in the second hidden layer, and 5 nodes in the output layer), and 8 4 3 5 (8 nodes in the input layer, 4 nodes in the first hidden layer, 3 nodes in the second hidden layer, and 5 nodes in the output layer) gave: 101.

This work studied the effect of fish pond wastewater, proximate and mineral analyses of the fish feeds from commercial fish ponds on receiving soils in the southern Guinea Savanna ecological zone of Minna, Nigeria. The study conducted during the dry seasons of the years 2015 and 2016 between January and May. Four sets of soil samples were collected at varying distances from the various fish ponds and analysed for physicochemical properties, Particle size analysis and Exchangeable Acidity. Similarly, two different sets of water samples were collected each from various farms studied and analysed for physicochemical properties such as pH, E c (Electrical conductivity), TDS (Total dissolved solids), Alkalinity, Total Hardness, Turbidity, Temperature, DO (Dissolved Oxygen), BOD (Biological oxygen demand), Calcium, Magnesium, Sodium and Potassium. Average results obtained for the two dry seasons for the soil samples around the fish farms showed that: pH 6.33 -7.24, Organic Carbon 2.03 -6.00 g/kg, Available Phosphorus 7 -10 mg/kg, Total Nitrogen 0.95 -2.10 g/kg, Exchangeable Bases and Exchangeable Acidity 0.02 -0.06 Cmol/kg were within the permissible limits of FAO and WHO. The various parameters were within the permissible limits for fish production. The fish wastewater had Electrical Conductivity 118 -230 µS/cm, pH 6.79 -7.10, and Biological Oxygen Demand 3.0 -6.0 mg/L. In conclusion, the wastewater from fish farms is good sources of fertiliser to the surrounding farmlands.

The growing dominance of urban environment with heavy metals through natural and anthropogenic depositions and the potentially adverse health implications following environmental contaminations have focused attention on the disposal of municipal and industrial wastes. This study employed analytical procedures to investigate the concentrations of chromium, iron, copper, zinc, manganese and aluminium at the municipal waste dump site of the study areas. Soil samples were randomly collected from different waste dumpsite across Nasarawa, Kogi, and Niger states and an undisturbed soil 100m away from the dumpsites was chosen as the control. From the result obtained, it was observed that the metals in all dumpsite types followed the order: Mn>Fe>Zn>Cu>Cr>Al. Mn had the highest mean concentration which was recorded in Bida, Borgu and Minna in Niger State while Al was the least detected in all the dumpsites with the lowest mean concentrations (5.7±0.96) recorded in Borgu. Mean concentration of all the metals at the dumpsites was higher than at the control which means there is an anthropogenic contribution from the environment. The concentrations of the heavy metals were generally lower than their respective guideline values for the protection of human and environmental health. However, with prolong practice of dumping refuse at these sites; concentrations of the heavy metals may increase above the recommended limits. Therefore, we recommend that further research be carried out on the heavy metals concentration of the waste materials to identify those that are potential sources of soil contamination to suggest appropriate treatment and disposal methods.