Spectral methods are advanced numerical techniques used to solve partial differential equations (PDEs), which are fundamental in modelling water-resource allocation processes such as river flow and groundwater distribution in Ghana. Spectral methods utilise orthogonal polynomial bases to approximate solutions to PDEs. Condition number analysis evaluates the sensitivity of the solution to perturbations in input data, ensuring reliable predictions in complex environmental systems. A key finding is that spectral methods can significantly reduce computational time compared to traditional finite difference or finite element methods for solving water-resource allocation models. Spectral methods offer a robust framework for accurately modelling and predicting water resources under varying conditions, with condition number analysis providing critical insights into the reliability of these models. Further research should explore integrating spectral methods with machine learning algorithms to enhance predictive accuracy in dynamic water resource systems. Partial Differential Equations, Spectral Methods, Condition Number Analysis, Water Resource Allocation, Ghana Under standard regularity and boundary assumptions, the forecast state is modelled by $\partial_t u(t,x)=\kappa\,\partial_{xx}u(t,x)+f(t,x)$, and stability follows from bounded perturbations.