A Scoping Review of Methodological Frameworks for Time-Series Forecasting in Municipal Water Risk Reduction: Diagnostics for South African Systems (2021–2026)

{ "background": "Municipal water systems are critical for agricultural resilience, yet face escalating pressures from climate variability and demand growth. Effective risk reduction hinges on robust forecasting methodologies, but a synthesis of applicable frameworks for local contexts is lacking.", "purpose and objectives": "This scoping review aims to identify, map, and evaluate methodological frameworks for time-series forecasting applied to municipal water risk reduction, with a diagnostic focus on applications relevant to the agricultural sector.", "methodology": "The review followed a structured, five-stage scoping framework. Peer-reviewed literature and technical reports were systematically identified from major scholarly databases. Included records were charted and analysed thematically to synthesise methodological approaches, model diagnostics, and implementation contexts.", "findings": "A dominant theme was the integration of seasonal autoregressive integrated moving average (SARIMA) models with exogenous climate variables, expressed as yt = \μ + \∑i=1ᵖ\ yt-i + \∑i=1q\ \εt-i + \∑j=1ᵐ\ xt,j + \, where xt,j represents rainfall or temperature. Model performance was highly sensitive to input data quality, with prediction intervals widening substantially under non-stationary drought conditions.", "conclusion": "A suite of hybrid statistical and machine learning frameworks shows diagnostic potential for municipal water forecasting. However, their utility is constrained by systemic data gaps and a lack of contextual adaptation for integrated agricultural water management.", "recommendations": "Future research must prioritise the development of accessible, data-sparse modelling toolkits and foster interdisciplinary collaboration between hydrologists, data scientists, and agricultural planners to enhance model operationalisation.", "key words": "water security, predictive modelling, risk assessment, agricultural water, municipal services, diagnostic evaluation", "contribution statement": "This review provides a novel diagnostic synthesis of forecasting methodologies, identifying a critical gap in frameworks tailored for data