Integrating climate models to confront the illusion of certainty in water planning: evidence from Morocco
摘要
Stationary assumptions have shaped Morocco’s national water planning for decades, fostering a perception of climatic certainty that has repeatedly failed during recent water crises. This article examines the implications of hydroclimatic non-stationarity on water planning in a semi-arid Moroccan basin. An ensemble of CMIP6 projections under SSP4.5 and SSP8.5 scenarios was statistically downscaled to drive a calibrated hydrological model, generating future streamflow projections that were integrated into a scenario-based water system simulation framework. The analysis incorporates competing municipal and irrigation demands, existing reservoirs, and seawater desalination contributions. Results reveal a general decline in future inflows, and significant inter-model divergence. Under these conditions, stationary planning assumptions systematically overestimate water availability and conceal critical system vulnerabilities. While reservoir-based supply alone cannot reliably meet future demands, desalination improves municipal reliability, but irrigation deficits remain severe across most plausible scenarios. In practice, adaptation options are constrained by existing infrastructure and governance realities. However, by combining a non-stationary climate framework with stakeholder-defined planning scenarios, the proposed approach enabled optimization of desalination design capacity to reflect both hydroclimatic uncertainty and water managers’ priorities. Beyond physical constraints, the findings reveal the limitations of deterministic planning and a governance trap in which uncertainty is inadequately translated into action, as witnessed by repeated revisions of national water strategies, delays in commissioning of key infrastructure, and a tendency toward short-term, reactive crisis management rather than anticipated planning. The study emphasizes the urgent need for climate-informed, adaptive water planning that explicitly accounts for uncertainty and non-stationarity, particularly in water-stressed regions.