Explainable Artificial Intelligence and Digital Twins for Sustainable and Resilient Water Management—A Systematic Review

Artificial intelligence (AI), explainable AI (XAI), digital twins, and intelligent decision-support systems are increasingly proposed for water management, yet existing reviews usually emphasize algorithms or architectures rather than the evidence chain from prediction to governed decisions and measured outcomes. This PRISMA 2020- and PRISMA-S-informed systematic review critically synthesized 48 records published from 1 January 2017 to 5 August 2026 across hydrology, water distribution, water quality, wastewater treatment, irrigation, and basin management. The corpus comprised 23 empirical, technical, or hybrid application records and 25 secondary or conceptual records. Deployment was coded with a conservative maturity rubric (M0–M4, with M1a for offline benchmark or simulated validation and M1b for retrospective real-world validation), and sustainability evidence was coded from S0 (absent) to S4 (prospectively measured). Forty-three records remained at M0–M2, five reached M3 operational decision support, and no audited M4 closed-loop implementation was identified within the reviewed corpus. Nine records used explicit XAI, but practitioner usefulness was rarely tested. Sustainability evidence was S0 in 6 records, S1 in 18, S2 in 17, S3 in 5, and S4 in 2. The review’s principal contribution is the proposed TRACE-Water framework, which connects traceable data, robust validation, actionable explanation, controlled decision loops, and evaluated sustainability in a water-specific evidence chain. TRACE-Water complements, rather than replaces, FAIR principles, AI risk frameworks, model documentation, and digital-twin governance; it also requires future empirical validation.