Resilient-robust policy design for coupled human-water systems

Effective management of coupled human–water (CHW) systems under uncertainty demands policies that are both robust across a wide range of plausible futures and dynamically resilient to shocks. However, existing optimization approaches typically treat resilience statically or as a secondary constraint, overlooking systems’ adaptive recovery dynamics and stakeholder-driven local contexts. Here, we apply a two-stage Resilient–Robust (R2) approach that (i) co-develops five localized Shared Socio-economic Pathways (LSSPs) with stakeholders, (ii) optimizes five dynamic resilience metrics within per-LSSP many-objective searches (Stage A), and (iii) conducts a post-search, cross-scenario robustness evaluation to identify stable policies (Stage B). Applied to Pakistan’s Rechna Doab region, our framework reveals that under various future scenarios, groundwater level and economic water use efficiency, i.e. farm income, proved to be highly sensitive and unstable. Despite high spatial heterogeneity across the study region, the system showed a non-linear temporal decline from 2020 to 2050 according to the resilience criteria. LSSP1 shows high resilience, recovering 32% faster in farm income and maintaining 20% more stable groundwater than LSSP5, while LSSP3 lags with longer recovery times and more severe disturbances; similarly, policies optimized under LSSP1 scenarios are notably robust (79.2% mean, with 58% exceeding 80%) compared to LSSP3’s lower performance (15% below 50%). Empirically, we find that policies optimized under lower adaptation-challenge narratives (LSSP1) exhibit greater cross-scenario robustness than those from high-challenge narratives (LSSP3). By separating the per-scenario search from the post-search evaluation, our R2 approach provides a transparent, stakeholder-grounded basis for resilient water-policy interventions under uncertainty.