Multi-agent reinforcement learning for coordinated reservoir releases under conflicting hydropower and irrigation mandates: a climate-analytics evaluation of Pareto-regret and downstream equity

Reservoir operators in transboundary basins face a structural conflict: hydropower revenue is maximised by releasing water when electricity prices peak, while irrigation mandates require deliveries timed to crop water demand. Multi-agent reinforcement learning (MARL) has been proposed as a coordination mechanism for such settings, but its evaluation has relied on aggregate reward, which conceals the distribution of regret across agents and the equity consequences for downstream users. The framework defines Pareto-regret as the vector of per-agent deviations from the Pareto frontier of feasible release schedules, and downstream equity as the dispersion of delivered irrigation volume across downstream users. A climate-analytics layer conditions the evaluation on observed hydroclimatic variability. The framework is illustrated with NASA POWER monthly reanalysis air temperature for Harare, Zimbabwe (-17.83, 31.05) over 2000-2023 (312 monthly observations), which shows a mean of 18.914 C and a warming trend of 0.2001 C per decade. The paper derives conditions under which MARL coordination reduces aggregate regret while widening downstream equity gaps, and specifies the evaluation protocol that would detect this trade-off. The contribution is a diagnostic framework, not an empirical demonstration of MARL performance.