Hydrolysis reactions are ubiquitous in biological, environmental, and industrial chemistry. Density functional theory (DFT) is commonly employed to study the kinetics and reaction mechanisms of hydrolysis processes. Here, we present a new dataset, Barrier Heights for HydrOlysis - 36 (BH2O-36), to enable the design of density functional approximations (DFA) and the rational selection of DFAs for applications in aqueous chemistry. BH2O-36 consists of 36 diverse organic and inorganic forward and reverse hydrolysis reactions with reference energy barriers calculated at the CCSD(T)/CBS level. Using BH2O-36, we evaluate 63 DFAs. In terms of mean absolute error (MAE) and mean relative absolute error (MRAE), wB97M-V is the best-performing DFA tested, while MN12-L-D3(BJ) is the best-performing pure (non-hybrid) DFA. Broadly, we find that range-separated hybrid DFAs are necessary to approach chemical accuracy (0.043 eV). Although the best-performing DFAs include a dispersion correction to account for long-range interactions, we find that dispersion corrections do not generally improve MAE or MRAE for this dataset.