Urban Land Surface Models (ULSMs) simulate energy and water exchanges between the urban surface and atmosphere. When part of numerical weather prediction, ULSMs provide a lower boundary for the atmosphere and improve the applicability of model results in the urban environment compared with non-urban land surface models. However, earlier systematic ULSM comparison projects assessed the energy balance but ignored the water balance which is coupled to the energy balance. Here, we analyze the water balance representation in 19 ULSMs participating in the Urban-PLUMBER project using results for 20 sites spread across a range of climates and urban form characteristics. As observations for most water fluxes are unavailable, we examine the water balance closure, flux timing, and magnitude with a score derived from seven indicators. We find that the water budget is only closed in 57% of the model-site combinations assuming closure when annual total incoming fluxes (precipitation and irrigation) fluxes are within 3% of the outgoing (all other) fluxes. Results show the timing is better captured than magnitude. No ULSM has passed all good water balance indicators for any site. Our results indicate models could be improved by explicitly verifying water balance closure and revising runoff parameterizations. By expanding ULSM evaluation to the water balance and related to latent heat flux performance, we demonstrate the benefits of evaluating processes with direct feedback mechanisms to the processes of interest.

Quantifying how climate change drives drought is a priority to inform policy and adaptation planning. We show that the latest Coupled Model Intercomparison Project (CMIP6) simulations project coherent regional patterns in meteorological drought for two emissions scenarios to 2100. We find robust projected changes in seasonal drought duration and frequency (robust over >45% of the global land area), despite a lack of agreement across models in projected changes in mean precipitation (24% of the land area). Future drought changes are larger and more consistent in CMIP6 compared to CMIP5. We find regionalised increases and decreases in drought duration and frequency that are driven by changes in both precipitation mean and variability. Conversely, drought intensity increases over most regions but is not simulated well historically by the climate models. The more robust projections of meteorological drought compared to mean precipitation in CMIP6 provides significant new opportunities for water resource planning.

Grassland responses to drought are strongly mediated by leaf phenology, with greening and browning being highly sensitive to soil moisture. However, this process is represented overly simplistically in most vegetation models, limiting their capacity to predict grassland responses to global change factors. We derive functions representing grassland phenological responses to soil water content (SWC), by fitting an empirical model to greenness data. Data were obtained from fixed cameras (phenocams) monitoring phenology at several grassland experiments in Sydney, Australia. The data-model synthesis showed that the sensitivity of growth to SWC exhibited a concave-down response in most species. For senescence, we found a strong nonlinear increase in senescence rate with declining SWC. Both findings contradict common assumptions in vegetation models. Incorporating nonlinear responses in the empirical model reduced the error in cover predictions by 12%. Model evaluation against data from drought treatments indicated that differential sensitivity of phenology to SWC helps explain differences among species’ responses to variable rainfall. Our work provides a new methodology, and new evidence, to support the development of improved representations of grassland phenology for vegetation models.