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Abstract Challenges exist for assessing the impacts of climate and climate change on the hydrological cycle on local and regional scales, and in turn on water resources, food, energy, and natural hazards. Potential evapotranspiration (PET) represents atmospheric demand for water, which is required at high spatial and temporal resolutions to compute actual evapotranspiration and thus close the water balance near the land surface for many such applications, but there are currently no available high-resolution datasets of PET. Here we develop an hourly PET dataset (hPET) for the global land surface at 0.1° spatial resolution, based on output from the recently developed ERA5-Land reanalysis dataset, over the period 1981 to present. We show how hPET compares to other available global PET datasets, over common spatiotemporal resolutions and time frames, with respect to spatial patterns of climatology and seasonal variations for selected humid and arid locations across the globe. We provide the data for users to employ for multiple applications to explore diurnal and seasonal variations in evaporative demand for water. 

Abstract. Dryland regions are characterised by water scarcity and are facingmajor challenges under climate change. One difficulty is anticipating howrainfall will be partitioned into evaporative losses, groundwater, soilmoisture, and runoff (the water balance) in the future, which has importantimplications for water resources and dryland ecosystems. However, in orderto effectively estimate the water balance, hydrological models in drylandsneed to capture the key processes at the appropriate spatio-temporal scales.These include spatially restricted and temporally brief rainfall, highevaporation rates, transmission losses, and focused groundwater recharge.Lack of available input and evaluation data and the high computational costsof explicit representation of ephemeral surface–groundwater interactionsrestrict the usefulness of most hydrological models in these environments.Therefore, here we have developed a parsimonious distributed hydrologicalmodel for DRYland Partitioning (DRYP). The DRYP model incorporates the keyprocesses of water partitioning in dryland regions with limited datarequirements, and we tested it in the data-rich Walnut Gulch ExperimentalWatershed against measurements of streamflow, soil moisture, andevapotranspiration. Overall, DRYP showed skill in quantifying the maincomponents of the dryland water balance including monthly observations ofstreamflow (Nash–Sutcliffe efficiency, NSE, ∼ 0.7),evapotranspiration (NSE > 0.6), and soil moisture (NSE ∼ 0.7). The model showed that evapotranspiration consumes > 90 % of the total precipitation input to the catchment andthat < 1 % leaves the catchment as streamflow. Greater than 90 % of the overland flow generated in the catchment is lost throughephemeral channels as transmission losses. However, only ∼ 35 % of the total transmission losses percolate to the groundwater aquiferas focused groundwater recharge, whereas the rest is lost to the atmosphereas riparian evapotranspiration. Overall, DRYP is a modular, versatile, andparsimonious Python-based model which can be used to anticipate and plan forclimatic and anthropogenic changes to water fluxes and storage in drylandregions.