Groundwater is a central component of the Earth system. However, our understanding of how it is dynamically interlinked with the atmosphere, hydrosphere, cryosphere, biosphere, geosphere, and anthroposphere remains limited. In the pursuit of understanding groundwater dynamics across diverse global settings, we present GROW (the global-scale integrated GROundWater package). This analysis-ready, quality-controlled dataset combines depth to groundwater and level time series from 55 countries, 91% from North America, India, Europe, and Australia, with associated Earth system variables. The dataset contains >200,000 time series with either daily, monthly, or yearly temporal resolution, accompanied by 36 time series or static attributes of meteorological, hydrological, geophysical, vegetation, and anthropogenic variables (e.g., precipitation, drainage density, rock type, NDVI, land use). 34 data flags regarding well features (e.g., coordinates and country), as well as time series characteristics (e.g., gap fraction or autocorrelation), facilitate quick data filtering. GROW provides a foundation for understanding large-scale groundwater processes in space and time, as well as for calibrating and evaluating models that simulate groundwater dynamics within the Earth system.

Groundwater, Earth’s largest nonfrozen freshwater reservoir, is vital for water supply security. Groundwater models help to manage complex domestic, agricultural, and industrial water demands while preserving ecosystem health under climate change. The community-driven groundwater model portal (GroMoPo) hosts groundwater model metadata to analyse biases and distribution of groundwater models. Over 450 models are currently featured on GroMoPo, with most models from high-GDP countries at local-to-regional scales. The GroMoPo initiative addresses current knowledge gaps and facilitates future collaboration and data sharing.

Topography affects the distribution and movement of water on Earth, yet new insights about topographic controls continue to surprise us and exciting puzzles remain. Here we combine literature review and data synthesis to explore the influence of topography on the global terrestrial water cycle, from the atmosphere down to the groundwater. Above the land surface, topography induces gradients and contrasts in water and energy availability. Long-term precipitation usually increases with elevation in the mid-latitudes, while it peaks at low- to mid-elevations in the tropics. Potential evaporation tends to decrease with elevation in all climate zones. At the land surface, topography is expressed in snow distribution, vegetation zonation, geomorphic landforms, the critical zone, and drainage networks. Evaporation and vegetation activity are often highest at low- to mid-elevations where neither temperature, nor energy availability, nor water availability—often modulated by lateral moisture redistribution—impose strong limitations. Below the land surface, topography drives the movement of groundwater from local to continental scales. In many steep upland regions, groundwater systems are well connected to streams and provide ample baseflow, and streams often start losing water in foothills where bedrock transitions into highly permeable sediment. We conclude by presenting organizing principles, discussing the implications of climate change and human activity, and identifying data needs and knowledge gaps. A defining feature resulting from topography is the presence of gradients and contrasts, whose interactions explain many of the patterns we observe in nature and how they might change in the future.

The hydrological dynamics of intermittent rivers and ephemeral streams (IRES) impacts the availability of water to riparian ecosystems, the height of downstream runoff peaks, and the replenishment of groundwater systems. Despite its significance, the influence of superficial geology on IRES flow processes remains an area of limited understanding. Here we first present a comprehensive data set encompassing streamflow and groundwater levels from an intermittent stream situated in New South Wales, Australia. We then use targeted geophysical investigations to show how the configurations of superficial geology control the streamflow and groundwater responses. The analysis reveals that periods of stable stream stage consistently occur after episodic surges in streamflow, followed by recession and channel desiccation. The duration of the stable phases exhibits an upstream‐to‐downstream pattern, reaching a maximum of 44 ± 3 days upstream and then abruptly declining further downstream. There is remarkable consistency in the duration of these stable flow periods, irrespective of the size of preceding streamflow peaks. We propose two primary controls of this behavior: (a) variability in permeability contrasts between channel alluvium and surrounding geological deposits, and (b) longitudinal fluctuations in the volume of the recent channel alluvial reservoir. The interplay of these controls generates a “goldilocks zone,” which optimizes riparian water availability and the potential for groundwater recharge in IRES landscapes. These geological controls may reflect a continuum present in other dryland catchments with widespread implications for groundwater recharge and stream classification based on flow occurrence and duration.

The subsurface Urban Heat Island effect has been proposed as a shallow geothermal energy resource, however, annual near-subsurface temperature variation may result in unexpected system performance. Understanding heat transport processes in the urban subsurface is key to managing and modelling city-scale thermal regimes for geothermal energy resource development. Existing studies have focussed on analysis of repeat temperature-depth profiles rather than long-term groundwater temperature time-series. We show here how time-series analysis can complement temperature-depth profiles and offer additional insights into the controls on subsurface thermal transport processes. Annual variations in temperature time-series from 49 boreholes in the Cardiff Geo-observatory (UK), recorded between 2014-2018, fall into several distinct shape categories. We hypothesise these shapes are indicative of the dominance of particular flow and heat transport mechanisms such that sinusoidal profiles are associated with conduction-only settings, while ‘right-skewed’ profiles denote the influence of advection. Short-lived temperature events are observed on the cooling limbs of such profiles and are correlated with groundwater level rises, indicative of recharge events. These winter temperature drops have the effect of cooling groundwater faster in winter than it is warmed in summer. The short timescales of these events suggest recharge is localised and may be controlled by preferential flow paths within the superficial deposits overlying the aquifer. While these events do have an overall cooling effect on the seasonal temperature profile, groundwater temperatures following these events recover quickly to levels near what they were before the recharge event, suggestive of the presence of local thermal non-equilibrium with the gravel aquifer. More complex behaviours observed in boreholes located close to the city’s rivers indicate recharge responses coupled with the influence of stream-aquifer interactions. Thus, temperature time-series data have potential as a tool to identify subsurface hydraulic and thermal processes, with implications for geothermal exploration and the wider field of hydrogeology.

In drylands, rainfall is typically delivered during short-lived, localised convective storms, whose characteristics strongly influence how water is partitioned into different terrestrial stores. However, the rainfall data often used in modelling future projections of water resources is typically derived from climate models that are too coarse to represent convective processes occurring at scales smaller than the model grid. In this paper we quantify the impact of climate model representation of convection on the simulated water balance at four locations in the Horn of Africa: a humid site in the Ethiopian Highlands, a semi-arid site in southern Kenya, an arid site in eastern Ethiopia, and a hyper-arid site in northern Somalia. We benchmark a novel pan-Africa convection permitting climate model (CP4A) and its parameterised counterpart (P25) against high-resolution satellite-derived gridded datasets of rainfall (IMERG) and potential evapotranspiration (PET) (hPET). The comparison shows that explicitly resolving convection improves the representation of dryland rainfall characteristics, such as rainfall frequency, intensity, and the relative contribution of low vs. high-intensity rainfall to annual totals. We also demonstrate that convective representation can impact model PET, but differences are more muted relative to rainfall, and both CP4A/P25 can capture seasonal and diurnal PET dynamics. To establish how the impact of convective representation on rainfall characteristics can control hydrology, we used Hydrus 1-D to run one-dimensional vadose zone hydrological simulations at our four study sites, where Hydrus is driven by rainfall and PET from CP4A and P25 (and hPET). We find that the “drizzle” bias in P25 means that when rainfall is propagated through Hydrus, wetting fronts are confined to upper soil layers, resulting in higher evaporative losses, lower soil moisture, and lower bottom drainage in drylands. The improved representation of dryland rainfall characteristics in CP4A means that cumulative (in mm) surface runoff is up to ten times higher (over the ten-year simulation), bottom drainage (indicative of potential recharge) is up to 25 times higher, and soil moisture remains above the wilting point for longer compared to P25 Hydrus runs (despite simulating lower total rainfall and infiltration). Whereas at our humid site, water partitioning is less sensitive to rainfall characteristics and hydrological fluxes more closely follow annual rainfall totals. Our results demonstrate that dryland vadose zone hydrology is highly sensitive to the impact of convective representation on rainfall characteristics, and that studies focused on modelling future water resources using climate models that parameterise the average effects of convection risk misrepresenting societally relevant fluxes such as soil moisture, groundwater availability, and surface runoff.

The relationship between topography and the terrestrial water cycle has been documented for thousands of years, yet there is still much to learn about Earth’s complex dynamics – both above, at, and below the surface.

Abstract. The increasing demand for freshwater resources due to population growth, economic development, and climate change, requires more accurate representation and quantification of the key components of the water balance at relevant scales that goes beyond catchment domains. To address this need, we present DRYP 2.0 (DRYland water Partition model), a new version of a parsimonious, process-based, spatially distributed hydrological model (DRYP). DRYP 2.0 introduces several new capabilities, including the hydrological representation of small ephemeral ponds and large lakes, multiple interacting hydrogeological domains within a single-layer groundwater model, and vegetation canopy interception and evaporation to better capture the effects of vegetation on hydrology across different climatic gradients. Computational performance has also been enhanced through more efficient algorithms that reduce simulation time for long runs and/or over large spatial domains. We demonstrate these advances using high-resolution (1 km, 1 h) simulations over the Horn of Africa Dryland region (2,000,000 km²) as well as through various synthetic numerical tests. The results highlight the ability of the model, even without calibration, to reproduce global remote sensing data such as soil moisture, actual evapotranspiration, and total water storage, while also significantly reducing computation time. Furthermore, the explicit inclusion of multiple hydrogeological domains reveals important impacts on water table depth, with implications for improving global-scale simulations of the water balance.

Topography affects the distribution and movement of water on Earth, but exciting puzzles remain and new discoveries regarding topographic controls continue to surprise us. In this contribution, we discuss some open questions regarding the influence of topography on the terrestrial water cycle based on a combination of literature review and data synthesis. How will changes in water and energy supply along elevation gradients translate into changes in actual evaporation, and how will this be modulated by plant physiological responses and topographically driven moisture redistribution? What role does groundwater play in sourcing the world's water towers, and how will this role change with melting of snowpacks and glaciers? What is the relative importance of topography (vs. climate and geology) in driving groundwater flow dynamics across scales, and how does topography influence inter-catchment groundwater flow or mountain block recharge? A key feature emerging from these questions is the presence of numerous interacting gradients and contrasts that explain many of the patterns we observe. Studying these interactions, and thus answering at least some of the questions posed above, has the potential to improve our understanding of hydrological systems and how they may evolve in the wake of global change.

Understanding subsurface heat transport processes is important for geothermal energy development and heat-flow modelling applications, and for resolving hydrogeological, biogeochemical and microbiological processes. Studies of subsurface thermal regimes have predominantly focussed on repeat temperature-depth profile analysis. The application of groundwater temperature time-series data to characterise thermal and hydraulic processes is relatively under-exploited. Here, an unusually rich set of half-hourly groundwater level and temperature time-series data from 48 boreholes in the Cardiff Geo-observatory (UK) between 2014 and 2018 is used to explore the interrelationships between subsurface hydraulic and thermal processes. Characteristic time-series curve shape categories were identified in annual-scale temperature changes and shown to be indicative of distinct flow and heat transport mechanisms. Sinusoidal curves are found in conduction-dominant settings, while ‘right-leaning’ time-series indicate faster cooling than warming and are associated with the influence of advection of heat due to recharge. Short-lived temperature events found on the cooling limbs of right-leaning curves correlate with sharp groundwater level rises, indicating recharge. Temperatures rebound quickly following these events but do not return to pre-event levels, having the effect of cooling groundwater faster in winter than it is warmed in summer. More complex behaviours observed in boreholes located close to rivers indicate recharge responses coupled with the influence of changes in stream–aquifer interactions which co-occur with heavy rainfall. The results demonstrate that groundwater temperature time-series interpretation may be a cost-effective way of providing new insights into the characteristics of subsurface hydraulic and thermal processes with implications for geothermal exploration and a range of other hydrogeological applications.

Global data have served an integral role in characterizing large-scale groundwater systems, identifying their sustainability challenges, and informing on socioeconomic and ecological dimensions of groundwater. These insights have revealed groundwater as a dynamic component of the water cycle and social–ecological systems, leading to an expansion in groundwater science that increasingly focuses on groundwater’s interactions with ecological, socioeconomic, and Earth systems. This shift presents many opportunities that are conditional on broader, more interdisciplinary system conceptualizations, models, and methods that require the integration of a greater diversity of data in contrast to conventional hydrogeological investigations. Here, we catalogue 144 global open access datasets and dataset collections relevant to groundwater science that span elements of the hydrosphere, biosphere, atmosphere, lithosphere, food systems, governance, management, and other socioeconomic system dimensions. The assembled catalogue offers a reference of available data for use in interdisciplinary assessments, and we summarize these data across their primary system, spatial resolution, temporal range, data type, generation method, level of groundwater representation, and institutional location of lead authorship. The catalogue includes 15 groundwater datasets, 23 datasets derived in relation to groundwater, and 106 datasets associated with groundwater. We find the majority of datasets are temporally static and that temporally dynamic data peak in availability during the 2000–2010 decade. Only a small fraction of temporally dynamic data is derived with any direct representation of groundwater, highlighting the need for greater incorporation of groundwater in Earth system models and data collection initiatives across socioeconomic, governance, and environmental science research communities. A small number of countries, led by the USA, Germany, the Netherlands, and Canada, generate most global groundwater data, reflecting a global North bias in the institutional leadership of these data generation activities. We raise three priority themes for future global groundwater data initiatives, which include: data improvements through prioritizing observed and temporally dynamic data; elevating regional and local scale data and perspectives to address challenges relating to equity and bias; and advancing data sharing initiatives founded on reciprocal benefits between global initiatives and data providers.

Groundwater is a central component of social-ecological systems. However, our understanding of how it is dynamically interlinked with the atmosphere, hydrosphere, cryosphere, biosphere, geosphere, and anthroposphere is limited. Existing datasets lack features that enable us to better understand groundwater functions and how they are affected by anthropogenic change. Specifically, there remains no large-scale groundwater dataset that provides analysis-ready groundwater time series alongside groundwater-associated variables and attributes. In the pursuit of understanding the planet's groundwater dynamics, we present GROW (global GROundWater analysis package). This user-friendly, quality-controlled dataset combines groundwater depth and level time series from around the world with associated social-ecological variables. GROW is designed to enable large-sample spatio-temporal groundwater analysis without much further preprocessing. The dataset contains more than 180,000 time series from 41 countries – whereby over 90 % of the time series are from either North America, Australia or Europe - in a daily, monthly, or yearly temporal resolution. Most of them are between 10 and 20 years long, from 01/1888 to 04/2024, and have a median depth to the water table of 8 metres. Groundwater data is paired with a total of 37 time series or attributes of meteorological, hydrological, geophysical, botanical, and anthropogenic variables (e.g., precipitation, ground elevation, aquifer type, NDVI, land use). More than 20 data flags about well features (e.g., location coordinates and license), as well as time series characteristics (e.g., gap fraction or length), simplify a quick data filtering tailored to specific needs. GROW provides an essential foundation understanding large-scale groundwater processes and provides a robust resource for calibrating and validating models that address groundwater dynamics in social-ecological systems. Gaining an enhanced insight in these processes is essential for managing groundwater resources and ensuring their long-term sustainability.

Seasonal rainfall is critical to lives and livelihoods within the Horn of Africa drylands (HAD), but it is highly variable in space and time. The main HAD rainfall seasons are typically defined as March-May (MAM) and October-December (OND). However, these 3-month periods are only generalized definitions of seasonality across the HAD, and local experience of rainfall may depart from these substantially. Here, we use daily rain gauge data with a duration of at least 10 years from 69 stations across the drylands of Kenya, Somalia, and Ethiopia to locally delineate key rainfall seasons. By calculating local seasonal rainfall timings, totals, and extremes, we obtain more accurate estimates of the spatial variability in rainfall delivery across the HAD, as well as climatological patterns. Results show high spatial variability in season onset, cessation, and length across the region, indicating that a homogenous classification of rainfall seasons across the HAD (e.g., MAM and OND) is inadequate for representing local rainfall characteristics. Our results show that the "long rains" season is not significantly longer than the "short rains" season over the period of study. This could be related to the previously documented decline of the "long rains" seasonal totals over recent decades. Several rainfall metrics also vary spatially between seasons, and the rainfall on the most extreme days can accumulate to double the local mean seasonal total. The locally defined rainfall seasons better capture the bulk of the rainfall during the season, giving improved characterization of rainfall metrics, consistent with the aim of a better understanding of rainfall impacts on local communities.

In dryland ecosystems, vegetation within different plant functional groups exhibits distinct seasonal phenologies that are affected by the prevailing hydroclimatic forcing. The seasonal variability of precipitation, atmospheric evaporative demand, and streamflow influences root-zone water availability to plants in water-limited environments. Increasing interannual variations in climate forcing of the local water balance and uncertainty regarding climate change projections have raised the potential for phenological shifts and changes to vegetation dynamics. This poses significant risks to plant functional types across large areas, especially in drylands and within riparian ecosystems. Due to the complex interactions between climate, water availability, and seasonal plant water use, the timing and amplitude of phenological responses to specific hydroclimate forcing cannot be determined a priori , thus limiting efforts to dynamically predict vegetation greenness under future climate change. Here, we analyze two decades (1994–2021) of remote sensing data (soil adjusted vegetation index (SAVI)) as well as contemporaneous hydroclimate data (precipitation, potential evapotranspiration, depth to groundwater, and air temperature), to identify and quantify the key hydroclimatic controls on the timing and amplitude of seasonal greenness. We focus on key phenological events across four different plant functional groups occupying distinct locations and rooting depths in dryland SE Arizona: semi-arid grasses and shrubs, xeric riparian terrace and hydric riparian floodplain trees. We find that key phenological events such as spring and summer greenness peaks in grass and shrubs are strongly driven by contributions from antecedent spring and monsoonal precipitation, respectively. Meanwhile seasonal canopy greenness in floodplain and terrace vegetation showed strong response to groundwater depth as well as antecedent available precipitation (aaP = P − PET) throughout reaches of perennial and intermediate streamflow permanence. The timings of spring green-up and autumn senescence were driven by seasonal changes in air temperature for all plant functional groups. Based on these findings, we develop and test a simple, empirical phenology model, that predicts the timing and amplitude of greenness based on hydroclimate forcing. We demonstrate the feasibility of the model by exploring simple, plausible climate change scenarios, which may inform our understanding of phenological shifts in dryland plant communities and may ultimately improve our predictive capability of investigating and predicting climate-phenology interactions in the future.

Seasonal rainfall forecasts support early preparedness. These forecasts are typically disseminated at Regional Climate Outlook Forums (RCOFs), in the form of seasonal tercile probability categories—above normal, normal, below normal. However, these categories cannot be related directly to impacts on terrestrial water stores within catchments, since they are mediated by non-linear hydrological processes occurring on fine spatiotemporal scales, including rainfall partitioning into infiltration, evapotranspiration, runoff and groundwater recharge. Hydrological models are increasingly capable of capturing these processes, but there is no simple way to drive such models with a specific RCOF seasonal tercile rainfall forecast. Here we demonstrate a new method, “Quantile Bin Resampling” (QBR), for producing seasonal water forecasts for a drainage basin by integrating a tercile seasonal rainfall forecast with a hydrological model. QBR is based on mapping historical quantiles of basin-average rainfall to historical simulations of the water balance, and circumvents challenges associated with using climate model output to drive impact models directly. We evaluate QBR by generating 35 years of seasonal reforecasts for various water balance stores and fluxes for the Upper Ewaso Ng’iro basin in Kenya. Hindcasts indicate that when input tercile rainfall forecasts have skill, QBR provides accurate water forecasts at kilometre-scale resolution, which is relevant for anticipatory action down to village level. Pilot operational experimental water forecasts were produced for this basin using QBR for the 2022 March-May rainfall season, then disseminated to regional stakeholders at the Greater Horn of Africa Climate Outlook Forum (GHACOF). We discuss this initiative, along with limitations, plans and future potential of the method. Beyond the demonstrated application to water-related forecasts, QBR can be easily adapted to work with any rainfall-driven impact model. It can translate objective tercile climate probabilities into impact-relevant water balance forecasts at high spatial resolution in an efficient, transparent and flexible way.

Groundwater is an essential resource for natural and human systems throughout the world and the rates at which aquifers are recharged constrain sustainable levels of consumption. However, recharge estimates from global-scale models regularly disagree with each other and are rarely compared to ground-based estimates. We compare long-term mean annual recharge and recharge ratio (annual recharge/annual precipitation) estimates from eight global models with over 100 ground-based estimates in Africa. We find model estimates of annual recharge and recharge ratio disagree significantly across most of Africa. Furthermore, similarity to ground-based estimates between models also varies considerably and inconsistently throughout the different landscapes of Africa. Models typically showed both positive and negative biases in most landscapes, which made it challenging to pinpoint how recharge prediction by global-scale models can be improved. However, global-scale models which reflected stronger climatic controls on their recharge estimates compared more favourably to ground-based estimates. Given this significant uncertainty in recharge estimates from current global-scale models, we stress that groundwater recharge prediction across Africa, for both research investigations and operational management, should not rely upon estimates from a single model but instead consider the distribution of estimates from different models. Our work will be of particular interest to decision makers and researchers who consider using such recharge outputs to make groundwater governance decisions or investigate groundwater security especially under the potential impact of climate change.

GroundwaterEarly View Technology Spotlight GroMoPo: A Groundwater Model Portal for Findable, Accessible, Interoperable, and Reusable (FAIR) Modeling Sam Zipper, Corresponding Author Sam Zipper [email protected] orcid.org/0000-0002-8735-5757 Corresponding author: Kansas Geological Survey and Department of Geology, University of Kansas, 1930 Constant Ave., Lawrence, KS 66047; [email protected]Search for more papers by this authorKevin M. Befus, Kevin M. Befus orcid.org/0000-0001-7553-4195 Department of Geosciences, University of Arkansas, Fayetteville, AR, USASearch for more papers by this authorRobert Reinecke, Robert Reinecke orcid.org/0000-0001-5699-8584 Institute of Geography, University of Mainz, Mainz, GermanySearch for more papers by this authorDaniel Zamrsky, Daniel Zamrsky orcid.org/0000-0001-6046-688X Department of Physical Geography, Utrecht University, Utrecht, The NetherlandsSearch for more papers by this authorTom Gleeson, Tom Gleeson orcid.org/0000-0001-9493-7707 Department of Civil Engineering, University of Victoria, Victoria, CanadaSearch for more papers by this authorSacha Ruzzante, Sacha Ruzzante orcid.org/0000-0003-4569-0183 Department of Civil Engineering, University of Victoria, Victoria, CanadaSearch for more papers by this authorKristen Jordan, Kristen Jordan orcid.org/0000-0002-7491-7490 Kansas Geological Survey, University of Kansas, Lawrence, KS, USASearch for more papers by this authorKyle Compare, Kyle Compare orcid.org/0000-0001-6655-8195 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, USASearch for more papers by this authorDaniel Kretschmer, Daniel Kretschmer orcid.org/0000-0003-0115-1268 Institute of Geography, University of Mainz, Mainz, Germany Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, GermanySearch for more papers by this authorMark Cuthbert, Mark Cuthbert orcid.org/0000-0001-6721-022X School of Earth and Environmental Sciences, Cardiff University, Cardiff, UKSearch for more papers by this authorAnthony M. Castronova, Anthony M. Castronova orcid.org/0000-0002-1341-5681 Consortium of Universities for the Advancement of Hydrologic Sciences Inc. (CUAHSI), Arlington, MA, USASearch for more papers by this authorThorsten Wagener, Thorsten Wagener orcid.org/0000-0003-3881-5849 Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, GermanySearch for more papers by this authorMarc F.P. Bierkens, Marc F.P. Bierkens orcid.org/0000-0002-7411-6562 Department of Physical Geography, Utrecht University, Utrecht, The Netherlands Deltares, Unit Subsurface and Groundwater Systems, Utrecht, The NetherlandsSearch for more papers by this author Sam Zipper, Corresponding Author Sam Zipper [email protected] orcid.org/0000-0002-8735-5757 Corresponding author: Kansas Geological Survey and Department of Geology, University of Kansas, 1930 Constant Ave., Lawrence, KS 66047; [email protected]Search for more papers by this authorKevin M. Befus, Kevin M. Befus orcid.org/0000-0001-7553-4195 Department of Geosciences, University of Arkansas, Fayetteville, AR, USASearch for more papers by this authorRobert Reinecke, Robert Reinecke orcid.org/0000-0001-5699-8584 Institute of Geography, University of Mainz, Mainz, GermanySearch for more papers by this authorDaniel Zamrsky, Daniel Zamrsky orcid.org/0000-0001-6046-688X Department of Physical Geography, Utrecht University, Utrecht, The NetherlandsSearch for more papers by this authorTom Gleeson, Tom Gleeson orcid.org/0000-0001-9493-7707 Department of Civil Engineering, University of Victoria, Victoria, CanadaSearch for more papers by this authorSacha Ruzzante, Sacha Ruzzante orcid.org/0000-0003-4569-0183 Department of Civil Engineering, University of Victoria, Victoria, CanadaSearch for more papers by this authorKristen Jordan, Kristen Jordan orcid.org/0000-0002-7491-7490 Kansas Geological Survey, University of Kansas, Lawrence, KS, USASearch for more papers by this authorKyle Compare, Kyle Compare orcid.org/0000-0001-6655-8195 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, USASearch for more papers by this authorDaniel Kretschmer, Daniel Kretschmer orcid.org/0000-0003-0115-1268 Institute of Geography, University of Mainz, Mainz, Germany Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, GermanySearch for more papers by this authorMark Cuthbert, Mark Cuthbert orcid.org/0000-0001-6721-022X School of Earth and Environmental Sciences, Cardiff University, Cardiff, UKSearch for more papers by this authorAnthony M. Castronova, Anthony M. Castronova orcid.org/0000-0002-1341-5681 Consortium of Universities for the Advancement of Hydrologic Sciences Inc. (CUAHSI), Arlington, MA, USASearch for more papers by this authorThorsten Wagener, Thorsten Wagener orcid.org/0000-0003-3881-5849 Institute of Environmental Sciences and Geography, University of Potsdam, Potsdam, GermanySearch for more papers by this authorMarc F.P. Bierkens, Marc F.P. Bierkens orcid.org/0000-0002-7411-6562 Department of Physical Geography, Utrecht University, Utrecht, The Netherlands Deltares, Unit Subsurface and Groundwater Systems, Utrecht, The NetherlandsSearch for more papers by this author First published: 22 July 2023 https://doi.org/10.1111/gwat.13343Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Supporting Information Filename Description gwat13343-sup-0001-Supinfo.pdfPDF document, 86.8 KB Data S1 Supporting information. 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Potential evapotranspiration (PET) represents the evaporative demand in the atmosphere for the removal of water from the land and is an essential variable for understanding and modelling land–atmosphere interactions. Weather generators are often used to generate stochastic rainfall time series; however, no such model exists for the generation of a stochastically plausible PET time series. Here we develop a stochastic PET generator, stoPET, by leveraging a recently published global dataset of hourly PET at 0.1∘ resolution (hPET). stoPET is designed to simulate realistic time series of PET that capture the diurnal and seasonal variability in hPET and to support the simulation of various scenarios of climate change. The parsimonious model is based on a sine function fitted to the monthly average diurnal cycle of hPET, producing parameters that are then used to generate any number of synthetic series of randomised hourly PET for a specific climate scenario at any point of the global land surface between 55∘ N and 55∘ S. In addition to supporting a stochastic analysis of historical PET, stoPET also incorporates three methods to account for potential future changes in atmospheric evaporative demand to rising global temperature. These include (1) a user-defined percentage increase in annual PET, (2) a step change in PET based on a unit increase in temperature, and (3) the extrapolation of the historical trend in hPET into the future. We evaluated stoPET at a regional scale and at 12 locations spanning arid and humid climatic regions around the globe. stoPET generates PET distributions that are statistically similar to hPET and an independent PET dataset from CRU, thereby capturing their diurnal/seasonal dynamics, indicating that stoPET produces physically plausible diurnal and seasonal PET variability. We provide examples of how stoPET can generate large ensembles of PET for future climate scenario analysis in sectors like agriculture and water resources with minimal computational demand.