Abstract. Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques with evaluation against river flow at the global scale for the first time. The modelling procedure was challenging but doable, and even the first model version showed better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for >130 000 catchments (with an average resolution of 1000 km2), delineated to cover the Earth's landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, and the pathways between various compartments. Global parameter values were estimated using a stepwise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily and monthly time series (>10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, the World-Wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best (KGE >0.6) in the eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements, and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version. This first model version clearly indicates challenges in large-scale modelling, usefulness of open data, and current gaps in process understanding. However, we also found that catchment modelling techniques can contribute to advance global hydrological predictions. Setting up a global catchment model has to be a long-term commitment as it demands many iterations; this paper shows a first version, which will be subjected to continuous model refinements in the future. WWH is currently shared with regional/local modellers to appreciate local knowledge.

Abstract. Recent advancements in catchment hydrology (such as understanding hydrological processes, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques at the global scale for the first time. The modelling procedure was challenging but doable and even the first model version show better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for > 130 000 catchments (with an average resolution of 1000 km2), delineated to cover the Earths landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, interfacial fluxes, and the pathways between various compartments. Global parameter values were estimated using a step-wise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily time-series (> 10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in an average monthly KGE of 0.4. However, the world-wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best in Eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows and constancy of daily flow. Nevertheless, there remains large potential for model improvements and we suggest both redoing the calibration and reconsidering parts of the model structure for the next WWH version. The calibration cycle should be repeated a couple of times to find robust values under new fixed parameter conditions. For the next iteration, special focus will be given to precipitation, evapotranspiration, soil storage, and dynamics from hydrological features, such as lakes, reservoirs, glaciers, and floodplains. This first model version clearly indicates challenges in large scale modelling, usefulness of open data and current gaps in processes understanding. Parts of the WWH can be shared with other modellers working at the regional scale to appreciate local knowledge, establish a critical mass of experts and improve the model in a collaborative manner. Setting up a global catchment model has to be a long-term commitment of continuous model refinements to achieve successful and truly useful results.

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions. ARTICLE HISTORY

Discharge projections into the Hudson Bay Complex to 2070 are investigated for global mean temperature warming levels of 1.5 and 2.0 °C. Median precipitation increases from 1986–2005, ranging from 2% during summer to 19% during winter, are projected to increase discharge in all seasons except summer. The rise in discharge is greatest furthest north, into Foxe Basin, Ungava Bay, and Hudson Strait, exceeding 10% above historical annual means. A 2.0 °C warming results in higher discharge than 1.5 °C warming owing to greater precipitation (e.g., 6.5% greater spring discharge increase); however, summer discharge for 2.0 °C warming is lower due to enhanced evaporation and lower precipitation increase from historical (4.0% lower summer discharge increase). Extreme daily high flows are projected to be greater than historical, more so for 2.0 °C warming than 1.5 °C warming, and this is greatest in the eastern and northern regions. These projections suggest continued increasing river discharge into pan‐Arctic coastal oceans.

Introduction The Hudson Bay Drainage Basin (HBDB) comprises over a third of the Canadian landmass, contains important hydroelectric infrastructure and agricultural lands, and accounts for over a fifth of freshwater exports into the pan-arctic ocean system via the Hudson Bay Complex (HBC comprised of Hudson Bay, James Bay, Ungava Bay, Foxe Basin, and Hudson Strait; McClelland et al., 2006). The HBC itself is important to Arctic marine wildlife, and as a shipping route for Canada. It is a large region of primary production, influenced mainly by the timing of freshwater inputs (largely meltwater). Changing freshwater inputs are essential to the annual formation, decay and break up of sea ice, with seasonal ice cover being important for the HBC pelagic life (Manak and Mysak, 1989; Saucier et al. 1998, 2004). HBDB terrestrial discharge influences Arctic Ocean circulation, sea ice dynamics, biological and biogeochemical processes within the HBC. Due to the importance of terrestrial freshwater fluxes into the HBC for bay-wide circulation and ice-formation, continental-scale hydrologic modeling is key to the combined modeling/observation BaySys group of projects (Barber, 2014). The importance of the HBDB necessitates a comprehensive hydrological monitoring and prediction framework. Although hydrometric gauges remain relatively dense in southern, lower-latitude regions of the basin, higher-latitude counterparts remain poorly gauged. Additionally, the number of hydrometric gauges has declined across Canada in recent decades (Coulibaly et al., 2013; Mlynowski et al., 2011). As of 2013, 40% of the HBDB is ungauged and 27% has never been gauged. Hydrological modeling is required to fill spatial and temporal gaps in the observational record, and for making long-term streamflow projections. Over the same period this basin has undergone significant climate change impacting the distribution and