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.

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.

Modern hydrology relies on multiple sources of information combined with climatological, hydrological and glaciological data. These data can be collected through various sources such as private initiatives by companies, research programs, and both national and international organisms. They also vary by types, e.g., in situ measurements, satellite, reanalysis and simulated data. Recently the ANDEX research project, as a GEWEX regional program, was created to understand the processes related to the hydrological cycle and energy fluxes in the Andean region from Colombia to Patagonia. It is quite challenging to carry out this program given the complex orography and diversity of climates from tropical to sub-polar climates. This review article is a compilation of the various databases that are useful for hydrometeorological research in the South American Andes. The National Meteorological and Hydrological Services in Bolivia, Chile, Colombia, Ecuador, Peru, Venezuela and Argentina provide a large amount of data however the high-elevation areas are poorly instrumented and the number of stations varies greatly between the countries. National databases are only partially shared with the international bodies responsible for summarizing the existing data; this causes problems in term of data product assimilation. Across the entire continent, too few radiosondes are being used despite the fact that these data are crucial for validating and identifying problems in the atmospheric models. An increasing number of satellite data are available but it is difficult to assimilate them into the hydroclimatological models suited to the adjusted spatial and temporal resolutions. Specifically, for precipitation, we recommend merged products that account for the high spatial and temporal variability across the Andes. Finally, the international ANDEX program could be an excellent opportunity to increase the knowledge of the hydrological processes in the Andes.

The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6° S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day⁻¹ to 0.2 for rates higher than 20 mm day⁻¹. Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.

The spatiotemporal modes of seasonal rainfall variability and their relation with sea surface temperature anomalies (SSTA 1.2 indices) are examined in the transition from the coastal plain towards the western Andes cordillera in southern Ecuador/northwestern Peru using instrumental records (1970–2000) collected from the Catamayo–Chira basin. A multi-criteria data analysis is conducted within different elevation ranges. The criteria involve rotated principal components, cross correlations and temporal changes of anomalies in rainfall quantiles. The results confirm that SSTA 1.2 indices influence rainfall variability over the coastal plain (< 510 m a.s.l.) where forcing is dominant within December–May. The El Niño Southern Oscillation also plays a role inland of the coastal plain where a region of ENSO-like rainfall variability is found on the southeastern part of the basin (4°30'–5° S/79°15'–80° W) within March–May (MAM). This suggests that inland distance and elevation are only partial controls of ocean–atmospheric forcing up to ~ 1300 m a.s.l. Our analysis also provides evidence of the SSTA 1.2 indices influence in a large altitudinal range ~ 1400–2700 m a.s.l. confined to the southeastern basin. This region is found consistently perturbed by ENSO within MAM. We conclude that geo-morphological features of the southwestern Ecuadorian Andean ridges play a twofold role in the control of ocean–atmospheric forcing. They can modulate the atmospheric circulation, leading to a dissipation of the signal, or they might favor meteorological processes, leading to enhancement of orographic precipitation. This would explain the observed ENSO signals in instrumental records at locations as high as 2700 m a.s.l.

The seasonal predictability of daily rainfall characteristics is examined over 21 hydrologic units in the Pacific–Andean region of Ecuador and Peru (PAEP) using a nonhomogeneous hidden Markov model (NHMM) and retrospective seasonal information from general circulation models (GCMs). First, a hidden Markov model is used to diagnose four states that play distinct roles in the December–May rainy season. The estimated daily states fall into two wet states, one dry state, and one transitional dry–wet state, and show a systematic seasonal evolution together with intraseasonal and interannual variability. The first wet state represents regionwide wet conditions, while the second one represents north–south gradients. The former could be associated with the annual moisture offshore of the PAEP, thermally driven by the climatological maximum of sea surface temperatures in the Niño-1.2 region. The latter corresponds with the dynamically noisy component of the PAEP rainfall signal, associated with the annual displacement of the intertropical convergence zone. Then, a four-state NHMM is coupled with GCM information to simulate daily sequences at each station. Simulations of the GCM–NHMM approach represent daily rainfall characteristics at station level well. The best skills were found in reproducing the interannual variation of seasonal rainfall amount and mean intensity at the regional-averaged level with correlations equal to 0.60 and 0.64, respectively. At catchment level, the best skills appear over catchments south of 4°S, where hydrologically relevant characteristics are well simulated. It is thus shown that the GCM–NHMM approach provides the potential to produce precipitation information relevant for hydrological prediction in this climate-sensitive region.