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We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al. (2015b, https://doi.org/10.5065/D6MW2F4D) together with the catchment attributes introduced in this paper (https://doi.org/10.5065/D6G73C3Q) constitute the freely available CAMELS data set, which stands for Catchment Attributes and MEteorology for Large-sample Studies.

The MERRA-2 atmospheric reanalysis product provides global, 1-hourly estimates of land surface conditions for 1980–present at ∼50-km resolution. MERRA-2 uses observations-based precipitation to force the land (unlike its predecessor, MERRA). This paper evaluates MERRA-2 and MERRA land hydrology estimates, along with those of the land-only MERRA-Land and ERA-Interim/Land products, which also use observations-based precipitation. Overall, MERRA-2 land hydrology estimates are better than those of MERRA-Land and MERRA. A comparison against GRACE satellite observations of terrestrial water storage demonstrates clear improvements in MERRA-2 over MERRA in South America and Africa but also reflects known errors in the observations used to correct the MERRA-2 precipitation. Validation against in situ measurements from 220–320 stations in North America, Europe, and Australia shows that MERRA-2 and MERRA-Land have the highest surface and root zone soil moisture skill, slightly higher than that of ERA-Interim/Land and higher than that of MERRA (significantly for surface soil moisture). Snow amounts from MERRA-2 have lower bias and correlate better against reference data from the Canadian Meteorological Centre than do those of MERRA-Land and MERRA, with MERRA-2 skill roughly matching that of ERA-Interim/Land. Validation with MODIS satellite observations shows that MERRA-2 has a lower snow cover probability of detection and probability of false detection than MERRA, owing partly to MERRA-2’s lower midwinter, midlatitude snow amounts and partly to MERRA-2’s revised snow depletion curve parameter compared to MERRA. Finally, seasonal anomaly R values against naturalized streamflow measurements in the United States are, on balance, highest for MERRA-2 and ERA-Interim/Land, somewhat lower for MERRA-Land, and lower still for MERRA (significantly in four basins).

Global warming is expected to intensify the Earth's hydrological cycle and increase flood and drought risks. Changes over the 21st century under two warming scenarios in different percentiles of the probability distribution of streamflow, and particularly of high and low streamflow extremes (95th and 5th percentiles), are analyzed using an ensemble of bias-corrected global climate model (GCM) fields fed into different global hydrological models (GHMs) provided by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) to understand the changes in streamflow distribution and simultaneous vulnerability to different types of hydrological risk in different regions. In the multi-model mean under the Representative Concentration Pathway 8.5 (RCP8.5) scenario, 37 % of global land areas experience an increase in magnitude of extremely high streamflow (with an average increase of 24.5 %), potentially increasing the chance of flooding in those regions. On the other hand, 43 % of global land areas show a decrease in the magnitude of extremely low streamflow (average decrease of 51.5 %), potentially increasing the chance of drought in those regions. About 10 % of the global land area is projected to face simultaneously increasing high extreme streamflow and decreasing low extreme streamflow, reflecting the potentially worsening hazard of both flood and drought; further, these regions tend to be highly populated parts of the globe, currently holding around 30 % of the world's population (over 2.1 billion people). In a world more than 4° warmer by the end of the 21st century compared to the pre-industrial era (RCP8.5 scenario), changes in magnitude of streamflow extremes are projected to be about twice as large as in a 2° warmer world (RCP2.6 scenario). Results also show that inter-GHM uncertainty in streamflow changes, due to representation of terrestrial hydrology, is greater than the inter-GCM uncertainty due to simulation of climate change. Under both forcing scenarios, there is high model agreement for increases in streamflow of the regions near and above the Arctic Circle, and consequent increases in the freshwater inflow to the Arctic Ocean, while subtropical arid areas experience a reduction in streamflow.

Climate warming induces shifts from snow to rain in cold regions 1 , altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services 2 , 3 – 4 . A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain 5 , 6 , 7 , 8 – 9 . Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950–2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the ‘less snow equals earlier streamflow’ heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world. Analysis of streamflow measurements from 1950 to 2020 across 3,049 snow-affected catchments over the Northern Hemisphere shows that seasonal streamflow occurs earlier in snow-heavy catchments but later in less snowy regions.

Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient =0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.

The amount of water stored on continents is an important constraint for water mass and energy exchanges in the Earth system and exhibits large inter-annual variability at both local and continental scales. From 2002 to 2017, the satellites of the Gravity Recovery and Climate Experiment (GRACE) mission have observed changes in terrestrial water storage (TWS) with an unprecedented level of accuracy. In this paper, we use a statistical model trained with GRACE observations to reconstruct past climate-driven changes in TWS from historical and near-real-time meteorological datasets at daily and monthly scales. Unlike most hydrological models which represent water reservoirs individually (e.g., snow, soil moisture) and usually provide a single model run, the presented approach directly reconstructs total TWS changes and includes hundreds of ensemble members which can be used to quantify predictive uncertainty. We compare these data-driven TWS estimates with other independent evaluation datasets such as the sea level budget, large-scale water balance from atmospheric reanalysis, and in situ streamflow measurements. We find that the presented approach performs overall as well or better than a set of state-of-the-art global hydrological models (Water Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a spatial resolution of 0.5∘, at both daily and monthly scales over the period 1901 to present, based on two different GRACE products and three different meteorological forcing datasets, resulting in six reconstructed TWS datasets of 100 ensemble members each. Possible user groups and applications include hydrological modeling and model benchmarking, sea level budget studies, assessments of long-term changes in the frequency of droughts, the analysis of climate signals in geodetic time series, and the interpretation of the data gap between the GRACE and GRACE Follow-On missions. The presented dataset is published at https://doi.org/10.6084/m9.figshare.7670849 (Humphrey and Gudmundsson, 2019) and updates will be published regularly.

Large Arctic river basins experience substantial variability in climatic, landscape, and permafrost conditions. However, the processes behind the observed changes at the scale of these basins are relatively poorly understood. While most studies have been focused on the “Big 6” Arctic rivers – the Ob', Yenisey, Lena, Mackenzie, Yukon, and Kolyma – few or no assessments exist for small and medium-sized river basins, such as the Yana and Indigirka River basins. Here, we provide a detailed analysis of streamflow data from 22 hydrological gauges in the Yana and Indigirka River basins with a period of observation ranging from 35 to 79 years up to 2015. These river basins are fully located in the zone of continuous permafrost. Our analysis reveals statistically significant (p<0.05) positive trends in the monthly streamflow time series during the autumn–winter period for most of the gauges. The streamflow increases in a stepwise pattern (post-1981) for 17 out of 22 gauges in September (average trend value for the period of record is 58 % or 9.8 mm) and 15 out of 22 gauges in October (61 % or 2.0 mm). The positive trends are seen in 9 out of 19 rivers that do not freeze in November (54 %, 0.4 mm) and 6 out of 17 rivers that do not freeze in December (95 %, 0.15 mm). Precipitation is shown to decrease in late winter by up to 15 mm over the observational period. Additionally, about 10 mm of precipitation that used to fall as snow at the beginning of winter now falls as rain. Despite the decrease in winter precipitation, no decrease in streamflow has been observed during the spring freshet in May and June in the last 50 years (from 1966); moreover, five gauges show an increase of 86 % or 12.2 mm in spring floods via an abrupt change in 1987–1993. The changes in spring freshet start date are identified for 10 gauges; the earlier onset in May varies from 4 to 10 d over the observational period. We conclude that warmer temperatures due to climate change are impacting the hydrological regime of these rivers via changes in precipitation type (rain replacing snow). Other factors, such as the melting of permafrost, glaciers, and aufeis or changes in groundwater conditions, are likely to contribute as well; however, no direct observations of these changes are available. The changes in streamflow can have a significant impact on the ecology of the zone of continuous permafrost, while the increasing freshwater fluxes to the Arctic Ocean can impact the Arctic thermohaline circulation.

Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as a backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: (i) the Demer catchment dominated by agriculture and (ii) the Ourthe catchment dominated by mixed forests. We present the results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and leaf area index (LAI). The DA experiments covered the period from January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture–runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.

Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean–land–atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall–runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( <  4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall–runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall–runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not remedy poor reliability in very dry catchments. Overall, FoGSS is an attractive alternative to historical inflow sequences in all but the driest catchments. We discuss ways in which forecast reliability in very dry catchments could be improved in future work.