GRUN: an observation-based global gridded runoff dataset from 1902 to 2014

Ghiggi, Gionata; Humphrey, Vincent; Seneviratne, Sonia I.; Gudmundsson, Lukas

doi:10.5194/essd-11-1655-2019

Cited by 202 publications

(230 citation statements)

References 107 publications

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“…CC BY 4.0 License. Dai (2016) provides an estimate at about 36 500 km 3 /year, very close to the estimate by Zektser and Dzhamalov (1981), as well as to the value 38 450 km 3 /year estimated by Ghiggi et al (2019), based on GRUN for the period 1902 -2014; the latter authors also report results from earlier studies ranging from 30 000 to 66 000 km 3 /year. On the other hand, the recent study by Schellekens et al (2017) suggests a value of about 46 300 km 3 /year, very close to that by Shiklomanov and Sokolov (1985).…”

Section: Is Quite Indicative: 460supporting

confidence: 87%

Revisiting global hydrological cycle: Is it intensifying?

Koutsoyiannis¹

2020

Preprint

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Abstract. As a result of technological advances in monitoring atmosphere, hydrosphere, cryosphere and biosphere, as well as in data management and processing, several data bases have become freely available. These can be exploited in revisiting the global hydrological cycle with the aim, on the one hand, to better quantify it and, on the other hand, to test the established climatological hypotheses, according to which the hydrological cycle should be intensifying because of global warming. By processing the information from gridded ground observations, satellite data and reanalyses, it turns out that the established hypotheses are not confirmed. Instead of monotonic trends, there appear fluctuations from intensification to deintensification and vice versa, with deintensification prevailing in the 21st century. The water balance on land and sea appears to be lower than the standard figures of literature, but with greater variability on climatic time scales, which is in accordance with Hurst-Kolmogorov stochastic dynamics. The most obvious anthropogenic signal in the hydrological cycle appears to be the overexploitation of groundwater, which has a visible effect on sea level rise. Melting of glaciers has an equal effect, but in this case it in not known which part is anthropogenic, as studies on polar regions attribute mass loss mostly to ice dynamics.

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Section: Is Quite Indicative: 460supporting

confidence: 87%

Revisiting global hydrological cycle: Is it intensifying?

Koutsoyiannis¹

2020

Preprint

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“…This finding further suggests the urgent demand for ongoing efforts to make streamflow observation more accessible. In addition, new innovations in remote sensing (Gouweleeuw et al, 2018) or development of runoff reanalysis (Ghiggi et al, 2019) should also be supported to complement the understanding of changes in floods for locations that were not observed by stream gauges.…”

Section: The Implication Of Simulation Uncertainty On the Projection mentioning

confidence: 99%

“…However, no study has evaluated the performance of GHMs in terms of reproducing trends of streamflow indices, including flood indicators. To date, GHMs have been assessed extensively on their capacity to represent physical features of the hydrological regime, such as streamflow percentiles, the seasonal cycle or the timing of peak discharge (Gudmundsson et al, 2012a;Zaherpour et al, 2018;Beck et al, 2017;Zhao et al, 2017;Veldkamp et al, 2018;Pokhrel et al, 2012;Biemans et al, 2011;Giuntoli et al, 2018). Nevertheless, streamflow variability can be subject not only to long-term changes in atmospheric forcing, but also to climate variability (e.g.…”

Section: Introductionmentioning

confidence: 99%

Historical and future changes in global flood magnitude – evidence from a model–observation investigation

Hong

Zhao

Westra

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. To improve the understanding of trends in extreme flows related to flood events at the global scale, historical and future changes of annual maxima of 7 d streamflow are investigated, using a comprehensive streamflow archive and six global hydrological models. The models' capacity to characterise trends in annual maxima of 7 d streamflow at the continental and global scale is evaluated across 3666 river gauge locations over the period from 1971 to 2005, focusing on four aspects of trends: (i) mean, (ii) standard deviation, (iii) percentage of locations showing significant trends and (iv) spatial pattern. Compared to observed trends, simulated trends driven by observed climate forcing generally have a higher mean, lower spread and a similar percentage of locations showing significant trends. Models show a low to moderate capacity to simulate spatial patterns of historical trends, with approximately only from 12 % to 25 % of the spatial variance of observed trends across all gauge stations accounted for by the simulations. Interestingly, there are statistically significant differences between trends simulated by global hydrological models (GHMs) forced with observational climate and by those forced by bias-corrected climate model output during the historical period, suggesting the important role of the stochastic natural (decadal, inter-annual) climate variability. Significant differences were found in simulated flood trends when averaged only at gauged locations compared to those averaged across all simulated grid cells, highlighting the potential for bias toward well-observed regions in our understanding of changes in floods. Future climate projections (simulated under the RCP2.6 and RCP6.0 greenhouse gas concentration scenarios) suggest a potentially high level of change in individual regions, with up to 35 % of cells showing a statistically significant trend (increase or decrease; at 10 % significance level) and greater changes indicated for the higher concentration pathway. Importantly, the observed streamflow database under-samples the percentage of locations consistently projected with increased flood hazards under the RCP6.0 greenhouse gas concentration scenario by more than an order of magnitude (0.9 % compared to 11.7 %). This finding indicates a highly uncertain future for both flood-prone communities and decision makers in the context of climate change.

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“…Several global hydrological 45 products have been developed that provide estimates of runoff or river discharge, with a wide range of forcing and methodological approaches (e.g. Fekete et al, 2002;Döll et al, 2003;Qian et al, 2006;Sperna Weiland et al, 2010;Reichle et al, 2011;Yamazaki et al, 2011;Beck et al, 2017;Ghiggi et al, 2019;Lin et al, 2019). While these datasets can be used to understand past variability and change in the terrestrial hydrological cycle, they are currently not produced in an operational environment in near real time, so cannot be used for monitoring current global river conditions or provide initial conditions to 50 hydrometeorological forecasting systems.…”

Section: Introductionmentioning

confidence: 99%

GloFAS-ERA5 operational global river discharge reanalysis 1979–present

Harrigan¹,

Zsótér²,

Alfieri³

et al. 2020

Preprint

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Estimating how much water is flowing through rivers at the global scale is challenging due to a lack of observations 15 in space and time. A way forward is to optimally combine the global network of earth system observations with advanced numerical weather prediction (NWP) models to generate consistent spatio-temporal maps of land, ocean, and atmospheric variables of interest, known as a reanalysis. While the current generation of NWP output runoff at each grid cell, they currently do not produce river discharge at catchment scales directly, and thus have limited utility in hydrological applications such as flood and drought monitoring and forecasting. This is overcome in the Global Flood Awareness System (GloFAS; 20 http://www.globalfloods.eu/) by coupling surface and sub-surface runoff from the HTESSEL land surface model used within ECMWF's latest global atmospheric reanalysis (ERA5) with the LISFLOOD hydrological and channel routing model. The aim of this paper is to describe and evaluate the GloFAS-ERA5 global river discharge reanalysis dataset launched on 5 November 2019 (version 2.1 release). The river discharge reanalysis is a global gridded dataset with a horizontal resolution of 0.1° at a daily time step. An innovative feature is that it is produced in an operational environment so is available to users from 25 1 January 1979 until near real time (within 7 days behind real time). The reanalysis was evaluated against a global network of 1801 river discharge observation stations. Results found that the GloFAS-ERA5 reanalysis was skilful against a mean flow benchmark in 86 % of catchments according to the modified Kling-Gupta Efficiency Skill Score, although the strength of skill varied considerably with location. The global median Pearson correlation coefficient was 0.61 with an interquartile range of 0.44 to 0.74. The long-term and operational nature of the GloFAS-ERA5 reanalysis dataset provides a valuable dataset to the 30 user community for applications ranging from monitoring global flood and drought conditions, identification of hydroclimatic variability and change, and as raw input to post-processing and machine learning methods that can add further value. The dataset is openly available from the Copernicus Climate Change Service Climate Data Store: https://doi.Author contributions. SH drafted the manuscript and performed the evaluation. EZ wrote the suite to produce the dataset.CB adapted the suite to produce the dataset operationally. FW and CB were responsible for ingestion of the dataset into the Climate Data Store. LA, CP, PS, HC, and FP helped frame the paper. All co-authors contributed to the editing of the manuscript 360 and to the discussion and interpretation of results.

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GRUN: an observation-based global gridded runoff dataset from 1902 to 2014

Cited by 202 publications

References 107 publications

Revisiting global hydrological cycle: Is it intensifying?

Revisiting global hydrological cycle: Is it intensifying?

Historical and future changes in global flood magnitude – evidence from a model–observation investigation

GloFAS-ERA5 operational global river discharge reanalysis 1979–present

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