G-RUN ENSEMBLE: A multi-forcing observation-based global runoff reanalysis

Abstract

<p>Although river flow is the best-monitored variable of the terrestrial water cycle, the scarcity of available in situ observations in large portions of the world has until now hindered the development of consistent observational estimates with global coverage. Recently, fusing sparse in-situ river discharge observations with gridded precipitation and temperature using machine learning has shown great potential for developing global monthly runoff estimates (Ghiggi et al., 2019). However, the accuracy of the utilised gridded precipitation and temperature products is variable and the corresponding uncertainty in the resulting runoff and river flow estimates was not yet quantified.</p><p>Global-RUNoff ENSEMBLE (G-RUN ENSEMBLE) (Ghiggi et al., in review) provides a multi-forcing global reanalysis of monthly runoff rates at a 0.5° resolution, composed of up to 525 runoff simulations. The G-RUN ENSEMBLE is based on 21 different atmospheric forcing datasets, overall spanning the period 1901-2019. The reconstructions are benchmarked against a comprehensive set of global-scale hydrological models (GHMs) simulations, using a large database of river discharge observations that were not used for model training as a reference.</p><p>Overall, the G-RUN ENSEMBLE shows good accuracy compared to the set of GHMs evaluated, especially with respect to the reproduction of the dynamics and seasonality of monthly runoff rates. We found that the spread imposed by the atmospheric forcing data in the G-RUN ENSEMBLE is small compared to the spread observed within the ensemble of GHMs simulations driven with a subset of such forcings. This might occur because GHMs are more impacted by biases in the input meteorological forcing and are more susceptible to accumulate errors over the simulation time than the adopted machine learning approach.</p><p>In summary, the multi-forcing nature of the G-RUN ENSEMBLE allows to quantify the uncertainty associated with the currently available atmospheric forcings, thereby paving the way for more robust and reliable water resources assessments, climate change attribution studies, hydro-climatic process studies as well as evaluation, calibration and refinement of GHMs.</p><p><strong>R</strong><strong>eferences</strong></p><p>Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L. 2019: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019.</p><p>Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: G-RUN ENSEMBLE: A multi-forcing observation-based global runoff reanalysis, Water Res. Res., in review.</p>

The 63-year changes in annual streamflow volumes across Europe with a focus on the Mediterranean basin

“…The quality control was conducted in succession on daily and aggregated time series, using the following steps reported in Gudmundsson and Seneviratne (2016) (Pettitt, 1979), and (iv) the von Neumann ratio test (Von Neumann, 1941). Homogeneity tests were carried out using the "iki.dataclim" statistical package for R (Orlowsky et al, 2014).…”

“…Homogeneity tests were carried out using the "iki.dataclim" statistical package for R (Orlowsky et al, 2014). The streamflow time series were considered as being consistent when the null hypothesis at the 1 % level was accepted at least in three of four tests (ECA & D; Gudmundsson and Seneviratne, 2016;Peña-Angulo et al, 2019). Despite the fact that potential levels of human-induced alterations in the river flow regime could be still present in time series data after the application of the aforementioned controls, a certain degree of disturbance can be tolerated (Murphy et al, 2013).…”

Anthropogenic climate change has influenced global river flow seasonality

“…https://doi.org/10.6084/m9.figshare.9228176.v2 (47). The model results are available from the ISIMIP2b project in Water (global) sector (https://www.isimip.org/outputdata/).…”

Forecasting tropical ENSO-induced drought conditions using sea surface height in the Western Pacific

“…It has a spatial resolution of 0.5° (~ 55 km) spanning from 1901 to 2019. Only discharge data from catchment basins with areas between 10 and 2,500 km 2 were used as training input to the algorithm 28,29 . This version of the GRUN dataset 28 improves upon the first iteration 29 by using temperature and precipitation data from an ensemble of 21 global datasets as input to the developed algorithm.…”

“…Only discharge data from catchment basins with areas between 10 and 2,500 km 2 were used as training input to the algorithm 28,29 . This version of the GRUN dataset 28 improves upon the first iteration 29 by using temperature and precipitation data from an ensemble of 21 global datasets as input to the developed algorithm. Validation of the runoff product utilized discharge data from basins with more than 10,000 km 2 of area and its accuracy was assessed against nine other global hydrological models 28 .…”