On the runoff validation of ‘Global BROOK90’ automatic modeling framework

Abstract

The recently presented Global BROOK90 automatic modeling framework combines a non-calibrated lumped hydrological model with ERA5 reanalysis data as the main driver, as well as with global elevation, land cover and soil datasets. The focus is to simulate the water fluxes within the soil–water–plant system of a single plot or of a small catchment especially in data-scarce regions. The comparison to runoff is an obvious choice for the validation of this approach. Thus, we choose for validation 190 small catchments (with a median size of 64 km2) with discharge observations available within a time period of 1979–2020 and located all over the globe. They represent a wide range of relief, land cover and soil types within all climate zones. The simulation performance was analyzed with standard skill-score criteria: Nash–Sutcliffe Efficiency, Kling–Gupta Efficiency, Kling–Gupta Efficiency Skill Score and Mean Absolute Error. Overall, the framework performed well (better than mean flow prediction) in more than 75% of the cases (KGESS > 0) and significantly better on a monthly rather than on a daily scale. Furthermore, it was found that Global BROOK90 outperforms GloFAS-ERA5 discharge reanalysis. Additionally, cluster analysis revealed that some of the catchment characteristics have a significant influence on the framework performance. HIGHTLIGHTS The study evaluates the runoff component performance of the Global BROOK90 automatic framework for hydrological modeling. Discharge observations from 190 small catchments located all over the globe were used. Satisfactory results for more than 75% of the catchments were achieved. KGE decomposition and influence of catchment characteristics on the framework performance were discussed.

Evaluation of Runoff Simulation Using the Global BROOK90-R Model for Three Sub-Basins in Türkiye

“…To find a solution to this problem, Vorobevskii et al [35] aimed to globalize the model and make it easily applicable by integrating global open-source datasets into the R package of the BROOK90 model, starting with the slogan "Just drop a catchment and receive a model output". The Global BROOK90-R (GB90-R) model developed for this purpose can be run for any basin and specified period [35][36][37]. GB90-R automatically downloads the required meteorological input and location related parameters (elevation, soil cover/usage, soil characteristics, and meteorological data) from global data sets [35,[38][39][40].…”

“…Vorobevskii et al [36] examined the flow prediction performance of the Global BROOK90-R model on daily and monthly time scales in 190 small watersheds (average 64 km 2 ) with diverse geographic conditions (i.e., climate, topography, land cover, and soil structure) around the world. The average success rate in runoff estimation of the non-calibrated GB90-R model by Vorobevskii et al [36] was evaluated according to the Kling-Gupta efficiency (KGE), Kling-Gupta efficiency skill score (KGESS), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE) metrics. The median success rate in estimating monthly runoff of the GB90 model for 190 basins by Vorobevskii et al [36] was obtained as 0.22, 0.45, 0.06, and 1 for KGE, KGESS, NSE, and MAE, respectively.…”

“…The average success rate in runoff estimation of the non-calibrated GB90-R model by Vorobevskii et al [36] was evaluated according to the Kling-Gupta efficiency (KGE), Kling-Gupta efficiency skill score (KGESS), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE) metrics. The median success rate in estimating monthly runoff of the GB90 model for 190 basins by Vorobevskii et al [36] was obtained as 0.22, 0.45, 0.06, and 1 for KGE, KGESS, NSE, and MAE, respectively. In addition, the estimated median values of KGE, KGESS, NSE, and MAE for a daily time scale in the studied basins were 0.11, 0.37, −0.06 and 1.25, respectively.…”

Editorial: Advances in Water Science Research

“…The topics of remote sensing, modeling, and new technology in hydrology are prominent in this issue. Four papers develop the application of the technologies in hydrology and water resources (Dang et al 2021;Guan et al 2021;Tian et al 2021;Vorobevskii et al 2021). Tian et al (2021) assess the accuracy of GPM products by comparing GPM-based satellite data and observed rain data for the Xiangjiang river catchment.…”

“…The verification results indicate that this method has an accuracy advantage and can be used as a convenient tool to determine reservoir characteristic curves. Vorobevskii et al (2021) evaluate the performance of the Global BROOK90 automatic framework for hydrological modeling using discharge observations from 190 small catchments located across the globe. They find that the framework performs well in more than 75% of the cases and significantly better on a monthly rather than on a daily scale.…”