Assessment of the potential forecasting skill of a global hydrological model in reproducing the occurrence of monthly flow extremes

Yossef, N.A.N.N. Candogan; Beek, L. P. H. van; Kwadijk, Jaap; Bierkens, Marc F. P.

doi:10.5194/hess-16-4233-2012

Cited by 26 publications

(28 citation statements)

References 43 publications

(42 reference statements)

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“…For more detailed information on CRUforcing, its processing, and PCR in general, we refer to van Beek (2008), Bierkens (2008), andvan Beek et al (2011). PCR was already applied for a wide range of studies such as flood and drought forecasting (Yossef et al, 2012), human impact on droughts (Wanders and Wada, 2015), global water stress (van Beek et al, 2011), and global groundwater simulations (de Graaf et al, 2015). More relevant to this study, PCR constitutes the computational backbone of the "GLObal Flood Risk with IMAGE Scenarios" framework (GLOFRIS; Winsemius et al, 2013), which is also used as the basis for the Aqueduct Global Flood Analyzer of the World Resources Institute (World Resources Institute, 2017).…”

Section: Pcr-globwbmentioning

confidence: 99%

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

et al. 2017

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Abstract. We here present GLOFRIM, a globally applicable computational framework for integrated hydrologicalhydrodynamic modelling. GLOFRIM facilitates spatially explicit coupling of hydrodynamic and hydrologic models and caters for an ensemble of models to be coupled. It currently encompasses the global hydrological model PCR-GLOBWB as well as the hydrodynamic models Delft3D Flexible Mesh (DFM; solving the full shallow-water equations and allowing for spatially flexible meshing) and LISFLOOD-FP (LFP; solving the local inertia equations and running on regular grids). The main advantages of the framework are its open and free access, its global applicability, its versatility, and its extensibility with other hydrological or hydrodynamic models. Before applying GLOFRIM to an actual test case, we benchmarked both DFM and LFP for a synthetic test case. Results show that for sub-critical flow conditions, discharge response to the same input signal is near-identical for both models, which agrees with previous studies. We subsequently applied the framework to the Amazon River basin to not only test the framework thoroughly, but also to perform a first-ever benchmark of flexible and regular grids on a large-scale. Both DFM and LFP produce comparable results in terms of simulated discharge with LFP exhibiting slightly higher accuracy as expressed by a Kling-Gupta efficiency of 0.82 compared to 0.76 for DFM. However, benchmarking inundation extent between DFM and LFP over the entire study area, a critical success index of 0.46 was obtained, indicating that the models disagree as often as they agree. Differences between models in both simulated discharge and inundation extent are to a large extent attributable to the gridding techniques employed. In fact, the results show that both the numerical scheme of the inundation model and the gridding technique can contribute to deviations in simulated inundation extent as we control for model forcing and boundary conditions. This study shows that the presented computational framework is robust and widely applicable. GLOFRIM is designed as open access and easily extendable, and thus we hope that other large-scale hydrological and hydrodynamic models will be added. Eventually, more locally relevant processes would be captured and more robust model inter-comparison, benchmarking, and ensemble simulations of flood hazard on a large scale would be allowed for.

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Section: Pcr-globwbmentioning

confidence: 99%

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

et al. 2017

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“…1; gauging stations and basin characteristics are summarized in Table 1. FEWS-World incorporates the global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). The capability of global hydrological models to predict streamflow was demonstrated previously by several studies such as the WaterGap (Alcamo et al, 2003;Döll et al, 2003), LaD (Milly and Schmakin, 2002), VIC (Nijssen et al, 2001), WBM (Vörösmarty et al, 2000;Fekete et al, 2002), Macro-PDM (Arnell, 1999(Arnell, , 2004 and PCR-GLOBWB (SpernaWeiland et al, 2010;van Beek et al, 2011). Candogan Yossef et al (2012 assessed the skill of the global hydrological model PCR-GLOBWB in reproducing past discharge extremes for 20 large rivers of the world, as a first step towards developing a global seasonal hydrological forecasting system and assessing its skill.…”

Section: Introductionmentioning

confidence: 97%

“…The BS values for April high flows at all lead times are higher for actual skill calculations where the forecasted discharges are compared to actual discharge records, than the theoretical skill where they are compared to model simulations. Indeed, it was shown by Candogan Yossef et al (2012) that the ESP procedure performs worse than the unconditional climatological record of observed flow from April to September even for lead times of 1 month. The forecast skill in the Brahmaputra is strongly dominated by MF during the monsoon season for all lead times.…”

Section: Tropical Monsoon-dominated Basinsmentioning

confidence: 99%

“…Candogan Yossef et al (2012) showed that hydrological forecasting skill in the Amazon basin is dominated by initial conditions for lead times of 1-2 months, and even up to 4 months for forecasting the discharge during the Southern Hemisphere spring, from August until November. Initial conditions are especially important during high-flow conditions (March, April and May) (Paiva et al, 2012) and the recession period (June, July, August), when the increased groundwater storage plays an important role.…”

Section: Tropical Monsoon-dominated Basinsmentioning

confidence: 99%

“…The study quantified skill in deterministic hindcast mode, using the ERA-40 reanalyses by the European Centre for Medium-range Weather Forecasts (ECMWF). This preliminary assessment by Candogan Yossef et al (2012) concluded that the prospects for seasonal forecasting with PCR-GLOBWB or comparable models are positive. Since actual probabilistic meteorological forecast ensembles were not used, the assessment did not include errors in the meteorological forcing.…”

Section: Introductionmentioning

confidence: 99%

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Skill of a global forecasting system in seasonal ensemble streamflow prediction

Yossef

Beek

Weerts

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. In this study we assess the skill of seasonal streamflow forecasts with the global hydrological forecasting system Flood Early Warning System (FEWS)-World, which has been set up within the European Commission 7th Framework Programme Project Global Water Scarcity Information Service (GLOWASIS). FEWS-World incorporates the distributed global hydrological model PCR-GLOBWB (PCRaster Global Water Balance). We produce ensemble forecasts of monthly discharges for 20 large rivers of the world, with lead times of up to 6 months, forcing the system with biascorrected seasonal meteorological forecast ensembles from the European Centre for Medium-range Weather Forecasts (ECMWF) and with probabilistic meteorological ensembles obtained following the ESP procedure. Here, the ESP ensembles, which contain no actual information on weather, serve as a benchmark to assess the additional skill that may be obtained using ECMWF seasonal forecasts. We use the Brier skill score (BSS) to quantify the skill of the system in forecasting high and low flows, defined as discharges higher than the 75th and lower than the 25th percentiles for a given month, respectively. We determine the theoretical skill by comparing the results against model simulations and the actual skill in comparison to discharge observations. We calculate the ratios of actual-to-theoretical skill in order to quantify the percentage of the potential skill that is achieved. The results suggest that the performance of ECMWF S3 forecasts is close to that of the ESP forecasts. While better meteorological forecasts could potentially lead to an improvement in hydrological forecasts, this cannot be achieved yet using the ECMWF S3 dataset.

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Evaluating snow models with varying process representations for hydrological applications

Magnusson¹,

Wever²,

Essery

et al. 2015

Water Resources Research

125

123

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Much effort has been invested in developing snow models over several decades, resulting in a wide variety of empirical and physically based snow models. For the most part, these models are built on similar principles. The greatest differences are found in how each model parameterizes individual processes (e.g., surface albedo and snow compaction). Parameterization choices naturally span a wide range of complexities. In this study, we evaluate the performance of different snow model parameterizations for hydrological applications using an existing multimodel energy-balance framework and data from two wellinstrumented alpine sites with seasonal snow cover. We also include two temperature-index snow models and an intensive, physically based multilayer snow model in our analyses. Our results show that snow mass observations provide useful information for evaluating the ability of a model to predict snowpack runoff, whereas snow depth data alone are not. For snow mass and runoff, the energy-balance models appear transferable between our two study sites, a behavior which is not observed for snow surface temperature predictions due to site-specificity of turbulent heat transfer formulations. Errors in the input and validation data, rather than model formulation, seem to be the greatest factor affecting model performance. The three model types provide similar ability to reproduce daily observed snowpack runoff when appropriate model structures are chosen. Model complexity was not a determinant for predicting daily snowpack mass and runoff reliably. Our study shows the usefulness of the multimodel framework for identifying appropriate models under given constraints such as data availability, properties of interest and computational cost.

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Assessment of the potential forecasting skill of a global hydrological model in reproducing the occurrence of monthly flow extremes

Cited by 26 publications

References 43 publications

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

Skill of a global forecasting system in seasonal ensemble streamflow prediction

Evaluating snow models with varying process representations for hydrological applications

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