Assessing the performance of global hydrological models  for capturing peak river flows in the Amazon basin

Towner, Jamie; Cloke, Hannah; Zsótér, Ervin; Flamig, Zachary L.; Hoch, Jannis; Bazo, Juan; Pérez, Erin Coughlan de; Stephens, Elisabeth

doi:10.5194/hess-23-3057-2019

Cited by 95 publications

(57 citation statements)

References 91 publications

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“…Twice per week (on Monday and Thursday) ECMWF ENS is extended to run to 46 days ahead at a coarser resolution (~36 km horizontal resolution), although in GloFAS only days 16 to 30 are used. The hydrological modelling components of GloFAS (Figure 1) comprises the land surface model of ECMWF IFS, HTESSEL (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land; Balsamo et al, 2009), and LISFLOOD, a spatially distributed grid-based hydrological and channel routing model (van der Knijff et al, 2010). Precipitation is transformed to surface and sub-surface runoff in HTESSEL, with groundwater and channel routing processes simulated in LISFLOOD.…”

Section: Glofas Components Configuration and Datamentioning

confidence: 99%

“…to historical data used for specific inter-comparisons in hydrological performance (e.g. Beck et al, 2017;Towner et al, 2019) rather that a comprehensive set of reforecasts or real-time forecasts. This will pave the way for multi-model forecast skill comparisons, such as those carried out routinely in the NWP field (for example, WMO Lead Centre for Deterministic NWP verification: https://apps.ecmwf.int/wmolcdnv/, last accessed: 13 October 2020).…”

Section: Future Directionsmentioning

confidence: 99%

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Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Harrigan

Zsótér

Cloke

et al. 2020

Preprint

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Abstract. Operational global-scale hydrological forecasting systems are widely used to help manage hydrological extremes such as floods and droughts. The vast amounts of raw data that underpin forecast systems and the ability to generate information on forecast skill have, until now, not been publicly available. As part of the Global Flood Awareness System (GloFAS; https://www.globalfloods.eu/) service evolution, in this paper daily ensemble river discharge reforecasts and real-time forecast datasets are made free and openly available through the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). They include real-time forecast data starting on 1 January 2020 updated operationally every day and a 20-year set of reforecasts and associated metadata, available through the dedicated GloFAS FTP service. This paper describes the model components and configuration used to generate the real-time river discharge forecasts and the reforecasts. An evaluation of ensemble forecast skill using the Continuous Ranked Probability Skill Score (CRPSS) was also undertaken for river points around the globe. Results show that GloFAS is skilful in over 93thinsp;% of catchments in the short- (1- to 3-days) and medium-range (5- to 15-days) against a persistence benchmark forecast, and skilful in over 80 % of catchments out to the extended-range (16- to 30-days) against a climatological benchmark forecast. However, the strength of skill varies considerably by location with GloFAS found to have no or negative skill at longer lead times in broad hydroclimatic regions in tropical Africa, western coast of South America, and catchments dominated by snow and ice in high northern latitudes. Forecast skill is summarised as a new headline skill score to be added as a layer on the GloFAS forecast Web Map Viewer at the next GloFAS cycle release, expected Autumn 2020, to aid user’s interpretation and understanding of forecast quality.

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Section: Glofas Components Configuration and Datamentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Harrigan

Zsótér

Cloke

et al. 2020

Preprint

Self Cite

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“…Yet, this is not the case and thus points to one of the key structural challenges with such cascading one-directional model coupling: while the most advanced hydrodynamic schemes can be added, the overall model accuracy still depends greatly on model data and parameter uncertainties, calibration, and both the meteorological and hydrologic forcing. Recent research showed, for instance, that the meteorological data set used can be a key control of discharge accuracy (Towner et al, 2019). Note also that PCR-DynRout and CMF use different topography and river bathymetry data as well as different river network concepts (i.e.…”

Section: Simulated Dischargementioning

confidence: 99%

“…Aligning the routines was, however, outside of the scope of this study. The arising issues of different inundation extent due to different model routines and data was already discussed by other studies and remains subject to ongoing debate on how to minimise the gap between models (Bernhofen et al, 2018;Hoch and Trigg, 2019;Trigg et al, 2016). Last, it is important to state that no calibration of the models with respect to simulated flood extent was performed.…”

Section: Inundation Extentmentioning

confidence: 99%

Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic–hydrodynamic model coupling framework

Hoch

Eilander

Ikeuchi

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Fluvial flood events are a major threat to people and infrastructure. Typically, flood hazard is driven by hydrologic or river routing and floodplain flow processes. Since they are often simulated by different models, coupling these models may be a viable way to increase the integration of different physical drivers of simulated inundation estimates. To facilitate coupling different models and integrating across flood hazard processes, we here present GLOFRIM 2.0, a globally applicable framework for integrated hydrologichydrodynamic modelling. We then tested the hypothesis that smart model coupling can advance inundation modelling in the Amazon and Ganges basins. By means of GLOFRIM, we coupled the global hydrologic model PCR-GLOBWB with the hydrodynamic models CaMa-Flood and LISFLOOD-FP. Results show that replacing the kinematic wave approximation of the hydrologic model with the local inertia equation of CaMa-Flood greatly enhances accuracy of peak discharge simulations as expressed by an increase in the Nash-Sutcliffe efficiency (NSE) from 0.48 to 0.71. Flood maps obtained with LISFLOOD-FP improved representation of observed flood extent (critical success index C = 0.46), compared to downscaled products of PCR-GLOBWB and CaMa-Flood (C = 0.30 and C = 0.25, respectively). Results confirm that model coupling can indeed be a viable way forward towards more integrated flood simulations. However, results also sug-gest that the accuracy of coupled models still largely depends on the model forcing. Hence, further efforts must be undertaken to improve the magnitude and timing of simulated runoff. In addition, flood risk is, particularly in delta areas, driven by coastal processes. A more holistic representation of flood processes in delta areas, for example by incorporating a tide and surge model, must therefore be a next development step of GLOFRIM, making even more physically robust estimates possible for adequate flood risk management practices.

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“…For these purposes, the Nash-Sutcliffe efficiency (NSE; Nash and Sutcliffe, 1970) and the Kling-Gupta efficiency (KGE;Gupta et al, 2009) are two commonly used performance metrics in hydrology (e.g. Newman et al, 2017;Towner et al, 2019). NSE and KGE measure the overall model performance can be measured by only a single numerical value within the range of minus infinity and one.…”

Section: Introductionmentioning

confidence: 99%

Technical note: Diagnostic efficiency – specific evaluation of model performance

Schwemmle¹,

Demand²,

Weiler³

2020

Preprint

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Abstract. Better understanding of the reasons why hydrological model performance is good or poor represents a crucial part for meaningful model evaluation. However, current evaluation efforts are mostly based on aggregated efficiency measures such as Kling-Gupta Efficiency (KGE) or Nash-Sutcliffe Efficiency (NSE). These aggregated measures only distinguish between good and poor model performance. Especially in the case of a poor model performance it is important to identify the different errors which may have caused such unsatisfying predictions. These errors may origin from the model parameters, the model structure, and/or the input data. In order to provide more insight, we define three types of errors which may be related to their origin: constant error (e.g. caused by consistent input data error such as precipitation), dynamic error (e.g. structural model errors such as a deficient storage routine) and timing error (e.g. caused by input data errors or deficient model routines/parameters). Based on these types of errors, we propose the novel Diagnostic Efficiency (DE) measure, which accounts for the three error types. The disaggregation of DE into its three metric terms can be visualized in a plain radial space using diagnostic polar plots. A major advantage of this visualization technique is that error contributions can be clearly differentiated. In order to provide a proof of concept, we first generated errors systematically by mimicking the three error types (i.e. simulations are surrogated by manipulating observations). By computing DE and the related diagnostic polar plots for the mimicked errors, we could then supply evidence for the concept. Finally, we tested the applicability of our approach for a modelling example. For a particular catchment, we compared streamflow simulations realized with different parameter sets to the observed streamflow. For this modelling example, the diagnostic polar plot suggests, that dynamic errors explain the model performance to a large extent. The proposed evaluation approach provides a diagnostic tool for model developers and model users and the diagnostic polar plot facilitates interpretation of the proposed performance measure.

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Assessing the performance of global hydrological models for capturing peak river flows in the Amazon basin

Cited by 95 publications

References 91 publications

Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System

Evaluating the impact of model complexity on flood wave propagation and inundation extent with a hydrologic–hydrodynamic model coupling framework

Technical note: Diagnostic efficiency – specific evaluation of model performance

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