Non-Cognitive Predictors of Student Success:
A Predictive Validity Comparison Between Domestic and International Students
We must understand better
why Global Flood Models can differ locally.
Non-Cognitive Predictors of Student Success:
A Predictive Validity Comparison Between Domestic and International Students
The WHY
Global Flood Models (GFMs) are powerful tools to detect flood risk hotspots, provide early warning, and inform policy.
Yet, there are several major shortcomings:
1. Each GFM follows its own approach (Fig. 1);
2. GFMs employ different numerical schemes, data;
3. Validation is done for different basins using varying data and metrics (Tab. 1)
As a result, models can differ locally (Fig. 2) The WHAT
By establishing a GFM validation and benchmarking framework (Fig. 3) it becomes possible to disentangle the underlying drivers of the deviations through:
providing standard forcing data
validating & benchmarking model results
storing & indexing reference output The HOW
We need to test several elements of GFMs. To do so, we also foresee several challenges to be met.
Testing elements:
• Inundation extent & depth
• Discharge hydrograph
• Input forcing/data
• Regionality
Testing challenges:
• Test location
• Common forcing data
• Observed discharge, extent, and depth And THEN?
• Make it cloud-based and open
• Evolve into plug-and-play tool for model component coupling (Fig. 4)
• Open up model code and make it accessible
Moving towards a
Global Flood Model
Validation Framework
Jannis M. Hoch and Mark Trigg
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Interface Script GFM 1
GFM 3 GFM 2
Comp 1
Comp 3 Comp 2
Store model results
Validate model results
Standardized validation data
Benchmark model results Stored output
from other GFMs
Update data base and index
version Provide (pre-
processed) forcing data
sets
Upload model results
Provide
validation &
benchmarking output
Front-end Back-end
References:
Hoch, J. M., and Trigg, M. A.: Advancing global flood hazard simulations by improving comparability, benchmarking, and integration of global flood models, Environ. Res. Lett., 2019.
Trigg, M. A. et al: The credibility challenge for global fluvial flood risk analysis, Environ. Res. Lett., 2016.
📧 j.m.hoch@uu.nl
Climate reanalysis data
Land surface model Continuous river flow
routing
Flood frequency analysis
Downscaling or
calculating flood extents and depths
Global gauged flow data
Regional flow frequency analysis
Flood flow magnitude
Flood flow routing, rivers and floodplains
Calculate flood extents and depth
Climate cascade model type Gauged flow data model type
Fig. 1: Overview of different GFM modelling approaches and their modelling steps
Fig 3: Conceptual design of the proposed GFM validation & benchmarking Framework
Fig. 2: Agreement between GFMs of 1/100 years flood extent for the lower Niger
Fig. 4: Conceptualization of a GFM plug-and-play tool combining components (“Comp”) from different GFMs
Basins Periods Data sets
> 25 > 5 16
Tab. 1: Summary of meta-study analysing the different river basins, time periods, and data sets used for GFM validation
