River Model Inter-comparison for(and before) SWOT
Cédric H. David1, Konstantinos M. Andreadis1, James S.
Famiglietti1, R. Edward Beighley2,Aaron A. Boone3, Dai Yamazaki4, Hyungjun Kim5, JeanMichel Fiset6, Ernesto Rodriguez1,Sylvain Biancamaria7, Rodrigo Paiva8
1.
2.
3.
4.
5.
6.
7.
8.

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
Northeastern University, Irvine, Boston, MA, United States
CNRM-GAME Meteo-France, Toulouse, France
Japan Agency for Marine-Earth Science and Technology, Yokosuka, Kanagawa, Japan
University of Tokyo, Tokyo, Tokyo, Japan
Environnement Canada, Montreal, Canada
LEGOS, Toulouse, France
Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil

Motivation

River gauges are disappearing (http://grdc.bafg.de)

Mystery between gauges (David et al. 2013)

Runoff is uncertain (from D. Lettenmaier)

SWOT should help (Biancamaria et al. 2016)

Background (1/4)

Saint Venant (1843)
à the golden equations

Miller et al. (1994)
à the first global scale river model

Modeling across scales involves a variety of simplifications

Background (2/4)
A world of grids

A world of features

Both approaches are equally frequent

Background (3/4)

A variety of equations is used

Background (4/4)

Anthropogenic effects are often not represented

Objectives
Understanding the best integration methods between expected SWOT
terrestrial retrievals and existing global hydrologic/hydrodynamicmodels
1. How can we best prepare for the expected SWOT continental to
global measurements before SWOT even flies? That is, how can
we understand the relationships between existing surface water
variations and expected SWOT capabilities?
2. What is the added value of including SWOT terrestrial
measurements into global hydro models for enhancing ourunderstanding of the terrestrial water cycle and the climate
system? Are current global hydrologic models ready to ingest
expected SWOT data? What SWOT variable(s) or SWOT-derived
product(s) offer the best promise for integration and for data
assimilation?

Approach
Justification

Consequence• Low barrier of entry to engage
many• Consistency among
simulations despite model
differences (apples/apples)• Consistency among
simulations despite basindifferences• Some expertise of the coreteam in study areas• Walking before running• Datasets readily available
online• Same runoff forcing, related
topography & river network• Global availability of data
products or modeling methods• Start with river basins withexisting team publications• Increasing complexity

Modeling paradigm

Four basins in four years

The basins studied in this project benefit from existing studies:a) the Mississippi [David et al., 2015],b) Saint-Lawrence [Fry et al., 2014],c) Niger [Pedinotti et al., 2014],d) Amazon [Beighley et al., 2009].

Many models• CaMaFLOOD (D. Yamazaki)• HRR (E. Beighley)• LISFLOOD (K. Andreadis)• RAPID (C. David)• ISBA-TRIP (A. Boone)• WATFLOOD (J. M. Fiset)• MGB-IPH (R. Paiva)• TRIP (H. Kim)
• Others?

Experimental design

We will combine an inter-comparison framework consisting of a series of six
horizontal water transfer schemes: CaMa-Flood [Yamazaki et al., 2011], HRR[Beighley et al., 2009], ISBA-TRIP [Decharme et al., 2012], LISFLOOD-FP [Batesand de Roo, 2000], RAPID [David et al., 2011], and WATFLOOD [Kouwen et al.,1993]. These models will be fed by runoff produced by the four land surface
models of NASA’s GLDAS [Rodell et al., 2004].

Preliminary results

Preliminary work has started to sub-sample continental-scale model outputs based on atentative SWOT trajectory. This endeavor was performed as community effort and is openly
accessible to members of the SWOT Science Team.

SWOT data look alike

Thickness of blue lines is function of simulated discharge
10-year simulation using RAPID (2000-2009), 15-min time step, output every 3-hr
Sub-sampling based on orbit at 890 km altitude, 77.6° inclination, 20.86 days repeatNo observational error accounted for here

Preliminary Mississippi parameters
Edit (2017-06-19), the URL provided was a typo:
ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/NLDAS_VIC0125_H.002/
It’s indeed the VIC data that we’re using as specified in the “Source”

Time line
• 08/31/16• 08/31/16 – 12/31/16 Analysis• 12/31/16 Mississippi study completedThanks!