Diffusive modeling of global river and floodplain dynamics
H21B-0839
based on 1km-resolution DEM
1*21
Dai YAMAZAKI , Shinjiro KANAE , Taikan OKI

Terrestrial water circulation is important both as a component of the climate
system and as a freshwater supplying system for human beings. Global river
routing models are practically the only available tool for simulating terrestrial
water circulation, however they have not adequately represented the physicalmechanism of terrestrial water storage and movement, such as floodplain
inundation dynamics regulated by much smaller-scale topography than globalmodel resolution.
The Catchment-based Macro-scale Floodplain model (CaMa-Flood) proposed inthis research overcomes this drawback by enabling higher resolution approachand explicit representation of sub-grid topography, and realized explicit
representation of floodplain inundation dynamics. Ability of CaMa-Flood is tested
by comparing simulated river discharge and inundated area extent with in-situ
and satellite observations.

Variables

Parameter(*Correspondence to Dai YAMAZAKI: yamadai@rainbow.iis.u-tokyo.ac.jp)

ZChannel Elevation
L
Channel LengthWChannel WidthEmbankment Height BA
Catchment Area
Floodplain Elevation Profilef(Topography)
River Storage
Sr
Floodplain Storage
SrRiver Water Depth
HrFloodplain Depth
HfInundated AreaAf

River channel and floodplain storages aredefined as continuative reservoirs in each grid.
Total water storage in each grid is divided intoriver channel and floodplain storage to balancewater surface elevation of both reservoirs.

LSf

Sf

Sr

aph
ogrpoT

HfHryBWZA

isfc
(i)

Downstream

Downstream

if  n v h2 2Manning Friction Slope

Si (t  t )  Si (t )   jupstream

Q j t  Qi t  Ai Ri t

Continuative Equation

Simulation of river discharge and floodplain inundation is executed with CaMaFlood. Primary results for Amazon River and Mekong River basins are validated
against in-situ and satellite observations.
To evaluate impacts of introducing floodplain
storage and diffusive wave equation, three setsof experiment are carried over.

Obs
FLD+Diff
FLD+Kine
NoFLD

NoFLD

Fld+Kine

Experiment
NoFLD
FLD+Kine
FLD+Diff

Fld+Diff

StorageRiver Channle Only
River Channel + Floodplain
River Channel + Floodplain

Flow Routing
Kinematic Wave
Kinematic Wave
Diffusive Wave

Simulated daily river discharge is validated
against GRDC observation discharge (OBS) atObidos. Fluctuation of river discharge by NoFLD
is quite large compared to other experiments
and observation. This implies that floodplain has
a role to smooth discharge variance by storingwater spilled out from river channel. Result by
FLD+Diff shows better fit to observation thanthat of FLD+Kine.
FLD+Diff

Simulated Flow Velocity by FLD+Diff and FLD+Kine
are compared in high water season of Amazon(May). Flow velocity by FLD+Diff is slower inbranches of Amazon River (Circled). This is because
Diffusive Wave can represent backwater effect (i.e.
flow stagnation due to water level rise in mainstream). Representation of backwater effect may
lead to the improved river discharge simulation byFLD+Diff.

2009 AGU fall meeting:43

Diffusive

St. Venant Momentum Equation[m/s]Group of pixels
which is drained into theoutlet pixel is decided as“catchment pixels” of thatcell. Catchment Area isdecided according to therealistic boundaries basedon fine-resolution dataset

River Channel Length ismeasured along with thefine-resolution flow pathconsidering meandering
at sub-grid scale.

v
QR

Diffusive Wave Equation is adopted forgoverning equation to represent backwater effect.
Friction slope is estimated with Manning Equation,and manning coefficient is set to 0.1 and 0.3 for river
channel and floodplain, respectively.
Water storage in next time step is predicted byContinuative Equation using inflow from upstream,outflow to downstream, and forcing runoff from LSM.

Simulated Inundated Area by FLD+Diff is validated against satellite observation by Prigent [2007]. Model isoverestimating inundated area in upper Amazon River basin in high water season, but overall spatial patternof inundated area is almost similar to the observation.

Obs

Obs

Elevation of catchment pixels is sorted to create a virtual cross-section of thefloodplain. This Floodplain Elevation Profile is used to objectively describe therelation among floodplain water storage, floodplain water depth, and inundated area.
[Ex] Water depth is 10m when 60% of the catchment area is inundatedQW  max[10.0,10.0  Q 0.5 ]B  max[1.0,1.0  Q 0.15 ]Annual Discharge

Channel Width and Channel Embankment Height, which are not resolved
even in those fine-resolution dataset, are decided empirically.

Tonle-sap lake located in lower Mekong River has unique characteristics. One is the drastic seasonal change of
its surface area, and the other is the reverse flow in Tonle-Sap River due to the reversal of water level of
Mekong River and Tonle-Sap Lake in rainy season. CaMa-Flood can represent both of these characteristics.
Especially, simulation of a large scale reverse flow is firstly achieved by a macro-scale hydrological model.
(a)

(a)

(a)2003-09, Obs.

(b)2003-06, Obs.

(c)2003-09, Sim.

(d)2003-06, Sim.

(a)

FLD+Diff

FLD+Diff●Siem Reap●●

Prek Kdam
Phnom Pen

(b)
(b)

(b)

High Water Season

FLD+Kine

Flow Velocity
DischargeForcing Runoff

SRTM30 DEM [NASA]

Fine-resolution flow
path is traced from theoutlet pixel until the next
outlet pixel is reached.
This reached cell isdecided as downstream,and thus River Networks
are constructed.

(j)

LiDynamichi0i sfc =dh/dx+i 0n•Oi=Av
Li1 v v v h  i0  i f  0g t g x x KinematicRiver channel

Cross-section Depth
Riverbed Slope
Water Surface Slope
Manning Coefficient(b) Backwater
isfc •A h(j)A specific pixel offlow direction map ischosen as the “outletpixel” of each coarseresolution cell. Channel
Elevation is decided as
the elevation of theoutlet pixel.

Floodplain

AfWater flux between grids is only considered alongwith prescribed River Networks. Flux calculation is done
separately for river channel and floodplain in order toconsider the difference of water depth and surface
roughness in both reservoirs.

(i)

GDBD Flow Direction [Masutomi, 2009]

AAf(a) Downward Flow
•AOi=AvhRiver Networks and sub-grid topographies
are objectively extracted from fineresolution (1km) flow direction map and
DEM using FLOW method [Yamazaki, 2009].

Flooded Fraction [%]2 Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Japan

CaMa-Flood is a distributed river routing model which is forced by LSM runoffand simulates water storage, inundated area, river discharge, etc.

MekongRiver

1Institute of Industrial Science, the University of Tokyo, Japan

Temporalvariationofsimulated
inundated area is also compared withsatellite observation for Amazon River
Central Floodplains. Model can predict
the average and seasonal variation ofinundated area at a certain level, eventhough predicted inundation peak is one
month earlier than observation.

Low Water Season

Obs.
Sim.

Explicit representation of sub-grid topography and introduction of Diffusive Wave isachieved in CaMa-Flood model. Those improvements on global river routing models
enables realistic simulation of floodplain inundation dynamics. Simulated results byCaMa-Flood shows better agreement to observations than previous river routings.
Masutomi et al.: Development of Highly Accurate Global Polygonal Drainage Basin Data. Hydrol. Proc., 2009
Prigent et al.: Global Inundation Dynamics Inferred from Multiple Satellite Observations, 1993-2000. J. Geophys. Res., 2007
Yamazaki et al.: Deriving a global river network map and its sub-grid topographic characteristics
from a fine-resolution flow direction map, Hydrol. Earth Syst. Sci.., 2009.
The first author is supported by a Research Fellowship from Japan Society for the Promotion of Science (JSPS). This research is partly supported by theInnovative Program of Climate Change Projection for the 21st Century (KAKUSHIN Program) by the Japanese Ministry of Education, Culture, Sports,Science, and Technology. We especially appreciate Dr. Prigent for providing her latest observation datasets.

Everything, Everywhere, Every Time: Advances in High-resolution Data Acquisition and Their Integration Into High-Fidelity Hydrologic Models (H21B)