Continental scale hydrologic-hydraulic model inter-comparison

MGB model
Mississipi basin
Preliminary results
Rodrigo Paiva, Ayan Fleischmann, Walter Collischonn
Institute of Hydraulic ResearchFederal University of Rio Grande do Sul
Porto Alegre, Brazil
03 October 2016www.ufrgs.br/lsh

1Modelo de Grandes Bacias (MGB-IPH) [Collischonn et al., 2007; Paiva et al.,2011; Pontes et al., 2015]
Coupled Hydrology – Hydraulic model for large basins

Catchment Flow Routing• Surface , subsurface and groundwater runoff are routed using linear reservoirs
River network routing:Continuity equation

Momentum equation

Muskingum-Cunge
Inertial model(Bates et al., 2010)22

Several model applications in South American rivers:•Flood forecasting and optimal reservoir operation•Climate change studies•Land use and land cover changes•Coupling with SIAQUA-IPH water quality model•Coupling with sediment transport model•Use in water management studies•Coupling with remote sensing data3

Ex.: Amazon application (Paiva et al., 2013 WRR)
Nash and Suttcliffe
at gauges

SRTM DEM

5763 catchmentsÓbidosRemote sensing

Model4

Niger River Basin
Inner Delta
MGB-IPH FLOODED AREAS MAP

Niger Inland Delta:•~30-50% of inflow waters(~Kemacina + Douna gauges)
evaporate until Dire gauge•Coupled hydrologic andhydrodynamic modeling

Discharge (m³/s)

Diré gauge, downstream of Inland Delta•(Fleischmann et al., in preparation

5

MGB coupling with river altimetry data

• Use altimetry for model validation (e.g. Paiva et al., 2013 WRR)
• Model calibration using altimetry (e.g. Getirana et al., )• Model / Altimetry based rating curves (Paris et al., 2016 WRR)
• Data assimilation (Paiva et al., 2013 HESS)

6

Ex.: Data Assimilation ENVISAT altimetry
Paiva et al. 2013. HESS•

Ensemble Kalman filter improved water level estimates•

Improvement at daily basis, even though altimetry data has ~35-day temporal resolution

Amazon BasinaAssimilation*bValidation

Envisat
EnKF

Open-loop

Gauge

7

MGB model
Mississipi basin
Preliminary results8

Modelo de Grandes Bacias (MGB-IPH) [Collischonn et al., 2007; Paiva et al.,2011; Pontes et al., 2015]

Catchment Flow Routing• Surface , subsurface and groundwater runoff are routed using linear reservoirs
River network routing:Continuity equation

Momentum equationMuskingum-Cunge
Inertial model(Bates et al., 2010)99

Modelo de Grandes Bacias (MGB-IPH) [Collischonn et al., 2007; Paiva et al.,2011; Pontes et al., 2015]

Surface runoff NASA NLDAS2 VIC
Subsurface runoff NASA NLDAS2

Catchment Flow Routing• Surface , subsurface and groundwater runoff are routed using linear reservoirs
River network routing:Continuity equation

Momentum equation

Muskingum-Cunge
Inertial model(Bates et al., 2010)1010

Model River network-Hydrosheds flow direction and flow accumulation maps-Minimum upstream drainage area- Geoprocessing using IPH Hydro Tools

11Model Catchments-Segmentation using Hydrosheds flow direction and flow accumulation maps
- ~16,000 Catchments with river reaches of 10 km.
- Geoprocessing using IPH Hydro Tools

12Model River network-Hydrosheds DEM-Catchments with river reaches of 10 km.
- Full hydrosheds river network (thin black lines)

13

Runoff fields

Surface runoff NASA NLDAS2 VIC 1/8° 1h
Subsurface runoff NASA NLDAS2 VIC 1/8° 1hSpatial variability of Surface and Subsurface Runoff
14Model parameters
Computational Reach lenght = ~ 10 km

Bankful Width (Andreadis et al. dataset)
Bankful Depth (Andreadis et al. dataset)
Manning’s coeficient: 0.03
Bed elevation = SRTM Elevation – Bankful Depth

Floodplain topography – Extracted from SRTM HydroSHEDS 15 arcsec
Surface and baseflow routing parameters:
Time of concentration (Kirpich formula using slope and lenght of major tributary)
CS = 10
CB = 10 days
15

First results: HD model, default W and H
Missouri River at Hermann

Ohio River at Metropolis

Volume errors
No reservoir simulation

Mississipi River at Grafton

Mississipi River at Thebes

Red River at Spring Bank
Attenuated and delayed hydrograph

16Simulations using larger Bankful Depth (H95)
Missouri River at Hermann

Ohio River at Metropolis

Mississipi River at Grafton

Mississipi River at Thebes

Red River at Spring Bank
Improved timing

17

Simulations using larger Bankful Width (W95)
Missouri River at Hermann

Ohio River at Metropolis

Mississipi River at Grafton

Mississipi River at Thebes

Red River at Spring Bank
Improved timing18

Maximum flooded areas
H, W default

H95, W default

H95, W95

19

Different parameters change flooding and hydrograph timing

Simulations using Muskingum Cunge Routing (No floodplainor backwater)
Missouri River at Hermann

Ohio River at Metropolis

Mississipi River at Grafton

Mississipi River at Thebes

Red River at Spring Bank
Needs backwater effectand/or inundation?

20

Simulations using larger Bankful Depth (H95)
Missouri River at Hermann

Ohio River at Metropolis

Mississipi River at Grafton

Mississipi River at Thebes

Red River at Spring Bank

21

Simulations using Baseflow Routing(Linear reservoir)
CB = 10 days
Missouri River at Hermann

Ohio River at Metropolis

Mississipi River at Grafton

Mississipi River at Thebes

Red River at Spring Bank
Baseflow routing smooth hydrograph

22

Simulations using Surface (CS=10) and Baseflow Routing (CB =10days)Missouri River at Hermann

Ohio River at Metropolis

Mississipi River at Grafton

Mississipi River at Thebes

Red River at Spring Bank

Surface runoff routing attenuates
spiky hydrographs
23

Summary• Large volume errors• Cause may be input runoff or reservoirs• River geometry input parameters play important role on flooding timing ofhydrographs• Catchment routing smoothes spiky hydrographs• Backwater and flooding may be important in some rivers

24Some MGB References:
PONTES, P., et al. MGB-IPH MODEL FOR HYDROLOGIC AND HYDRAULIC SIMULATION OF LARGE FLOODPLAIN RIVER SYSTEMS COUPLED WITH OPEN SOURCE GIS.
Submitted manuscript, 2016.
FLEISCHMANN, A.; PAIVA, R.; COLLISCHONN, W.; SORRIBAS, M.; PONTES, P. On river-floodplain interaction and hydrograph skewness. Water Resources
Research 2016, doi 10.1002/2016WR019233.
FAN, F. M.; SCHWANENBERG, D. ; ALVARADO, R. ; REIS, A. A. ; COLLISCHONN, W. ; NAUMANN, S. Performance of Deterministic and Probabilistic Hydrological
Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir. Water Resources Management, p. 1-17, 2016.
Siqueira et al., 2016. IPH-Hydro-Tools: a GIS coupled tool for watershed topology aquisition in open-source environment. Brazilian Journal of Water Resources.

SORRIBAS, MINO VIANA ; PAIVA, RODRIGO C. D. ; MELACK, JOHN M. ; BRAVO, JUAN MARTIN ; JONES, CHARLES ; CARVALHO, LEILA ; BEIGHLEY, EDWARD ; FORSBERG,BRUCE ; COSTA, MARCOS HEIL . Projections of climate change effects on discharge and inundation in the Amazon basin. Climatic Change (online), v. x, p. 1
FAN, F. M. ; FLEISCHMANN, A. S. ; COLLISCHONN, W. ; AMES, D. P. ; RIGO, D. . Large-scale analytical water quality model coupled with GIS for simulation of point sourced
pollutant discharges, Environmental Modelling & Software, Volume 64, February 2015, Pages 58-71, ISSN 1364-8152, http://dx.doi.org/10.1016/j.envsoft.2014.11.012.
Fan, Fernando Mainardi ; COLLISCHONN, WALTER ; MELLER, ADALBERTO ; BOTELHO, LUIZ CÉSAR MENDES . Ensemble streamflow forecasting experiments in a tropicalbasin: The São Francisco river case study. Journal of Hydrology (Amsterdam), v. SI, p. 10.1016/j.jhydr, 2014.
Paiva, R. C. D. ; Paiva, R. C. D. ; Collischonn, W. ; Bonnet, M.-P. ; De Gonçalves, L. G. G. ; Calmant, S. ; Getirana, A. ; Santos Da Silva, J. . Assimilating in situ and radar
altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon. Hydrology and Earth System Sciences Discussions (Online), v.
10, p. 2879-2925, 2013.
Paiva, R. C. D.; Buarque, D. C. ; Collischonn, W. ; Bonnet, M.-P. ; Frappart, F.; Calmant, S.; Bulhões Mendes, C. A.. Large-scale hydrologic and hydrodynamic modeling ofthe Amazon River basin. Water Resources Research, v. 49, p. 1226-1243, 2013.
Paiva e t al., 2011. Large scale hydrologic and hydrodynamic modelling using limited data and a GIS based approach. JoH.
Bravo, J. M. ; Allasia, D. ; Paz, A. R. ; Collischonn, W. ; Tucci, C. E. M. . Coupled Hydrologic-Hydraulic Modeling of the Upper Paraguay River Basin. Journal of Hydrologic
Engineering, v. 17, p. 635, 2012.
Buarque, D. C. ; Collischonn, W. ; Paiva, R. C. D. . Coupling a basin erosion and river sediment transport model into a large scale hydrological model: an application in theAmazon basin. Geophysical Research Abstracts, v. 14, p. 11935, 2012.

Paz, Adriano Rolim da ; Collischonn, Walter ; Tucci, Carlos E. M. ; Padovani, Carlos R. . Large-scale modelling of channel flow and floodplain inundation dynamics and its
application to the Pantanal (Brazil). Hydrological Processes (Print), v. 25, p. 1498-1516, 2011.
Collischonn, W. ; Allasia, D. G. ; Silva, B. C. ; Tucci, C. E. M. . The MGB-IPH model for large-scale rainfall-runoff modelling. Hydrological Sciences Journal, v. 52, p. 878-895,2007.