Propagation of Precipitation Uncertainty in Terrestrial Hydrological Modeling— Case Study over West Africa
1Xiaogang He ,Hyungjun

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

12
1Kim , Pat J.-F. Yeh , Taikan Oki

2 International Center for Water Hazard and Risk Management (ICHARM)

Under the auspices of UNESCO, Tsukuba, Ibaraki, Japan

3 Model

1 Introduction and Background
Several recent studies haveshown the importance of land
surface atmosphere coupling in
the monsoon system of the WestAfrica. The African Monsoon
Multidisciplinary Analysis (AMMA)
Courtesy from Boone and Rosnay (2007)
has provided an opportunity toinvestigate model deficiencies inCourtesy from Boone and Rosnay (2007)
land surface process by providing Fig.1 Location of three meso-scale sites: Oueme-Benin (red), Southcorrespondhigh resolution datasets. In West Niger (orange) and Gourma-Mali (blue). Contours
to the annually-averaged Leaf Area Index (LAI m2m-2)
participation with the AMMA Land
Surface Model Inter-comparison Project Phase 2 (ALMIP2), a global land
surface model is regionalized for conducting a high space-time resolution(0.05˚, 30 minutes) experiment over three distinct meso-scale domains in West
Africa (see Fig.1).

4 Similarity of Two Interpolation Scheme

Land Surface Model

External Parameters
NameSource

Sand Fraction

ECOCLIMAP2Clay Fraction

ECOCLIMAP2Vegetation type map

GLOBAL MAP

Ground albedo (visible)

ECOCLIMAP2

Ground albedo(near infrared)

ECOCLIMAP2Mean slope

GTOPO30

Standard deviation ofTopography

GTOPO30

Leaf Area Index

ECOCLIMAP2

Soil type in Niger
sandy loam

sand

GTOPO30 DEM

Land use typewooded c4 grasslandloamy
sand

Takata et al.
(2003)

Fig.4 Simulation of evapotranspiration andFig.3 Comparison of the similarity of ensemble members (ΩP,ΩET , and ΩR) in Benin and Niger (monthly variation).

The objective of this research is to investigate the impacts of uncertainty of
precipitation on the surface fluxes and hydrological responses through LSM.
Specifically, we estimate the uncertainty in observed precipitation due todifferent interpolation methods (Lagrangian-krigged and Thiessen) and its
propagation to the simulated evapotranspiration and runoff.

Schematic representation of Minimal
Advanced Treatments of Surface
Integration and Runoff (MATSIRO)
is presented in the above figure. Itsolves the water and energybalance at ground and canopy
surfaces in snow-covered and snow
free areas within a grid cell.

2 Methodology

5 Propagation of Uncertainty in Forcing Data through LSM Simulation

Multi-Precipitation+)

0n0

Cleared Forest

T106(~ 110km)n

GTOPO30 is used to estimate the subgrid topography.
Soil type is classified at each grid based on the sandfraction and clay fraction provided by ECOCLIMAP2(Champeaux et al., 2004). Vegetation map is referred fromthe global land use map.

 High values of Ω index for precipitation demonstrate that Lagrangian-kriggedand Thiessen method have similar temporal variability between each other.
 High values of ΩET means high similarity between two time series of ET.
 Ensemble similarity of runoff (ΩR) shows stronger spatial heterogeneity and its
relative lower values demonstrate that simulated runoff tends to haveinconsistent temporal variability.

Benin
Semi-arid

not sensitive

6 hourly0≈

n0
≈

n

Satellite Observation
GPCP V2.20n0n0nmn0Time periods: n

Time periods: mn1 m
σ aˆ = Var[ ∑ aij ]m i =1

1.0 º Monthly

Gauge Observation
GPCC

1.0 º Monthly

PREC/L

1.0 º Monthly

ALMIP2 (in situ)
Lagrangian-krigged

0.05 º 30 mins

Thiessen

0.05 º 30 mins

Monthly Variation

Ensemble members: m

Atmospheric Reanalysis

sandy clayloam

runoff forced by Lagrangian-krigged andThiessen rainfall

sensitive

Niger

σ a = Var[aij ]22

Fig.5 Relation between the uncertainty propagation for evapotranspiration (ΩET - ΩP) and runoff (ΩR - ΩP). Shaded color
indicates the similarity of precipitation ensemble members (ΩP).

mσ aˆ − σ aΩa =2(m − 1)σ a2

2

Benin
Very different

0~1
Identical

High-resolution observational data based on dense gauge station
networks provides a good chance for us to compare the biases amongdifferent precipitation products. Here we use Ω index to evaluate thesimilarity between the ensemble members. Temporal variation of themcan be seen in Fig. 2. For uncertainty propagation analysis, only
ALMIP2 data is used.

Fig.2 Time series and Ω index for 6
precipitation products in Benin and Niger.

References

Diurnal Cycle

 a11 a12 ⋅⋅⋅ a1n  a a ⋅⋅⋅ a 2n A =  21 22      am1 am 2 ⋅⋅⋅ amn Ensemble Coherence Index: ΩIndex to measure the similarity of
individual time series of ensemble
members [Koster et al., 2000]

Niger

Boone A., and Rosnay P. d., 2007, Towards the improved understanding of land-surface processes and coupling with the atmosphere over West Africa. iLEAP Newsletter, 3, 33-35.
Boone A., et al., 2012, ALMIP2: Whitepaper. Available on line: http://www.cnrm.meteo.fr/amma-moana/amma_surf/almip2/docs/ALMIP2_overview.pdf
Champeaux, J. L. et al., 2004, Ecoclimap2: A new approach at global and European scale for ecosystem mapping and associated surface parameters database using
SPOT/VEGETATION data – first results. Geoscience and Remote Sensing Symposium, 3, 2046-2049.
Kim, H., 2010, Role of rivers in the spatiotemporal variations of terrestrial hydrological circulation [D]. Tokyo: The University of Tokyo.
Koster, R. D. et al., 2002, Comparing the degree of land-atmosphere interaction in four atmospheric general circulation models. J. Hydrometeor, 3, 363-375.
Takata, K., S. Emori, and T. Watanabe, 2003, Development of the minimal advanced treatments of surface interaction and runoff. Global and Planetary Change, 38 (1-2), 209-222.

GC33A. Interactions and Feedbacks Between Land Surface Processes and Water Cycle Dynamics in Africa @ AGU, 2012

 For monthly variation, large range and relatively lower values of ΩET, ΩSoil, ΩT and ΩEcanopy in Benin(cleared forest) indicate that evapotranspiration (and its components) is more sensitive to ΩPcompared to that in Niger (semi-arid). Inversely, Niger shows steeper slope between ΩR and ΩP
which means in semi-arid region, uncertainty of simulated runoff is larger compared to cleared forest.
Cleared forest and semi-arid regions have opposite tendency in the uncertainty propagation for ET
ΩET – ΩP as presented in Fig. 5. Generally negative values in Benin illustrate that the uncertainty inprecipitation is propagated in an amplified way. However, the generally positive values in Niger showthat uncertainty translates relatively reduced in ET.
Negative values of ΩR - ΩP, both in Benin and Niger, show that uncertainty propagation for runoff isamplified through land surface simulation.
 For diurnal variation, ET and its components are not sensitive to ΩP except for ΩEcanopy in Niger,
which indicates that simulation of canopy evaporation has larger inconsistency in diurnal cycle.
However, compared to monthly variation, uncertainty propagation for ET in diurnal cycle is notconsiderably amplified.
Xiaogang HE hexg@rainbow.iis.u-tokyo.ac.jp