UFZ -CENTRE FOR ENVIRONMENTAL RESEARCH LEIPZIG-HALLE

Sensitivity Analysis of the Land Surface Model Noah-MP for Different Output Fluxes in 12 Distinct CatchmentsJ. Mai 1, S. Thober 1, L. Samaniego 1, O. Branch 2, V. Wulfmeyer 2, M. Clark 3, S. Attinger 1, R. Kumar 1, and M. Cuntz 1

1 Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany, (juliane.mai@ufz.de) 2 University of Hohenheim Stuttgart, Germany, 3 National Center for Atmospheric Research NCAR Boulder, CO

1. Motivation

Land Surface Models (LSMs) use a plenitude of process descriptions to rep-resent the carbon, energy and water cycles. They are highly complex andcomputationally expensive. Practitioners, however, are often only interestedin specific parts of the model such as latent heat or surface runoff. In modelapplications like parameter estimation, the most important parameters arethen chosen by experience or expert knowledge. Hydrologists interested insurface runoff therefore choose mostly soil parameters while biogeochemistsfocus on carbon fluxes and hence on vegetation parameters. However, thismight neglect parameters that are important, for example, through stronginteractions with the parameters chosen. Additionally, supposedly unimpor-tant parameters are often fixed (i.e., hard-coded) during model development.However, these might be highly relevant during model calibration but remainnormally undetected.

Noah-MP is applied in 12MOPEX catchments. Metero-logical forcings are obtainedfrom NLDAS-2 and static datafrom LDAS. The spatial reso-lution is 0.125◦ and the simu-lation period is from 1979 to1999 with the first years asspin-up. Additional catchmentcharacteristics are given in thefollowing table.

100 ◦ W 95 ◦ W 90 ◦ W 85 ◦ W 80 ◦ W 75 ◦ W

30◦N

35◦N

40◦N

1

2

3

4

5

6

78

9

10

1112

Mean Mean Runoff Mean Mean Winter DominantNo Name Rainfall Runoff Coefficient Snow Temperature Land Cover

[mm] [mm] [-] [mm] [◦C] [-]1 Tygart Valley River 1198 735 0.61 1901 -0.3 Dec BdLf For2 French Broad 1413 800 0.56 902 3.6 Mixed Forest3 Monocacy River 1050 420 0.40 1737 1.3 Dec BdLf For4 Bluestone River 1036 416 0.40 798 2.0 Dec BdLf For5 Amite River 1563 609 0.38 25 11.2 Evg NdLf For6 East Fork White River 1013 377 0.37 679 -0.5 Grassland7 Rappahannock River 1037 377 0.36 733 3.0 Mixed Forest8 South Branch Pot. 1055 341 0.32 2582 0.3 Dec BdLf For9 English River 900 269 0.29 1776 -2.9 Grassland

10 Spring River 1075 298 0.27 377 3.0 Dec BdLf For11 San Marcos River 825 179 0.21 4 11.2 Grassland12 Guadalupe 763 116 0.15 7 10.1 Veg Mosaic

2. Study Area

The code was scanned for hard-coded values that are termed hidden parameters. 146 such numbers were found in all processes ofNoah-MP but independent of vegetation or soil type. These parameters are in addition to the 93 standard parameters of Noah-MP.38 standard and 80 hidden parameters remained in the analysis when the specific options of Noah-MP were chosen. Informativeparameters were screened for different model outputs[1]: surface runoff, underground runoff, latent heat, and transpiration. Sobol’sensitivity analyses were performed on the parameters identified. Per basin and output flux on average 1,300 model runs for thescreening and 75,000 model runs for the Sobol’ analysis were required. p1

p2

p3

3. Methods

1 2 3 4 5 6 7 8 9101112

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Radia

tion

Soil

Physi

o

Vegeta

tion

Str

uct

ure

Physi

olo

gy

Soil

Wate

r

Runoff

Input

Radia

tion

Tra

nsf

er

Physi

olo

gy

Snow

Wate

r

Snow

Energ

y

Soil

Energ

y

Late

nt

Heat

Catc

hm

ents

Surf

ace

Runoff

Catc

hm

ents

Standard Parameters Hidden Parameters

• Informative parameters depend more on model output thanon catchment characteristics

• On average 29% of in total 118 parameters are informative

• On average 38% of informative parameters are hidden

• Snow parameters are highlighted in surface runoff

• Parameters for vegetation, i.e. plant-soil interface, structureand physiology, are highlighted in latent heat

4. Parameter ScreeningRadiation

Physio.

Soil

StructureVegetation

Physiology

Soil

Water

Runoff

Input*

Radiation*

Transfer*

Physio.*

Water*

Snow

Energ

y*Snow

Energy*

Soil

0

0.8

2 6

101418222630343

8424

650

545862667074

7882

86

8S

outhB

ranchP

otomac

RadiationPhysio.

Soil

StructureVegetation

Physiology

Soil

Water

Runoff

Input*

Radiation*

Transfer*

Physio.*

Water*

Snow

Energ

y*Snow

Energy*

Soil

0

0.8

2 6

101418222630343

8424

650

545862667074

7882

86

12G

uadalupe

9E

nglishR

iver

RadiationPhysio.

Soil

StructureVegetation

Physiology

Soil

Water

Runoff

Input*

Radiation*

Transfer*

Physio.*

Water*

Snow

Energ

y*Snow

Energy*

Soil

0

0.8

2 6

101418222630343

8424

650

545862667074

7882

86

Underground Runoff

RadiationPhysio.

Soil

StructureVegetation

Physiology

Soil

Water

Runoff

Input*

Radiation*

Transfer*

Physio.*

Water*

Snow

Energ

y*Snow

Energy*

Soil

0

0.8

2 6

101418222630343

8424

650

545862667074

7882

86

Surface Runoff

RadiationPhysio.

Soil

StructureVegetation

Physiology

Soil

Water

Runoff

Input*

Radiation*

Transfer*

Physio.*

Water*

Snow

Energ

y*Snow

Energy*

Soil

0

0.4

2 6

101418222630343

8424

650

545862667074

7882

86

Latent Heat

RadiationPhysio.

Soil

StructureVegetation

Physiology

Soil

Water

Runoff

Input*

Radiation*

Transfer*

Physio.*

Water*

Snow

Energ

y*Snow

Energy*

Soil

0

0.1

2 6

101418222630343

8424

650

545862667074

7882

86

Transpiration

• Few informative parameters show highsensitivity

• Large number of snow parameters impor-tant in snowy catchments and these areall hidden

• Runoff in snowy catchments sensitive tohidden separation of rain and snow

• Saturated hydraulic conductivity very im-portant for runoff processes

• Influence of vegetation parameters de-pends on land cover type

• Sensitivities of underground runoff aremore similar to those of latent heat thanto those of surface runoff

• Sensitivities of latent heat dominated byevaporation processes

• Hidden parameter in formulation of sur-face resistance for evaporation is over-sensitive, similar to findings for CLM v3.5[2]

5. Sensitivity Analysis

[1] M. Cuntz, J. Mai et al., “Computationally inexpensive identificationof non-informative model parameters by sequential screening,” Jan2015, submitted to WRR.

[2] M. Gohler, J. Mai, and M. Cuntz, “Use of eigendecomposition in aparameter sensitivity analysis of the Community Land Model,” JGR,vol. 118, pp. 904–921, 2013.