Improvement of vegetation parameters for the estimation of evapotranspiration in the Midwest United States

Transcription

1 Improvement of vegetation parameters for the estimation of evapotranspiration in the Midwest United States Dazhi Mao Agricultural & Biological Engineering Purdue University March 3, 7

2 Outline Introduction ET models Review of literature Current progress Things need to do

3 Introduction Vegetation controls the loss of water by evaporation and transpiration ET is the major component in water balance calculation Land-use change can alter Evapotranspiration (ET) patterns Hydrology models need adequate ET representation to make reasonable analysis

4 ET model Penman-Monteith model Jarvis-scheme stomata resistance parameterization r λe ( R G) + ρc D / r n p a + γ (1 + r / r ) a SiB, SSiB, BATS, k uz CLASS, MOSAIC, r NOAH-LSM DHSVM, l rs = and VIC all use LAIeff Jarvis-scheme model r = r f ( T ) f ( vpd) f ( PAR) f ( θ ) l = = ( ) ( ) ln zm d / zom ln zh d / zoh s min 1 3 s a

5 Current problems Little attention paid to ET estimation in the upper Midwestern U.S. region (agriculture dominated) DHSVM and VIC model share the same ET calculation scheme Need proper vegetation properties to improve model performance in agricultural regions Review of references Adjust and validate VIC model vegetation properties against time series, point and regional variability

6 Review of literature Table 1. Properties for vegetation roughness length and zero displacement height Vegetation type d z om Reference Bare ground.15m Jones (199) Grassland.13 h c.7 h c Kjelaard and Stockle (1) Cropland.13 h c. h c Jones (199) Deciduous forest.75 h c.7 h c Jarvis et al (197) Evergreen conifer forest.75 h c.7 h c Jarvis et al (197) h c vegetation height, d displacement height, z om roughness length

7 Review of literature Adjusted VIC vegetation parameters for this study Vegetation type r smin (s/m) d z om Evergreen needle leaf forest.7 h c.75 h c Deciduous broadleaf forest.7 h c.75 h c Woodland h c.7 h c Mixed wooded grassland h c.7 h c Shrubland h c.7 h c Grassland h c.7 h c Cropland 9.13 h c. h c h c vegetation height varies monthly except for forest, which is constant throughout the year. needleleaf conifer is set to be 1m, and deciduous broadleaf is set to be 1m.

8 Models Vegetation h c (m) d z om r smin (s/m) LAI (max) Evergreen needleleaf 17.5 h c.53 h c 15 CLM (Dai et al., 3) Deciduous boradleaf Wooded grassland Grassland.3..1 h c.7 h c.33 h c.5 h c.7 h c Cropland..373 h c Evergreen needleleaf h c.7 h c LSM (Bonan, 1995) Deciduous boradleaf Wooded grassland Grassland h c.5 h c.1 h c.7 h c.7 h c.7 h c Cropland.5.1 h c.7 h c Evergreen needleleaf 17.5 h c.7 h c BATS (Dickenson et al., 1993) Deciduous boradleaf Wooded grassland Grassland.3.. h c.5 h c.7 h c.7 h c.7 h c.7 h c Cropland..1 h c.7 h c 15 Evergreen needleleaf MOSAIC (Koster and Suarez, 199) Deciduous boradleaf Wooded grassland Grassland Cropland Evergreen needleleaf m 7.7 SiB (Dorman and Sellers, 199) Deciduous boradleaf Wooded grassland Grassland.3. 1.m.m.m Cropland.

9 Study region a. Evergreen needleleaf forest b. Deciduous broadleaf forest 15 W 1 W 95 W 9 W 5 W W N N 5 N 5 N N N N N N N N N N N c. Wooded grassland d. Prairie grassland 3 N 3 N 5 N 5 N 15 W 1 W 95 W 9 W 5 W W Analysis method Time series comparison Point comparison and other single vegetation watersheds(annual avg) Regional variations N N 15 W 1 W 95 W 9 W 5 W W 15 W 1 W 95 W 9 W 5 W W e.cropland 1 5 N N 15 W 1 W 95 W 9 W 5 W W Percent coverage (%)

10 PET (mm) 1 1 a.sutherland (.9,-95.5) b.kanawha (.9,-93.9) 1 1 Observed Time series data PET (mm) 1 1 c.nashua (.75,-9.5) d.castana (.,-95.) 1 1 AMERIFLUX data Mesonet, IA PET comparison (Laura s new VIC PET code) PET (mm) PET(mm) e.ames (.3,-93.) g. Crawfordsville (1.,-91.5) f.lewis (1.,-95.) h.chariton (1.,-93.3) /3/9 7/3/9 1/3/9 /3/9 7/3/9 1/3/99 Observed Simulated IA PET comparison (LAI from VEG Library)

11 1 a.crop, IL(1.,-.) f.deciduous forest, MI(.,-9.3) 1 H(W/m ) -1 1 J O J A J 5 b.crop, IL(.,-.9) O J A J O J A J O J A J 1 3 g.deciduous forest, MI(5.5,-.71) a.crop,ne (1.1,-9.) 1 d.prairie,ks (37.5,-9.) 1 g.forest,wi (5.91,-9.) -1 O J A J O J A J O J A J O J A A J O J A J O J A J O J A J O J A J O -1 1 b.crop,il (1.,-.) 1 e.prairie,il (1.,-.) 1 h.forest,mi (5.5,-.71) 1 c.crop, NE(1.1,-9.) h.deciduous forest, WI(5.91,-9.) J O J A J O J A J O J A J O J A d.prairie, IL (1.,-.) O J A J O J A J 5 e.prairie, KS(37.5,-9.) J A J O J A J O J A J i.forest, IN(39.3,-.1) JA JOJA JOJA JOJ AJOJ A JOJA JOJA JO Sensible Heat Simulated H(W/m ) 1 1 c.crop,il (.,-.9) 1 Observed H(W/m ) 1 1 f.forest,in (39.3,-.1) i.forest,mi (.,-9.3) 1-1 O J A J O J A J O J A J 1 3 Observed Sensible heat H(W/m ) Simulated

12 a.crop, IL (1.,-.) f.deciduous forest, MI(.,-9.3) 1 1 a.crop,ne (1.1,-9.) d.prairie,ks (37.5,-9.) g.forest,wi (5.91,-9.) J O J A J 5 O J A J O J A J O J A J 1 3 b.crop, IL (.,-.9) g.deciduous forest, MI(5.5,-.71) LE(W/m ) 1 O J A J O J A J O J A J O J A A J O J A J O J A J O J A J O J A J O 1 1 b.crop,il (1.,-.) 1 e.prairie,il (1.,-.) 1 h.forest,mi (5.5,-.71) 1 c.crop, NE(1.1,-9.) h.deciduous forest, WI(5.91,-9.) J O J A J O J A J O J A J O J A J A J O J A J O J A J c.crop,il (.,-.9) 1 f.forest,in (39.3,-.1) 1 i.forest,mi (.,-9.3) 1 d.prairie, IL (1.,-.) i.forest, IN(39.3,-.1) 1 Simulated LE(W/m ) O J A J O J A J 5 e.prairie,ks(37.5,-9.) JA JOJA JOJA JOJ AJOJ A JOJA JOJA JO Latent Heat 1 Observed LE(W/m ) 1 1 O J A J O J A J O J A J 1 3 Observed Latent heat LE(W/m ) Simulated

13 Problems PET under-prediction Prairie grassland (LE) under-prediction, but H over-prediction Deciduous forest over-prediction Cropland still a little over-prediction Still need to adjust the parameters Is it necessary to run with vegetation parameters from other models?

14 Things need to do Further testing of vegetation properties Finish watershed selection Finalize parameter ranges in the VIC model Statistical analysis Regional scale application of the adjusted vegetation parameters