Title: Modeling land use effects in Hydrology: Nam Ken
1Modeling land use effects in Hydrology Nam Ken
Mae Sa
2Past and new objective
- BEFORE
- Apply DHSVM to basins
- How land use change affects hydrology?
- NOW
- Study how land use change affects hydrology, but
different approach - Understanding vegetation dynamics
- Simplifying parameters, based with observations
- Semi-distributed hydrologic modeling
- Based on hillslope partitioning of the landscape
as hydrologic units
3Outline
- Recent study on Rubber root water uptake
(Guardiola et al. 2008) - Semi-distributed Modeling
- New approach based on Hillslopes discretization
- Bousinesq equation
- Hydrologic Units
- Future work
4How land use change affects hydrology?
Distributed Model
Root-zone loss function
Fractional Coverage Hemi Fraction Coverage Truck
Space Aerodynamic Attenuation Clumping
Factor LAI Leaf Angle A B Scattering
Parameters Mass Release Drip Ratio Height Maximum
and Minimum Resistance Moisture Threshold Number
of Root Zones Root Zone Depth Overstory /
Understory Root fraction Overstory/Understory LAI
and Albedo
Soil Vegetation parameters
5Setting Rubber extension in China
Native in the Amazon (10S 10N) T 24 28oC
P 1500 2000 mm Extended to Non-native
environments Marginal growth conditions
Rubber 8 ? 30 Forest 36 ? 24 Grassland 26
? 14
6Experimental basin
INSTRUMENTS - STATIONS Tea Rubber Radiation,
Wind speed, Temp, RH, P,
Soil moisture Grassland Forest Soil moisture
and P
Monsoon climate (P 1100 to 1700 mm) - Rainy
season May to October - Dry season November to
April
Forest /2ary Forest 53 Paddy
6 Grassland/Bush 2 Swidden 23 Rubber 16
7Experimental basin
Soil Characteristics Undisturbed soil samples
(SM desorption curves) In situ Ksat
measurements (Borehole permeameter)
8Root-zone water balance
9Results Discussion SM trends analysis
Evaporation Fraction -
?(?)
2005 Negligible precipitation
Emax Priestley-Taylor
10Results Discussion SM trends analysis
E ? at the surface E from deeper layers
?
E ? at the surface E constant deeper layers
Emax Priestley-Taylor
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12Why Root-water uptake during shedding?
NOV DEC JAN FEB
MAR APR MAY
13Spring flushing paradox
What triggers shedding/flushing?
- Leaf flushing during the dry season the paradox
of Asian monsoon forest - New leaves during hottest driest season
- Trees rely on subsurface water
- Climate is not the primary control of the
phenology? Increase in DAY LENGTH
Leaf flushing during the dry season the paradox
of Asian monsoon forest
- Leaf flushing during the dry season the paradox
of Asian monsoon forest - New leaves during hottest driest season
- Trees rely on subsurface water
- Climate is not the primary control of the
phenology? Increase in DAY LENGTH
or INSOLATION
Is rubber a spring flushing tree?
Elliott et al. (2006) Global Ecology and
Biogeography 15, 248-257
14Results Discussion
15Results Discussion SM trends analysis
16Conclusions
- Data analysis approach
- RZ loss function applies to native vegetation.
- Non-native, loss function depends also on
day-length - Reduction of water storage observed
- Increase in water demand during water deficit
periods - Now the intention is to translate this into a
model
17Semi-Distributed Modeling
- Using hillslopes as basic building blocks for
catchment modeling. - Subsurface flow controlled by their geometry and
characteristics such as depth, porosity, Ksat - Each hillslope has a homogeneous characteristics,
i.e. vegetation type, soil type, and energy and
water inputs. (Semi-Distributed model)
18Semi-Distributed Modeling
Divided in 887 hillslopes
Divided in 815 hillslopes
19Semi-Distributed Modeling
Boussinesq equation
20Geomorphometric analysis
Slope 12o
lt 12o is the gradient required for saturation
to occur
Trucker Bras (1998) Water Resources Research
34(10)2751-2764
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22Semi-Distributed Modeling Namken
23Semi-Distributed Modeling Mae Sa
24Semi-Distributed Modeling SHU
- Different Root zone models on top of hsB
- Pervious
- Rubber
- Forest
- Grassland / crops
- Rice Paddies
- Impervious
- Roads
- Green Houses
ROUTING TO THE GAUGE
25Semi-Distributed Modeling SHU
Pervious Land Uses forest, crops, grassland,
rubber
Transpiration f(?, day-length, temp)
Canopy storage
Soil evap.
Runoff
Infiltration saturation excess (return flow)
L
K(?)
Percolation (N) INPUT FOR hsB
? gt ?fc ? Percolation (N) and/or Runoff
L and Transpiration depends on land use
26Simple Distributed Modeling SHU
27Semi-Distributed Modeling SHU
- Rice Paddies (6 in NK)
- Different vegetative phases
- Seedling flooding fields (15 days)
- Vegetative (different length variety)
- Reproductive (25 35 days) complete growth
- Ripening (25 - 35 days) (irrigation stop)
- Each phase
- Ponding depth
- Crop factor (depends of vegetation growth)
- There are 1 to 2 harvest/year (upper and lower
basin) - January and April/May, or only April/May
28Semi-Distributed Modeling SHU
- Flooded P(t) No flooded P0
- When flooded? Saturated soils
- Starting day random
- P(t) Rainfall ? determines irrigation
- Darcy law to calculate percolation
- When dry ? Unsaturated soils
- K(?) using Van Genuchten
- Darcy law to calculate percolation
Irrigation
Precipitation
P(t)
K(?)1
L1
L2
K(?)2
K(?)1 ltlt K(?)2
Percolation (N)
10 days average Kc 1 1/0.87 1.02 1.08 1.06
1.17 1.24 1.28 1.35 1.32 1.29 1.15 0.92 Ponding
200 150 125 100 100 100 100 100 100 100 75 50
0 (mm)
29Semi-Distributed Modeling SHU
- Impervious
- Very low Ksat, for roads and GH roofs
- Mainly Infiltration excess
- Green Houses ? deeper study about water uses
OL evaporation
L
K(?)
30Future work
- Finish Rubber root water uptake paper
- Couple the root zone models with hsB
- Route the outflow to compute streamflow
- Paper about Semi-distributed hydrological
modeling in SEA using hsB and HSU. Impacts on
land use change
31Rice Paddy
Modified Penman Monteith
Priestler a 1.26
32Agriculture
33Simple Distributed Modeling Hillslopes
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36Soil moisture trends analysis
2006 80 mm precipitation
37Discussion
38- Rubber drier with depth
- dampening with depth
- delay in response
39The effects of introducing non-native vegetation
on hydrological partitioning in a tropical
catchment