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Watershed Functions and Runoff Processes

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Sprinkling infiltrometer in Western Amazon rainforest: infiltration capacity ~ 180 mm/hr ... Central Amazon (Soils on Tertiary sedimentary rocks) W. Amazon, Rondonia ... – PowerPoint PPT presentation

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Title: Watershed Functions and Runoff Processes


1
Watershed Functions and Runoff Processes
  • Tom Dunne
  • Winter 2008

2
A word on the use of analytical and predictive
models in Watershed Analysis
3
Use and interpretation of analytical models of
watershed behavior.
A
Conceptual model of processes as affected by
landscape characteristics. Based on observations
and physical reasoning.
4
Use and interpretation of analytical models of
watershed behavior.
A
B
Conceptual model of processes as affected by
landscape characteristics. Based on observations
and physical reasoning.
Analytical theory of processes, their controls
and inter-relations, expressed mathematically. Too
complex to parameterize for predictions/explanati
ons.
5
Use and interpretation of analytical models of
watershed behavior.
A
B
Conceptual model of processes as affected by
landscape characteristics. Based on observations
and physical reasoning.
Analytical theory of processes, their controls
and inter-relations, expressed mathematically. Too
complex to parameterize for predictions/explanati
ons.
Parameterize? Whazzat????
6
Limitations on what can these models tell us
  • They express not only our best understanding but
    also the uncertainties
  • (i) in our understanding of processes
  • (ii) in our knowledge of critical values (e.g.
    albedo of clouds)
  • (iii) in our capacity for computation.
  • E.g.
  • They require simplifications of system
    descriptions (spatial resolution currently 2-5
    degrees 6-20 vertical levels time steps 30
    min.)
  • Necessary to parameterize some processes (e.g.
    cloud formation and effects on radiation balance
    ET runoff).
  • Parameterize means to represent processes that
    are complex on small time and spatial scales by
    means of simple equations containing coefficients
    (parameters) that express average behavior over
    some time period and spatial scale. e.g. Kcan
    R1 aP1 Q Q0e-kt

ESM 203 Lecture 19
7
Use and interpretation of analytical models of
watershed behavior.
A
B
C
Conceptual model of processes as affected by
landscape characteristics. Based on observations
and physical reasoning.
Analytical theory of processes, their controls
and inter-relations, expressed mathematically. Too
complex to parameterize for predictions/explanati
ons.
Computational model of effects of processes and
controls. Used for planning, design or
decision-making. Heavily parameterized to the
point where representation of observable physical
relationships is unclear, and cause-effect
subject to a range of interpretation.
8
Use and interpretation of analytical models of
watershed behavior.How should we insert values
in (or interpret results of) C in the light of
what we understand in A and express in B?
A
B
C
Conceptual model of processes as affected by
landscape characteristics. Based on observations
and physical reasoning.
Analytical theory of processes, their controls
and inter-relations, expressed mathematically. Too
complex to parameterize for predictions/explanati
ons.
Computational model of effects of processes and
controls. Used for design or decision-making. Heav
ily parameterized to the point where
representation of observable physical
relationships is unclear, and cause-effect
subject to a range of interpretation.
9
Use and interpretation of analytical models of
watershed behavior. How should we insert values
in (or interpret results of) C in the light of
what we understand in A and express in B?
A
B
C
Conceptual model of processes as affected by
landscape characteristics. Based on observations
and physical reasoning.
Analytical theory of processes, their controls
and inter-relations, expressed mathematically. Too
complex to parameterize for predictions/explanati
ons.
Computational model of effects of processes and
controls. Used for design or decision-making. Heav
ily parameterized to the point where
representation of observable physical
relationships is unclear, and cause-effect
subject to a range of interpretation.
Well, you see, judge, heres how the result from
this calculation relates to what is going on here
in the watershed when X changes to Y.
10
Watershed functions are driven by runoff
processes, which vary geographicallyIn this
context runoff means the processes by which
water travels to a stream channel
11
Watershed functions
  • Collection of water and transported materials
  • Storage (attenuates response to temporally
    discrete inputs). Floodplain storage of water,
    sediment, pollutants
  • Conveyance (attenuates response to spatially
    discrete inputs)
  • Discharge 
  • Transport
  • Assembly of sediments into landforms that are
    exploited as habitat for plants or animals

12
Watershed functions
  • Runoff processes supply water and transported
    materials into channels
  • The channels and valley floors temporarily store
    these substances as they move downvalley
  • This channel and valley-floor storage acts like a
    reservoir to dampen the response making the
    waves or pulses of input later and more diffuse

13
Watershed functions at differing basin sizes
  • As the size of the watershed increases, the
    storage and damping of the hillslope response by
    the channel/valley floor increases
  • In small watersheds, the hillslope processes
    dominate the hydrological response
  • Therefore the condition of the watershed surface
    controls hydrological response
  • Watershed surface affected by natural and
    anthropogenic processes (i.e. land management)
  • In large watersheds, hydrological response is
    dominated by valley-floor storage processes

14
TerminologyStreamflow (runoff) storm
runoff baseflowor quickflow delayed
flow(from ESM 203)
15
Runoff in the Water Balance ESM 203
Rnet
E
Advection of sensible (H) and latent heat (L)
P
  • ?volume fraction of water
  • V(t) volume of groundwater storage resulting
    from balance between drainage from soil and
    drainage to rivers
  • Ddepth of root zone

Quickflow R
Soil SM(t)D?(t)
Recharge when SM(t)gtSMmax
Ground water V(t)
Delayed flow R
16
Vegetation change and water yield
  • Thicker, taller, darker vegetation favors canopy
    interception (of rain or snow) and
    evapotranspiration ESM 203 notes
  • R P E
  • Inadvertent manipulation through clearing, fire,
    or reforestation diminishes E and therefore
    increases R
  • Planned rotational clearing for water yield
    management

17
Vegetation change and water yield results of
paired watershed experiments
18
Tree removal and increases in water yield (?R) in
a Douglas fir forest in Oregon results of paired
watershed experiments Harr, 1983
  • ?Rt (mm) 308 0.09Pt (mm) 18t (yr)
  • E.g. for Pt annual precipitation of 3000 mm/yr
  • 560 mm extra after year 1
  • 398 after 10 year 10
  • 218 mm after year 20

19
Vegetation change and water yield in Eastern
hardwood forests results of paired watershed
experiments Douglas, 1983
20
Runoff pathways determine the partitioning of
total R into overland and subsurface flow . and
that makes all the difference to the functioning
of a watershed !
21
There is a maximum rate at which a land surface
in a given condition can accept water.
  • This maximum rate is called the infiltration
    capacity.
  • Infiltration capacity is the maximum rate at
    which a soil can absorb rainfall
  • It is the key control on partitioning of water
    into surface and subsurface flow paths
  • Infiltration capacity declines exponentially
    through a rainstorm

22
Infiltration capacity (f) declines exponentially
through a rainstorm as time or soil moisture
content of soil surface (?) increase
23
Horton overland flow is generated when rainfall
intensity exceeds the infiltration capacity of
the soil
24
Analytical theory of infiltration rate (f)
Green-Ampt equation
  • Darcys Law ESM 203 notes

H elevation head pressure head Infiltration
is driven by gradient of elevation (which is
constant) and gradient of pressure (? p/ ? z)
between the surface and the wetting front (which
is decreasing as wetting front penetrates soil
and therefore ?z increases)
25
Green-Ampt derivation
26
Green-Ampt derivation
Rainfall intensity, I

?z(t) depth of wetting front at time t F(t)
accumulated amount of infiltration up to time
t Ts porosity (saturated water content) of
soil Ti initial water content of soil
27
Green-Ampt derivation
28
The infiltration capacity decreases through time
as the wetting front penetrates the soil,
decreasing the pressure gradient between the
surface and the wetting front
29
Controls on infiltration capacity(mainly
effective hydraulic conductivity Ksat)
  • Population of pore sizes (micro to macro) and
    therefore texture, structure, biotic activity,
    organic content, etc. Blocking of pores by frost
  • Vegetation cover/litter/soil macrofauna/macropores
    . Root zone collapse (burning trampling
    traffic)
  • Surface crusting (especially in silty soils).
    Lemon groves in Goleta
    Nabatean/Israeli runoff farming by removing
    stones.
  • Asphalt
  • Antecedent moisture of the soil (previous
    rainfall pattern) affects rate of convergence of
    f on K sat.

30
Horton overland flow
31
Horton overland flow
32
Augmentation of irregular sheet of overland flow
r
f
33
Sprinkling infiltrometer, Sedgwick
ReserveInfiltration capacity rainfall
intensity runoff rate
34
Sprinkling infiltrometer in Western Amazon
rainforest infiltration capacity 180 mm/hr
35
Sprinkling infiltrometer in 10-yr old pasture,
Central Amazonia
36
Infiltration capacity of soil (mm/hr)
Forest Pasture
  • Central Amazon
  • (Soils on Tertiary sedimentary rocks)
  • W. Amazon, Rondonia
  • (Soils on Precambrian rocks)
  • 143 98
  • 156 105
  • 181 30
  • 146 41
  • 13
  • 25

37
Measurement or estimation of infiltration
capacity
  • Monitoring of runoff and rainfall rates on plots
  • Cylinder infiltrometer
  • Sprinkling infiltrometer
  • Visual observations of water accumulation under
    measured rainfall intensity
  • Soil-based estimates from handbooks and soil
    survey reports, later calibrated against computed
    and measured runoff.
  • Storm-averaged value or ? index (Total storm
    rainfall minus total storm runoff divided by
    duration of rainfall for small basins)

38
Storm-averaged inf. Cap. (? index) is the volume
(depth) of rainfall the volume (depth) of
runoff divided by duration of shaded area
39
Low-infiltration landscape sparse vegetation,
clay-rich soil
Horton overland flow environments
40
Sparsely vegetated, low-infiltration landscape
41
Infiltration capacity lowered from gt100 mm/hr
(forest floor) to 1 mm/hr by eruption of silty
volcanic ash, Mt St Helens 1980
42
Clearing of olive tree woodland and heavy
grazing, following population concentration in
civil war in N. Kenya reduces infiltration
capacity and hillslope roughness
43
Reducing vegetation cover reduces infiltration
capacity and hillslope roughness
44
Bush clearing for agriculture, E. Kenya
45
Toxic, unvegetated mine spoils have low
infiltration capacities
46
Logging road, W. Washington Olympic Mts.
infiltration capacity 1 mm/hr
47
Construction sites have low infiltration
capacities, and short, steep slopes
48
Impervious, urban surfaces replacing forested
soils, Rio
49
Changes to infiltration capacity, surface
roughness and slope length increase and
accelerate overland flow in urban areas
50
Impervious cover is inversely proportional to lot
size
51
Runoff pathways
52
Steep, shallow, forested soil over volcanic
rocks, Japanese Alps generates shallow subsurface
flow (throughflow/interflow)
Island arc environment in wet climate
53
Buildup of saturation and pore pressure in Oregon
Coast Range
54
Deep, permeable volcanic ash, Aberdare Mts.,
eastern flank of Rift Valley, Kenya
Volcanic deposits on margins of a graben at humid
divergent plate margin
55
Deeply permeable, fractured sandstone, Mesa
Verde, Colorado
Sedimentary platform on a craton in subhumid
environment
56
Groundwater emerging from fractures in bedrock
produce channels by seepage erosion during
snowmelt, Vermont
57
Emerging groundwater in late melt season, Vermont
58
Ground water emerging in deep gullies in a
landscape with deep, permeable soils, S.E. Brazil
59
Interflow
60
Interflow, exfiltration and direct precipitation
generate saturation overland flow on parts of
landscapes
61
Saturation overland flow, Vermont
62
Seasonal variation of saturation overland flow on
low-permeability glacial till, Vermont
63
Saturation overland flow from spring snowmelt,
near Wenatchee, E. Washington
64
Macropore flowSubsurface flow through fractures
in swelling-clay soil, central California coast
65
Vertical fissures in soil on volcanic ash, N.
Tanzania
66
Gully produced by tunnel erosion after removal of
woodland and onset of heavy grazing, N. Tanzania
(approx 1960-1982)
67
Schematic summary of controls on runoff pathways
68
Significance of runoff processes (pathways) for
flooding, erosion and particulate transport
  • For understanding flood runoff (see later on
    modeling of watershed runoff).
  • For understanding erosion and contaminant sources
    and transport Therefore need for a spatially
    registered, field-based appreciation.

69
Significance of runoff processes for erosion and
transport
Broad Run, Pa
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