n - IWHW

Dept. Water, Soil & Atmosphere
University of Natural Resources
and Life Sciences Vienna
Introduction to Water and Soil (816.335)
Lecture Series in Water, Soil and Atmosphere (315.340)
Unit 3
Hubert Holzmann
1
Organisation
Schedule and Content
Date
Lecturer
Content
Basic level (introduction to water and soil)
4. 11.2011
Loiskandl
Ertl
Holzmann
H: Organisation and administration
H: Water balance and scale issues
Processes
Monitoring and Observation of water balance components
Interaction Soil / Vegetation / Atmosphere
Water quality and index class
11.11.
Loiskandl
Soil (Genesis, Type, Properties)
soil – plant interaction
evaporation / transpiration
18.11.
Holzmann
Runoff formation
Rainfall excess, methods
Model overview
25.11.
Holzmann
Runoff processes (drought, flood)
Case studies
Legislation
2.12.
Loiskandl
Argriculture and its impact to soil and soil water
Soil erosion
The courses of this template achieve the requirements for the course introduction to water and soil (816.335)
2
Organisation
Schedule and Content (continuation for 815.340)
Advanced level (Lecture series water, soil and atmosphere)
16.12.
Loiskandl
Soil remediation capacity
Water scarcity and water harvesting
13.1.2012
Holzmann
Application of rainfall runoff models (forecast, risk management)
Spatial analysis, GIS
20.1.2012
Ertl
Urban water and solute flow processes
27.1.2012
Ertl
Urban drainage
Sanitary engineering
Case studies and indication
The grading for the courses is based on written examinations, which will be held separately for 816.335 and
815340! Dates and locations will be announced in the course hompage (BokuOnline).
3
Outcome of Unit 1
Outcomes
Gaining basic knowledge in
• Formation of surface runoff
• Concepts of runoff modelling
- black box
- conceptual
- physically based
• Measurement techniques (case studies)
• Application of hydrological models
- flood forecasting
- climate change impact analysis
Hydrological Processes
Flow Routes
and processes
5
Runoff Formation
INF = R:
- Initial phase of rainfall event
- mean to high conductivity
- high rate of subsurface drainage
Rainfall R
R  INF  Q:
Temporal sequence
of infiltration and
surface runoff
Zeitliche Abfolge
von Infiltration und
Oberflächenabfluss
- mean phase of rainfall event
- mean conductivity
- mean rate of subsurface drainage
R  Q  INF:
- Final phase of rainfall event
- mean to low conductivity
- mean rate of subsurface drainage
R=Q:
Surface Runoff Q
- Final phase of extreme rainfall event
- low conductivity or impermeable
- restricted subsurface drainage
- saturation of soils
Hillslope
Infiltration INF
6
Runoff Formation
Schematic of Rainfall Excess Components. (From Environmental Ecology)
7
Runoff Formation
The process theory of runoff formation is as follows:
Raindrops fall over the area and flow along hillslopes to the nearest stream and
further towards the sea. For process modelling it is necessary to
a)
calculate the travel of a water drop from the hillslope to the stream and
b)
calculate the propagation of the water drop inside the stream.
A water drop that falls close to the stream reaches the stream very fast but for a
waterdrop that falls close to the water divide, it may take years to reach the
stream.
Thus to gain increased process knowledge field experiment at the hillslope scale
are conducted.
Saturation in zones of convergent topography
Saturation Areas
Extend of the stream network during a dry period (b, d) and during a rainfall event (c, e).
(from Maidment, 1992 – Handbook of Hydrology)
Discharge (m3/s)
Rainfall /
Excess (mm/Δt)
Runoff Event
Start of Rainfall
Time
Time Discretisation
Start and end of Direct Runoff
Hydrograph
Direct Runoff
Baseflow
Intensity of Areal Precipitation
Loss Rate
Rainfall Excess
Volume of direct runoff
Volume of rainfall excess
Basin area
Only a part of the areal rainfall contributes
to direct runoff!
Key Question:
Separation of rainfall excess and its temporal
evolution
Runoff Coefficient:
Ratio of runoff depth to precipitation depth.
Time
Niederschlags-Abfluss Modelle /
Rainfall Runoff Models (resp. Hydrological Models)
Festlegung der Modellstruktur
Design of Model structure
Kriterien / Criteria
• Ziele und Anwendungen / Objectives and Application
(zeitliche u. räumliche Gliederung /
temporal and spatial discretisation)
• Charakteristik des hydrologischen Systems
(dominierende Prozesse, Reaktionszeit /
dominant processes, runoff response time)
• Verfügbarkeit der Daten / Data availability
(Zeitreihen, Gebietsparameter /
Time series, spatial parameters)
Klassifizierung von N-A Modellen:
Classification of Rainfall Runoff Models
Prozessorientiert
process oriented
Process
Flächenaggregiert
(lumped)
deterministic
Raum- und zeitorientiert
space and time oriented
Verteilt
(distributed)
stochastic
Raum-/Zeitmaßstab
space and time scale
Raum / space
Verteilt / Distributed
Kleine EZG
Small basins
Hybride (gemischt)
Zeit / Time
Ereignisbezogen
event based
Mittlere EZG
mean basins
Kontinuierlich
continuous
Grosse EZG
Large basins
Klassifizierung von N-A Modellen – continuation
Classification of Rainfall Runoff Models
Nach Modelltyp:
Model type
Lösungsverfahren / solution technique
numerical
Finite
Differences
analogue
analytical
statistical
Finite
Element
Differential
equations
e.g. Regression
Nach Modellkonzept:
Concept
Model concept
Black Box
conceptual
Physically
based
statistical
Definitions:
Black Box Models
Black box models are based on transfer functions which relate inputs with outputs. These models, as the
name suggests, generally do not have any physical basis.
Conceptual Models
Conceptual models occupy an intermediate position between the fully physically- based approach and
empirical black box analysis. Such models are formulated on the basis of a relatively small number of
components, each of which is a simplified representation of one process element in the system being
modelled.
Physically based models:
The physically based models are based on our understanding of the physics of the hydrological processes
which control the catchment response and use physically based equations to describe these
processes. Also, these models are spatially distributed since the equations from which they are formed
generally involve one or more space coordinates. This implies that these models can be used for
forecasting the spatial as well temporal pattern of more than one hydrological variable. Such models
require much of computational time and also require advance computers as well as a broad data base.
From their physical basis such models can simulate the complete runoff regime, providing multiple outputs
(e.g. river discharge, phreatic surface level and evaporation loss) while black box models can offer only
one output. In these models transfer of mass, momentum and energy are calculated directly from the
governing partial differential equations which are solved using numerical methods. As the input data and
computational requirements are enormous, the use of these models for real-time forecasting has not
reached the `production stage' so far, particularly for data availability situations prevalent in developing
countries.
Infiltration
… Hydrologically, the infiltration process separates rain
into two parts. One part stored within the soil supplies
water to the roots of vegetation and recharges
groundwater. The other part which does not penetrate the
soil surface is responsible for surface runoff. Infiltration is
therefore a pivotal point within the hydrological cycle.
From (Kutilek & Nielsen (1994), Soil Hydrology)
Infiltration / Excess
Constant Conditions
(Ks saturated hydr.
conductivity)
The shape of the infiltration curve is a
function of soil physical properties
(hydraul. Conduvtivity, drainage capacity,
soil depth, vegetation cover, …)
Scheme of Rainfall Excess Components. (From Environmental Ecology)
Loss (Runoff Excess) computation
Empirical concepts
• Index Models
Constant Loss
Constant Runoff Coefficient
• Horton Model
• Time varying coefficient
• Varying coefficient (due to accumulated rain)
• Soil Storage Excess
Rainfall Excess
Neff = Na – y
Neff ... Rainfall Excess
Na ... Areal Rainfall
y … Constant Loss
Ia … Initial Abstration
6
Neff
Rain (mm)
Constant Loss:
4
- Easy to use
y
- 1 Parameter
model
- Not balanced with
natural conditions
2
0
1
Neff = Na * RC
RC ... Runoff Coefficient (0 – 1)
3
4
5
6
4
5
6
4
5
6
time
6
Rain (mm)
Constant Runoff Coefficient:
2
4
2
0
1
2
3
time
Neff = N - fp
where fp = fc + (fo – fc)exp(-*t)
- More realistic
- 3 Parameter model
6
Rain (mm)
Horton Model:
4
2
0
1
2
3
Rainfall Excess
0.8
0.6
0.4
0.2
0.0
Niederschlag
Rain (cm)(mm)
1.0
Verlustrate
Effektivniederschlag
Loss Rateund
and
Rainfall Excess
0
10
20
30
40
50
Zeit (h)
0.8
0.4
0.6
variabler,linearer Abflussbeiwert
lognormalverteilter Abflussbeiwert
0.2
Saturated RC
Initial RC
0.0
Abflussbeiwert
coefficient
Runoff
1.0
Variable
Coefficient
Variable Runoff
Abflussbeiwerte
0
5
10
Akkum.
Niederschlag
Accumul.
Rain(mm)
(cm)
Initial Abstraction
15
20
Applied Model Types
Antecedent Rain Index
ARIi 
t n
1
t n
a
i
  ( a i  Pi )
(1)
i t
i t
were
i
a
P
n
… Time index (in days)
… coefficient (=0.88)
… Precipitation (plus snowmelt
… optional) in mm/d
… memory length in days (=28)
2 Parameters
n ... Memeory length
a ... Recession coefficient
P2
t-j
t
P1
t-i
time
Direct Runoff Estimation
API – Storage Concept
for flood peak estimation
• Peak runoff depends on soil-moisture in the catchment
• Characterise moisture in the catchment by an index API
(Antecedent Precipitation Index)
• Relationship between API and runoff
• Rainfall losses were considered by a linear storage
• Aim: Good correlation between API and runoff
Qcomp ,i  36.44  47.80  ARI i , RAIN  SNOWMELT
Direct Runoff Estimation
API – Storage Concept
Peff ,t  Pt  ( S max  S act ,t )
S act ,t  S act ,t 1 * e
1
Sval
API t  ( API t 1  Peff ,t ) * e
3 Parameter model
Qcomp ,i  36.44  47.80  ARI i , RAIN  SNOWMELT
P
Peff
Smax
Sval
Sact
akvri
dt
API
i
1
24*dt *akvri
precipitation
effective precipitation
maximum storage capacity
retention factor
actual storage content
API coefficient
factor for temporal resolution
(1 for hourly calculations)
Antecedent Precipitation Index
timestep
Direct Runoff Estimation
API – Storage Concept
26.05.1999
26.05.1999
Q
26.05.1999
API
 36.44  47.80  ARI
06.07.1999
17.08.1999
Date
28.09.1999
28.09.1999
0
2
4
6
8
10
12
14
16
18
20
P [mm]
API
0
2
4
6
8
10
12
14
16
Precipitation
18
20
Discharge
P [mm]
API
180
170
160
150
140
130
120
110
100
90
80
70
17.08.1999
28.09.1999
60
Date
50
comp , i
i , RAIN  SNOWMELT
06.07.1999
17.08.1999
28.09.1999
40
30
Date
20
06.07.1999
17.08.1999
10
Date
0
0
2
4
6
8
10
12
14
16
Precipitation
18
20
Discharge
P [mm]
180
170
160
150
140
130
120
110
100
90
80
70
60
50
40
06.07.1999
30
20
10
0
Q [m³/s] / API [mm]
26.05.1999
180
170
160
150
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
0
2
4
170 - Hofstetten
6
8
Precipitation
10
Catchment 173 - Siegersdorf
12
Discharge
14
API
16
Precipitation
18
Discharge
Catchment 173 - Siegersdorf
20
P [mm]
Catchment
Q [m³/s] / API [mm]
180
170
160
150
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
Q [m³/s] / API [mm]
Q [m³/s] / API [mm]
Catchment 170 - Hofstetten
Unit Hydrograph Concept
Runoff Transformation
The Unit Hydrograph Method transforms the Rainfall Excess into a
Direct Runoff component at the stream gauge site. The Unit
Hydrograph is a runoff response function of the unit rainfall excess.
The volume of the areal unit rainfall corresponds with the runoff
volume of the unit hydrograph.
Methods
Unit Hydrograph Model
Principles of UH-Method
- Linearity
- Superposition
- Time invariance
Black Box Model
Methods
Triangular Unit Hydrograph
Effective
Precipitation
1 mm
Transformation Time tc
qmax
Discharge
m3/s
tc
(n  1)tc  qmax
 area  rain
2
2000  area
qmax 
3600  (n  1)tc
n . tc
1 mm = 1 l/m2 = 106 l/km2 = 1000 m3/km2
Unit Hydrograph
Example of triangular UH application with varying runoff coefficient
Parameter Estimation
The Loss parameters are generally estimated by model
calibration (comparison of simulated and observed event
data)
- Lack of physical interpretation
- Different event types (classification required)
- Limited transferrability to ungauged basins
- Difficult estimation of initial state conditions
Continuous Models
Model calibration
Model / parameter calibration
Calibration is the process of modifying the input or model
parameters until the output from the model matches an
observed set of data.
Model calibration
The goodness of fit is defined by the Objective Function. It is defined with regard to the specific
requirements and aims of the model application.
Each parameter set leads to a specific value of the
objective function. It can be possible, that different
sets can lead to similar results (Equifinality).
Z
X
X1 X2
X3
Objective Function
Zielkriterien
(Objective Function)
Angestrebt wird eine möglichst genaue
Anpassung der Modellberechnungen an die
Beobachtungsgrößen, wobei
anwendungsorientierte Vorgaben
berücksichtigt werden können.
Physically based concepts
Pros:
- Good spatial and temporal
resolution
- physically based
Cons:
- High data demand
- Spatial data required
- Overparameterisation
Schematic of MIKE SHE (modified from Refsgaard and Knudsen, 1996).
Soil Water Model
SVAT: Soil – Vegetation - Atmosphere
Soil parameter
state condition
boundary condition
precipitation
Surface
ETpot
fluxes
state condition t+1
ETact
ponding
Root depth
Capillary rise
Gravel layer
Groundwater level
Scheme of soil water balance model BOWA
recharge
Conceptual Models
Linearer Speicher / Linear Storage
Conceptual Models
Modell 1:
Modell 2:
Linearer Einzelspeicher
Nichtlinearer Einzelspeicher
QO
QO
Modell 1:
Single linear storage (with soil retention)
(Surface Flow)
QI
Modell 2:
Nonlinear Storage
(Surface Flow and Interflow)
Modell 3:
Modell 4:
Nichtlinearer Einzelspeicher
Mit Infiltrationsmodul
Mehrfachspeicher mit Bodenrückhalt und Grundwasser
QO
Modell 3:
Nonlinear Storage with Infiltration Module
(Surface Flow and Interflow)
QI
QI
QO ... Oberflächenabfluss
QI ... Zwischenabfluss
QG ... Basisabfluss
Model 4:
Multiple Storage with Infiltration Module
(Surface Flow, Interflow and Baseflow)
QG
Conceptual RR-Model
Evapotranspiration
Rainfall
Direct Runoff (Melt)
Surface Storage
SurfacRunoff
Runoff f(bw1, h1, k1)
Quick
PV
Mobile Soil Water
h1
bw1
Plant Available Soil Water
Interflow
FC
f(bw1, h2, k2)
h2
Stress
WP
Residual Soil Water
Percolation
bw2
f(bw1, h2, k3)
Baseflow
f(bw2, k4)
Runoff Model
Conceptual Rainfall Runoff Model
- Lumped Model
- Consideration of quick (surface) flow, interflow and base flow.
- Used for continuous rr-modeling and soil moisture accounting
- Applied for areas between 100 – 10 000 km2
- Implemented in an integrated runoff forecast system
Soil Depth Impact
0
20
10
30
40
10
20
30
20
0
Abfluss [m3/s]
10
0
Akt. Verdunstung
40
/ Total runoff
Abfluss gesam t
Oberflaechen(naher) Abfluss/ Surface runoff
/ Interflow
Interflow
/ Baseflow
Basisabfluss
Aktuelle Verdunstung
/ actual ET
Niederschlag
/ Precipitation
0
10
20
30
Zeit [d]
40
50
60
Niederschlag / Schmelze [mm/d]
50
Shallow Soil Boden
Seichtgruendiger
Soil Depth Impact
0
20
10
30
40
10
20
30
20
0
Abfluss [m3/s]
10
0
Akt. Verdunstung
40
/ Total runoff
Abfluss gesam t
Oberflaechen(naher) Abfluss/ Surface runoff
/ Interflow
Interflow
/ Baseflow
Basisabfluss
Aktuelle Verdunstung
/ actual ET
Niederschlag
/ Precipitation
0
10
20
30
Zeit [d]
40
50
60
Niederschlag / Schmelze [mm/d]
50
Deep Soil Boden
Tiefgruendiger
Temporal Scale Effect
5
Precipitation
3
2
0
1
Precip. (mm)
4
hourly rainfall
daily rainfall
0
50
100
150
Time (h)
2
4
hourly runoff
daily runoff
0
Spec. Discharge (mm)
6
Total Hillslope Runoff
0
50
100
Time (h)
150
Conclusions
•
Runoff formation is an areal heterogeneous process
•
It is affected by soil (physical) properties, land use and
cover, slope, etc.
•
The detailed physical processes of infiltration are sparseley
known for the catchment scale.
•
Therefore a conceptualisation (model) substitutes the real
system processes.
•
Areal classification (distribution) of Hydrological Response
Units (HRU) improves the physical meaning and the
parameterisation.
•
Land use change may have an (local) impact to the system.
Outcomes Unit 3
You should be able to answer the following questions:
•
•
•
•
•
•
•
•
•
•
•
•
•
•
What are Rainfall Runoff Models and what are they used for?
Which runoff component contributes more significantly to the streamflow during flood event?
(1) Subsurface Interflow or (2) Saturation Overland Flow – Comment your answer!
What is the runoff coefficient?
Describe the terms Areal Rainfall, Rainfall Excess, Baseflow, Direct Runoff!
Describe at least two methods for estimating the runoff excess!
What describes the Antecedent Rain (Precipitation) Index API?
Rainfall will form higher runoff peaks under (1) dry or (2) moist antecedent rain conditions? Comment
your answer!
What is the Unit Hydrograph?
You have an area of 5 km2 and a rainfall event with 16 mm rain depth. The runoff coefficient of the basin
is 25%. How big is the direct runoff volume of this event in m3.
What is the aim of parameter (model) calibration?
What is an objective function with respect to model calibration?
Give one example of an objective function for model calibration (fitting of hydrographs)
How can Rainfall Runoff Models be classified?
Which soils allow higher peak runoff: (1) shallow or (2) deep soils? Comment your answer!
Specialisation courses
Follow up courses
•
•
•
•
•
Possible impact of climate change on water resources (816.342)
Flood forecasting and flood protection (816.325)
Application of GIS in Hydrology and Water Management(816.323)
Seminary surface hydrology (816.305)
Integrated flood risk management (816.336)