Short Course on the use of drones in hydrology, EGU 2017

1. SC53| Early Career Scientists (ECS)| www.egu.eu/ecs
Use of UAV for Hydrological Monitoring
Wed, 26 Apr, 15:30–17:00 / Room -2.85
Salvatore Manfreda
University of Basilicata, Italy
salvatore.manfreda@unibas.it
http://www2.unibas.it/manfreda/
NhET & ESSI ECS

2. What can UAV do?

3. From Urs Treier

4. (Anderson & Gaston, 2013)

5. The Rules
Source: BI Intelligene – 2015 and ENAC 2017
USA ITALY
• The drafted rules are relatively restrictive — they allow only for commercial flights by licensed operators within visual range of the
aircraft (i.e., no remotely operated flights) and severely restrict where the flights can be conducted.
• The operator of the aircraft must be licensed as a drone pilot
by the FAA, and the credential must be renewed every two
years. The licensing process is not yet in place and is part of
what needs to be detailed for the rules to go into effect
• The UAV may not weigh more than 55 pounds (25 kilograms)
• The pilot must keep the UAV within visual line of sight at all
times during flight
• The UAV may not fly any faster than 100 miles per hour (161
kilometers per hour)
• Maximum altitude is only 500 feet (152 meters)
• Other government agencies will need to study the privacy
implications of the flights and propose a framework for
industry self-regulation
<25 kg
>25<150
UAV
60 kilometers per hour
150 meters altitude
and 500 meters
distance
• UAVs cannot be flown above any people not involved in the flight or near restricted airport or flight areas (this effectively rules out
UAV flights over urban or even sparsely populated areas)
• The flights must occur in daylight
• Professional use “Section 333” – exemptions
possible under authorization
60 months < 40 y
24 months < 50 y
12 months < 65 y
6 months > 65
Enac
APR SAPR - (remotely piloted aircraft systems)
On line section

6. TOP APPLICATIONS
Precision agriculture: management of crops to guarantee efficiency of
inputs like water and fertilizer and maximize productivity, quality, and
yield. It also involves the minimization of pests, unwanted flooding, and
disease.
Energy, mining, and utilities: resources management and research
requires monitoring over large territories, often in inaccessible areas.
Real estate, construction, and land development: need managing and
mapping large portion of land or collections of buildings.
Environmental monitoring: ecological state of ecosystems, plant stress,
water pollution, soil contamination, water contamination, monitoring
of water systems (rivers, lakes, dams etc.).

7. An example
in Agriculture
• Pathogen/pest/weed detection
• Crop modeling/yield estimation
• Nutrient management
• Crop water use/irrigation

8. Engines
MK3638
Li-Po Battery
8Amp 30C
Camera mount
servo-stabilized
carbon-fiber
UAVEurope ®
Example: multi-copter 6
engines equipped with a
thermal camera
Thermal camera
GOBI384 Xenics®!
Ubiquiwifi ,
on line wi-fi
data streaming
Propellers
(APC 12x3,8 inc)Frame
(carbon fiber Air-
Sci UAVEurope®)
Electronic
systems:
Mikrokopter®

9. Skywalker
HydroLAB Equipment
Phantom 3 pro
Portable radar
Uncooled LWIR Thermal ADC Snap Camera

14. Evolution of a
fungal pathogen
14 May 2014 (from Lyndon Estes, 2015)

15. Evolution of a
fungal pathogen
(from Lyndon Estes, 2015)25 July 2014

16. How to detect water
stress from an UAV?
From Xurxo Gago

17. Reference UAVs type Crop Thermal
Index
gs (R2) Potential
hydric (R2)
Baluja et al.
2012
Wing-span
fixed wing
Grapevine Tcanopy –
Tair
0.69 0.5
CWSI 0.68 0.5
IG 0.72 0.5
I3 0.5 0.42
Zarco-
Tejada et al.
2012
Wing-span
fixed wing
Citrus
sinensis Temperature 0.78 0.34
González-
Dugo et al.
2013 Wingspan
fixed-wing
Almond
Tc-Ta
0.67
Peach 0.92
Lemon 0.48
Orange 0.27
Apricot 0.64
Aerial thermography
for water stress detection

18. Aerial thermography
for water stress detection
(Berni et al., 2009)

19. Aerial thermography
for water stress detection
(González-Dugoetal.,2012)

20. Thermal indexes… an attempt to normalize the environment (Idso et al., 1980; Jones, 1999)
CWSI = T canopy – Twet / Tdry – Twet
IG = T dry – Tcanopy / Tcanopy – Twet
I3= T canopy – Twet / Tdry – Tcanopy
And the leaf energy balance:
𝑇" − 𝑇$ =
𝑟() 𝑟$* + 𝑟, 𝛾𝑅/0 − 𝑝𝑐3 𝑟() 𝐷
𝑝𝑐3 𝛾 𝑟$* + 𝑟, + 𝑠𝑟()
6
𝑟, =
−𝑝𝑐3 𝑟() 𝑠 𝑇" − 𝑇$ + 𝐷
𝛾 𝑇" − 𝑇$ 𝑝𝑐3 − 𝑟() 𝑅/0 − 𝑟$*
6
How to detect the
drought from an UAV?

21. How to detect the
drought from an UAV?
http://bestdroneforthejob.com/

22. Predicting root zone soil moisture
with near-surface moisture data in
semiarid environments

23. Manfreda et al. (AWR - 2007)
Relationship existing between
surface and root-zone soil
moisture

24. Soil Moisture Analytical
Relationship (SMAR)
The schematization proposed assumes the soil composed of two layers,
the first one at the surface of a few centimeters and the second one
below with a depth that may be assumed coincident with the rooting
depth of vegetation (of the order of 60–150 cm).
The challenge is to define a soil water balance equation where the
infiltration term is not expressed as a function of rainfall, but of the soil
moisture content in the surface soil layer.
This may allow the derivation of a function of the soil moisture in one
layer as a function of the other one.
Manfreda et al. (HESS - 2014)
s1(t)
s2(t)
First layer
Second layer
Zr2
Zr1

25. Soil water balance
Defining x=(s-sw)/(1-sw) as the “effective” relative soil saturation and
w0=(1-sw)nZr the soil water storage, the soil water balance can be
described by the following expression:
where:
s [–] represents the relative saturation of the soil,
sw [–] is the relative saturation at the wilting point,
n [–] is the soil porosity,
Zr [L] is the soil depth,
V2 [LT-1] is the soil water loss coefficient accounting for both
evapotranspiration and percolation losses,
x2 [–] is the “effective” relative soil saturation of the second soil layer.
1 − !! n!!!!
d!!(!)
d!
= !!!!!y t − !!!! !
Infiltration Losses
(1)

26. Water flux exchange between
the surface and the lower layer
The water flux from the top layer can be considered significant only
when the soil moisture exceeds field capacity (Laio, WRR 2006).
Assuming that
where n1 [-] is the soil porosity of the first layer;
Zr1 [L] is the depth of the first layer;
s1 (θ1/n1) [-] is the relative saturation of the first layer;
s1(t)
s2(t)
First layer
Second layer
(2)
Zr2
Zr1
sc1 [-] is the value of relative
saturation at field capacity.

27. Soil Moisture Analytical Relationship
(SMAR) beetween surface and root
zone soil moisture
Expanding Eq. 8 and assuming Dt = (tj - ti), one may derive the
following expression for the soil moisture in the second layer based on
the time series of surface soil moisture:
Assuming an initial condition for the relative saturation s2(t) equal to
zero, one may derive an analytical solution to this linear differential
equation that is
(7)
(9)
!! !! = !! + (!! !!!! − !!)!!!! !!!!!!!
+ 1 − !! !!! !! !! − !!!!
For practical applications, one may need the discrete form as well:
(8)

28. Sensitivity of SMAR’s
parameters
The derived root zone soil moisture (SRZ) is plotted changing the soil water
loss coefficient (A), the depth of the second soil layer (B), and the soil
textures (C).
Manfreda et al. (HESS - 2014)

29. Use with satellite data:
coupling the SMAR with EnKF
(Baldwin et al., J. Hydr., 2016)
Ridler et al (2014) showed how a satellite correction bias could be
calculated within the EnKF to correct the discrepancy between satellite
and in situ near-surface measurements.
Integrating a simple hydrologic model with an EnKF algorithm can
provide RZSM estimates as accurately as those made by complex
process models (Bolten et al, 2010; Crow et al, 2012).
Since the SMAR model’s four parameters (surface field capacity, root
zone wilting level, diffusivity coefficient and water loss coefficients) are
related theoretically to soil properties that are available globally, it
would be possible to effectively run SMAR across space after
uncovering relationships between soil physical variables and SMAR
model parameters (Reichle et al, 2001).

30. Schematic of the bias estimation
procedure for SMAR-EnKF
(Baldwin et al., J. Hydr., 2016)

31. SMAR-EnKF optimization
and prediction
Root mean square errors ranging from 0.014 - 0.049 [cm3 cm-3].
Semi-arid Highlands
Temperate Forests
Temperate Forests
North American Deserts
Great PlainsTropical Wet Forests
Forested Mountains Northern Forests
(Baldwin et al., J. Hydr., 2016)

32. Stream flow monitoring
with UAV

33. Flow Velocity
Measurements
Current meter
ADP
ADV
0.2
0.6
0.8
Current meter with suspension
equipment: trolley and middle weight
Traditional Methods

34. • The evaluation of river water discharge is traditionally
performed according to the rule ISO 748/1997, using the
velocity-area method which represents an efficient and
reliable tool. Operatively, computations require to divide
the section areas into several verticals and a further
subdivision of each vertical into discrete points, in order
to evaluate the mean velocity of the flow along each
vertical.
• The number of verticals and the distribution inside the
cross section must been chosen case by case based on
section width, riverbed geometry and flow regimes
characteristics, while the measurement points are fixed
according to the measurement methodology used, that
is, by wading or bridge.
Traditional Methods for water
discharge measures 1/2

35. The main objective is to obtain a correct evaluation of the
mean velocity for each vertical and section which is related to
a reliable reconstruction of flow field obtained through velocity
point measurements in several marks of hydraulic sections
generally distributed from bottom up to the free surface flow.
Once evaluated the mean velocity for each vertical, the water
discharge can be calculated by the way of the mean-section
method or mid section method.
Traditional Methods for water
discharge measures 1/2
i + 1
i
( ) ( )( )
( ) ( )[ ]
2.0
,1max
1
<
+
-+
iviv
iviv

36. Mean-Section method
In the first one, the partial discharge is computed by
multiplying the average value of mean velocities of two
adjacent verticals times the area included in the respective
verticals. The equation of the partial discharge between
two verticals 1 and 2, with depth d1 and d2, mean velocities
v1 and v2 and the horizontal distance between the two
verticals b, is the following:
b
2
dd
2
vv
q 2121
×÷
ø
ö
ç
è
æ +
×÷
ø
ö
ç
è
æ +
=
This is repeated for each segment and the total
discharge is obtained by adding the partial discharge
from each segment.
(10)

37. di+1di
bi
åå =
++
=
×÷
ø
ö
ç
è
æ +
×÷
ø
ö
ç
è
æ +
==
N
i
i
iiii
N
i
i b
ddvv
QQ
1
11
1 22
Mean-section method
2
1++
= å ii
ii
bb
dvQ
Mid section method
DISCHARGE
COMPUTATION
di
b
(11)
(12)

38. Entropy Model
Entropy distribution of flow velocity is:
( ) ú
û
ù
ê
ë
é
-
-
-+=
0max
0max
11ln
xx
xxM
e
M
u
u
M
1
1e
e
u
u
M
M
max
-
-
==f
where M is the entropy parameter that could be derived by the relation
between the mean and the maximum velocities observed in the cross
section:
÷
ø
ö
ç
è
æ
-
-
-
=
hD
y
hD
y
1expx
D
y
=x
dimensionless variable x, depending on the reference system adopted:
in the case of natural and artificial free surface
flows:
(13)
(14)
(15)
(16)

39. According to the approach of Moramarco and Singh (2010), the mean velocity
can be evaluated using the classical Manning’s formula:
fh SR
n
u 3/21
=
( ) ú
û
ù
ê
ë
é
÷
ø
ö
ç
è
æ
-+= *
D
y
k
α
y
y
k
uyu 1lnln
1
0
While the maximum velocity of the cross-section, umax, can be derived
through the velocity distribution proposed by Yang et al. (2004)
ú
û
ù
ê
ë
é
+
=÷÷
ø
ö
çç
è
æ
-
-
=F
D
y-D
ln
y
y-D
y
y
ln
k
1
g/R
n
1
M
1
1
)M(
max
max
max
0
max
6/1
h
M
M
e
e
y0 is the distance at which the velocity is hypothetically equal to zero; α is the dip-correction
factor, depending only on the ratio between the relative distance of the maximum velocity location
from the river bed, ymax, and the water depth, D, along the y axis, where umax is sampled
Entropy Model
(17)
(18)
(19)

40. Velocity distribution
smooth channel

41. Linear dependence
between Um and Umax

42. Particle tracking
velocimetry (PTV)
Lagrangian method
(Tauro et al., 2016)
Image processing

43. Particle Tracking
Particle detection Velocity vectors

44. River Monitoring

45. Testing different
tracers
Distance (m) Vmicro (m/s)
0.4 0.086
0.5 0.080
0.6 0.084
Wood Chips
polystyrene polyurethane
micro current meter
Q=18 l/s; h=0.27 m
Q=31 l/s; h=0.15 m

46. Velocity validation
Test 3 – PE + Alu
Test 3 – wood chips
EGU2017-15792 | Posters | NH6.3/AS4.43/GI2.10/HS11.31/SM5.8/SSS12.21
Testing different tracers for stream flow monitoring with UAS
Silvano Fortunato Dal Sasso, Salvatore Manfreda, Alonso Pizarro, and Leonardo Mita
Fri, 28 Apr, 17:30–19:00, Hall X3, X3.250

47. Comparison with
different tracers
Test Tracers
Vs (m/s)
% difference implementation issues
current meter radar Vmed (m/s)
1
wood chips
0.09
- 0.09 1% tracer clearly visible
coal 0.09 4% poor tracer's contrast
2
PE
0.46 0.38
0.50 8%
tracer visible - formation of
agglomerates
coal 0.52 12% poor tracer's contrast
3
PE + Al
0.37 0.32
0.37 0% tracer clearly visible
wood chips 0.36 4%
tracer with low dimension
and contrast
coal 0.34 9% poor tracer's contrast
PE + Al Wood chips Coal

48. Surface Flow Velocity
Pixels
500 600 700 800 900 1000 1100
Pixels
400
500
600
700
800
900
[m/s]
0
0.5
1
1.5
2
2.5
3
Charcoal
y = 1.0849x + 0.0869
R² = 0.89999
0
0.5
1
1.5
2
2.5
0 1 2 3
UAVderivedsurface
velocity(m/s)
Surface Velocity measure with
traditional techniques (m/s)

49. Surface Flow Velocity
Field survey on the Basento river
Vs (PTV) = 0.47 m/s
Vs (radar) = 0.43 m/s
1m

52. Papers related to this research line…
Baldwin, D., S. Manfreda, K. Keller, and E.A.H. Smithwick, Predicting root zone soil moisture with
soil properties and satellite near-surface moisture data at locations across the United States,
Journal of Hydrology, (under review) 2016.
Faridani, F., A. Farid, H. Ansari, and S. Manfreda, Estimation of the root-zone soil moisture using
passive microwave remote sensing and SMAR model, Journal of Irrigation and Drainage
Engineering, 04016070, 1-9, 2016.
Faridani, F., A. Farid, H. Ansari, S. Manfreda, A modified version of the SMAR model for estimating
root-zone soil moisture from time series of surface soil moisture, Water SA (under review), 2016
Manfreda, S., L. Brocca, T. Moramarco, F. Melone, and J. Sheffield, A physically based approach for
the estimation of root-zone soil moisture from surface measurements, Hydrology and Earth System
Sciences, 18, 1199-1212, 2014.
Manfreda, S., M. Fiorentino, C. Samela, M. R. Margiotta, L. Brocca, T. Moramarco, A physically
based approach for the estimation of root-zone soil moisture from surface measurements:
application on the AMMA database, Hydrology Days, pp. 47-56, 2013.
Manfreda, S., T. Lacava, B. Onorati, N. Pergola, M. Di Leo, M. R. Margiotta, and V. Tramutoli, On
the use of AMSU-based products for the description of soil water content at basin scale, Hydrology
and Earth System Sciences, 15, 2839-2852, 2011.

53. Summer School of Hydrology
Applied Course on UASs for
Environmental Monitoring
MATERA - Italy

54. First edition in 2011

55. Edition 2015

56. Edition 2016