Glaciers, Snow and Hazards: current EO

limitations and user needs for the future

Claudia Notarnicola

EURAC Research-Institute for Applied Remote Sensing, Bolzano/Bozen, Italy

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Why snow/glaciers and hazards?

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• Main Alpine cryosphere components are:

snow glaciers permafrost

Climate warming is causing fast changes of alpine cryosphere.

Alpine cryosphere is a

natural sensor of climate

change (IPCC, 2013)

Alpine cryosphere changes influence human

activities in a variety of ways, e.g. hydrology

and natural hazard

SNOW

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WATER AVAILABILITY – SNOW COVER –

SNOW WATER EQUIVALENT- RIVER DISCHARGE

TOURISM – SNOW COVER - SNOW HEIGHT

GLACIERS

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WATER AVAILABILITY –

GLACIER MELT IN SUMMER MONTHS

INDICATORS FOR CLIMATE CHANGES

E.G. MASS BALANCE

GLACIER LAKE OUTBURST FLOOD

HAZARDS

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AVALANCHE – SNOW COVER - SNOW STRUCTURE

PERMAFROST CHANGES.

DEBRIS FLOW

RISKS FOR INFRASTRUCTURES

WATER AVAILABILITY– FLOODS

Images from Mair V., et al 2011, PermaNET Synthesis Report

Users Needs

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Variables

Data Requirements Data Specifications

Context Problems UsersSpatial

Resolution

Time

Resolutio

n

Accura

cy

Lead

TimeScale

Snow cover

Civil Protection (e.g.

avalanche forecast)Resolution

Civil

Protection250 m weekly

Tourism Exposure Consortium daily

monthly

Snow water

equivalent

Civil Protection Hydrographic services SKITOUR 250 m weekly

Idroelectric productivity Wind

Infiltration Agriculture Irrigation Variable

Humidity

Run-off Civil Protection Outlier detection Daily Basin

Weekly

River discharge

Civil Protectionvariability of monthly

discharge with altitude

Water

resourcesBasin

48h forecastingHydroelectric

company

Outlier detection

Field measurements

Mattia Callegari 8

EO Observable Parameters

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Many data exists for observing alpine cryosphere changes: ground and satellite

Ground data Satellite data

Point measurements or small areas Cover wide areas (also in hardly accessible

places)

Low revisit time (for manual measurements) High revisit time

High accuracy Low accuracy

Temporal domain

Spatialdomain

Parameters

Cryosphere monitoring

EURAC-MODIS Snow cover maps

Oct-2015 – Jun-2016

Snow

11webgis.eurac.edu

Spatially explicit time series of data

High resolution snow maps from S2

snow

no snow

Snow and wet snow cover monitoring

Snow and wet snow cover monitoring

Daily mean discharge

Mean discharge of the 12 days after the S1 acquisition

Wet snow cover area (SAR)

Snow cover area (optical)

Glacier cover maps from S2

snow

firn

ice

soil

cloud

Glacier monitoring with S1

M. Callegari, L. Carturan, C.Notarnicola, P. Rastner, R. Seppi, F.Zucca, GLACIER ZONE CLASSIFICATION USING RADARSAT-2 C-BAND SAR POLARIMETRY AND SUPPORT VECTOR

MACHINE, IGARSS 2015, 27-31 July 2015, Milan, Italy

Rock glacier movements

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Amola RG

A

C

B0 30 cm/year

Geocoded velocity map

obtained with the SBAS algorithm

-2

0

2

4

6

8

10

12

14

16

18

20

-2 0 2 4 6 8 10 12 14 16 18 20

SBA

S (

cm

/y)

total station (cm/y)

Total station – SBAS comparison

Displacement time

series obtained

with SBASRMSE = 3.5 cm

(M.Callegari , PhD thesis 2015)

Parameters not observable from EO data

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Several key parameters are not directly observable by remote sensing

data.

River discharge, snow water equivalent are fundamental but needs to be

estimated through models, ground data et al.

In developed countries, users have already tools and data to support the

monitoring of the cryosphere.

Co-production is needed to result in developments and applications to

satisfy user needs.

River discharge with ML approach

Statistical approach

present

time

discharge

Prediction lag ∆𝑡

Target to be

predicted

Machine learning approach:

Support Vector Machine (SVM), which is used for time series prediction in many application

domains (e.g. economic).

Main advantages of SVM:

• can estimate highly non linear function

• can easily handle inputs of different kind (not only discharge time series)

Meteorological and

climatological variables

Discharge time series

Remote sensing

products

Results

10 years average

discharge

(benchmark)

AR = Autoregressive model (standard statistical method)

0

5

10

15

20

25

30

35

40

RM

SE%

1 month lag 3 months lag 6 months lag

River discharge forecast

0

20

40

60

80

100

120

Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct Jan

cum

ula

tive

vo

lum

e er

ror

(m3

x 1

07) 10 years average

AR

SVR noSCA

SVR

watershed 7, mean annual water volume = 160 x 107 m3

prediction lag = 1 month

Test year 1 Test year 2 Test year 3

M.Callegari, P.Mazzoli, L.De Gregorio, C.Notarnicola, L. Pasolli, M.Petitta, A.Pistocchi, Seasonal river discharge forecast using machine learning techniques: a case

study in the Italian Alps“, Water 2015, 7(5), 2494-2515; doi:10.3390/w7052494.

Potential

Spatially explicit times series of hydrological relevant data where other

observations are rare

Strong in Energy Balance related approaches of hydrology

Limitation

Data gaps and availability in the past (> 20 years)

Accuracy of indirect parameters (compared to measurements)

Key parameters not directly observable by remote sensing imagery

Solution

Clever integration with other data sources and / through models

How Remote Sensing can contribute …

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Regional Users – Future trends

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In the last years, regional users have shown high interest in:

- Tools/Indicators for support the adaptation to climate changes in the touristic

areas

- Tools/Indicators for supporting the response phase in case of events

- Tools/Indicators for supporting management of water resources (availability

for agriculture and tourism)

Thank you for the attention!

Questions/comments?

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