Seasonal hydrological forecasting from snow cover maps and climatological data using support vector machine

M. Callegari, L. De Gregorio, P. Mazzoli, C. Notarnicola, L. Pasolli, M. Petitta, A. Pistocchi, R. Seppi

7th EARSeL workshop on Land Ice and SnowRemote Sensing of the Earth’s Cryosphere: Monitoring for operational applications

and climate studies

3rd of February 2014, Bern, Switzerland

Motivations and objective

• Quick response hydrological events (such as floods) cannot be predicted with a lead time longer than a few days.

• Slow response discharges (such as droughts) depend typically on the depletion of the catchment that is related to the catchment state, which is easier to predict.

• Medium-term (1 to 6 months lag) water discharge estimation is important for water management in, e.g.:

• Agriculture or domestic use• Hydropower

Objective:To estimate monthly mean discharge in alpine

catchments with a prediction lag equal to 1, 3 and 6

Background

• Statistical models, e.g. autoregressive moving-average (ARMA), have been adopted for predicting the monthly discharge on the basis of the discharge time series.

present

time

discha

rge

• Machine learning techniques, such as SVR, can also be employed and can assure better prediction accuracy.

• Most used for economic forecasting

• Also employed for environmental parameters estimation

Prediction lag

Target to be

predicted

General concept of the proposed method

• SVR can ingest inputs coming from different sources • not only discharge time series

• In alpine regions, the snow accumulated in the basins plays the role of “water tower”

• it can provide relevant information for predicting the discharge

• Snow cover area (SCA) is much easier to detect with respect to SWE • Test SCA time series as input feature in the SVR

• Test other meteorological and climatic variables (which describe precipitation and snow melting processes) as input features of the SVR

Study area

ID RIVER NAME MEASUREMENT POINT

WATERSHED AREA (KM²) MIN. ALTITUDE (M) MAX. ALTITUDE (M)

3 Adige Tel 1676 510 38937 Adige Ponte Adige 2705 240 38938 Rio Fleres Colle Isarco 1966 1068 324510 Rio Vizze Novale 108 1375 350013 Rio Ridanna Vipiteno 207 939 345615 Rienza Monguelfo 264 1096 321716 Rio Casies Colle 117 1198 282517 Rio Anterselva Bagni Salomone 83 1091 342518 Aurino Cadipietra 149 1047 348520 Aurino Caminata 420 845 348521 Aurino San Giorgio 613 819 348527 Gadera Mantana 389 813 312028 Rienza Vandoies 1920 735 321737 Adige Bronzolo 6923 228 3893

Snow maps dataset

• From 2002 to 2012• Daily snow maps obtained by

250 m MODIS products. • Improved resolution to 250 m

EURAC 250 m NASA 500 m RGB 500 m

NASA algorithm and RGB images.

ON-LINE

Proposed method scheme

Training/validation input features

(i.e. SCA, past discharge, meteo. and climat.

parameters) Features selection

Model selection(C, ε, kernel

param) SVR training

Training/validation targets

(i.e. future discharge)

Empirical risk term

Regularization parameter

Kernel functio

n

OFF-LINE

SVR predictio

nPredicted

targetSelected input

features

Feature selection

SCA, discharge time frame length

selection

Model selection(C, ε, kernel param)

Meteorological and climatic variables

selection

Model selection(C, ε, kernel param)

Meteorological and climatic variables time frame length

selection

Model selection(C, ε, kernel param)

RMSE% on the validation samples of 3 catchments:• Adige at Bronzolo (big)• Rio Fleres at Colle Isarco (small)• Rienza at Vandoies (medium-sized)

Feature selection criteria

min RMSE%

Fast response to the discharge (differently from SCA)

Only the forecast in the target month can be informative

Simulate an ideal forecast (i.e. actual value) and try all the possible combination

FEATURE COMB.

RMSE%

NAO 20,9WAI 22,1SPI 20,3BAI 22,5

NAO, Temp 22,6WAI, BAI, SPI 22,6

… …… …

• Meteorological and climatological parameters describe precipitation and rapidity of the snow melting process

• Tested parameters: NAO, WAI, BAI, SPI, temperature

step 1 step 3step 2

RMSE%=2√mean [( 𝒚 −~𝒚

𝒚 )2]  ∙100

Results: SCA importance (step 1)

PREDICTION LAG

FEATURE SELECTED WITHOUT SCA

MEAN RMSE% WITHOUT SCA

FEATURE SELECTED WITH SCA

MEAN RMSE% WITH SCA

1 disch-11:0, dischAvg10 28% disch0, SCA-2:0, dischAvg10 22%3 disch-10:0, dischAvg10 32% disch0, SCA-1:0, dischAvg10 28%

Prediction lag = 1 month Prediction lag = 3 months

Results: meteorological and climatic variables (step 2 and 3)

PREDICTION LAG

FEATURE SELECTION STEP FEATURE SELECTED MEAN RMSE%

WITHOUT SCA1 1 - SCA and discharge time series

length selection disch0, SCA-2:0, dischAvg10 22.4%

1 2 – meteo params selection simulating best forecast disch0, SCA-2:0, dischAvg10, SPI 21.0%

1 3 - meteo params time series length selection disch0, SCA-2:0, dischAvg10, SPI0 21.6%

15 20 25 30 3515

20

25

30

35

37

28

8

7

3

18

20

2127

15

17

16

1310

RMSE% - SVR no meteo params

RM

SE

% -

SV

R w

ith m

eteo

par

ams

step 2 (simulated best meteo params forecast) step 3 (meteo parmas time series as inputs)

Results: SVR / average comparison

15 20 25 30 35 40 45 5015

20

25

30

35

40

45

50

37

28

8

7

3

18

20

2127

15

17

16

13

10

RMSE% - SVR

RM

SE

% -

10 y

ear a

vera

ge d

isch

arge

15 20 25 30 35 40 45 5015

20

25

30

35

40

45

50

37

28

8

7

3

18

20

2127

15

17

16

13

10

RMSE% - SVR

RM

SE

% -

10 y

ear a

vera

ge d

isch

arge

15 20 25 30 35 40 45 5015

20

25

30

35

40

45

50

37

28

8

7

3

18

20

2127

15

17

16

13

10

RMSE% - SVR

RM

SE

% -

10 y

ear a

vera

ge d

isch

arge

Prediction lag = 1 month Prediction lag = 6 monthsPrediction lag = 3 months

PREDICTION LAG FEATURE SELECTED MEAN RMSE% SVR

MEAN RMSE% 10 YEARS AVERAGE

DISCHARGE1 disch0, SCA-2:0, dischAvg10 22% 33%3 disch0, SCA-1:0, dischAvg10 28% 33%6 disch-10:0, SCA0, dischAvg10 31% 33%

𝑅2=0.81

Conclusion

• With the proposed approach it is possible to improve the prediction accuracy with respect to the prediction using the average discharge of the previous 10 years:

• Lag 1 -11% (33%, 22%)• Lag 3 -5% (33%, 28%)• Lag 6 -2% (33%, 31%)

• SCA time series reveals to be an important input feature for estimating the discharge:

• Lag 1 -6% (28%, 22%)• Lag 3 -4% (32%, 28%)

• Meteorological and climatic variables as input features do not bring any significant improvement in the prediction accuracy.

Future works

1. To apply the prediction method to other basins in the European Alps.

2. Build a similar discharge prediction method for basins with short time series (e.g. 1 year)

• How? Training on the single basin is not possible (few samples)

Find similar catchments with longer time series using watershed attributes (e.g. area, mean altitude, etc.) and climatic conditions

Train a SVR on the similar catchments found

1. 2.

Snow maps webgis EURAC

http://webgis.eurac.edu/snowalps/

Many thanks for the attention

http://webgis.eurac.edu/snowalps/

Seasonal hydrological forecasting from snow cover maps and climatological data using support vector machine

M. Callegari, L. De Gregorio, P. Mazzoli, C. Notarnicola, L. Pasolli, M. Petitta, A. Pistocchi, R. Seppi

7th EARSeL workshop on Land Ice and SnowRemote Sensing of the Earth’s Cryosphere: Monitoring for operational applications

and climate studies

3rd of February 2014, Bern, Switzerland

SVR training setup

Training setTest set

Training/Test Separation:

On the training set, cross-validation strategy is applied:

The prediction accuracy on the validation set is measured as RMSE% and it is used as criterion for model selection and feature selection. training sample

validation sample

step 1

step 3

step 2

RMSE%=2√mean [( 𝒚 −~𝒚

𝒚 )2]  ∙100

True target

Estimated target