7 th EARSeL workshop on Land Ice and Snow
description
Transcript of 7 th EARSeL workshop on Land Ice and Snow
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%
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RMSE% - SVR no meteo params
RM
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step 2 (simulated best meteo params forecast) step 3 (meteo parmas time series as inputs)
Results: SVR / average comparison
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RMSE% - SVR
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RMSE% - SVR
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RMSE% - SVR
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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