1. 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 7 th EARSeL workshop on Land Ice and Snow Remote Sensing of the Earth’s Cryosphere: Monitoring for operational applications and climate studies 3 rd of February 2014, Bern, Switzerland

2. 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

3. 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 discharge 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

4. 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

5. Study area IDR IVER NAME M EASUREMENT POINT W ATERSHED AREA ( KM ²) M IN. A LTITUDE ( M )M AX. A LTITUDE ( M ) 3AdigeTel16765103893 7AdigePonte Adige27052403893 8Rio FleresColle Isarco196610683245 10Rio VizzeNovale10813753500 13Rio RidannaVipiteno2079393456 15RienzaMonguelfo26410963217 16Rio CasiesColle11711982825 17Rio AnterselvaBagni Salomone8310913425 18AurinoCadipietra14910473485 20AurinoCaminata4208453485 21AurinoSan Giorgio6138193485 27GaderaMantana3898133120 28RienzaVandoies19207353217 37AdigeBronzolo69232283893

6. 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 mRGB 500 m

7. 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 function OFF-LINE SVR prediction Predicted target Selected input features

8. 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 F EATURE COMB.RMSE% NAO20,9 WAI22,1 SPI20,3 BAI22,5 NAO, Temp22,6 WAI, BAI, SPI22,6 …… …… 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

9. Results: SCA importance (step 1) P REDICTION LAG F EATURE SELECTED WITHOUT SCA M EAN RMSE% WITHOUT SCA F EATURE SELECTED WITH SCA M EAN 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 monthPrediction lag = 3 months

10. Results: meteorological and climatic variables (step 2 and 3) P REDICTION LAG F EATURE SELECTION STEP F EATURE SELECTED M EAN RMSE% WITHOUT SCA 1 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% step 2 (simulated best meteo params forecast)step 3 (meteo parmas time series as inputs)

11. Results: SVR / average comparison Prediction lag = 1 month Prediction lag = 6 months Prediction lag = 3 months P REDICTION LAG F EATURE SELECTED M EAN RMSE% SVR M EAN RMSE% 10 YEARS AVERAGE DISCHARGE 1 disch0, SCA-2:0, dischAvg10 22%33% 3 disch0, SCA-1:0, dischAvg10 28%33% 6 disch-10:0, SCA0, dischAvg10 31%33%

12. 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.

13. 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.

14. Snow maps webgis EURAC http://webgis.eurac.edu/snowalps/

15. Many thanks for the attention http://webgis.eurac.edu/snowalps/

16. 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 7 th EARSeL workshop on Land Ice and Snow Remote Sensing of the Earth’s Cryosphere: Monitoring for operational applications and climate studies 3 rd of February 2014, Bern, Switzerland

17. SVR training setup Training set Test 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 True target Estimated target