7 th EARSeL workshop on Land Ice and Snow

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

2. Motivations and objective • Quickresponsehydrological 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.: • Agricultureor domestic use • Hydropower Objective: To estimate monthly mean dischargein 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 dischargeon the basis of the discharge time series. Target to be predicted present discharge time Prediction lag • 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

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

6. Snow maps dataset • From 2002 to 2012 • Daily snow maps obtained by 250 m MODIS products. • Improved resolution to 250 m NASA 500 m RGB 500 m EURAC 250 m

7. Proposed method scheme Kernel function Empirical risk term Training/validation targets (i.e. future discharge) Regularization parameter Model selection (C, ε, kernelparam) Training/validation input features (i.e. SCA, past discharge, meteo. and climat. parameters) SVR training Features selection OFF-LINE ON-LINE Selected input features SVR prediction Predicted target

8. Featureselection step 2 step 3 step 1 • Meteorological and climatological parameters describe precipitation and rapidity of the snow melting process • Tested parameters: NAO, WAI, BAI, SPI, temperature Model selection (C, ε, kernelparam) Model selection (C, ε, kernelparam) Model selection (C, ε, kernelparam) Meteorological and climatic variables time frame length selection SCA, discharge time frame length selection Meteorological and climatic variables selection min RMSE% • Only the forecast in the target month can be informative • Simulate an ideal forecast (i.e. actual value) and try all the possible combination Fast response to the discharge (differently from SCA) Feature selection criteria • RMSE% on the validation samples of 3 catchments: • Adige at Bronzolo(big) • Rio Fleres at ColleIsarco(small) • Rienza at Vandoies(medium-sized)

9. Results: SCA importance (step 1) Prediction lag = 1 month Prediction lag = 3 months

10. Results: meteorological and climatic variables (step 2 and 3) step 3 (meteoparmas time series as inputs) step 2 (simulated best meteoparams forecast)

11. Results: SVR / average comparison Prediction lag = 6 months Prediction lag = 1 month Prediction lag = 3 months

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 • To apply the prediction method to other basins in the European Alps. • 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 1. 2. Train a SVR on the similar catchments found

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

17. SVR training setup Training/Test Separation: Training set Test set 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. step 1 step 2 training sample Estimated target validation sample step 3 True target