Heihe River Runoff Prediction

Transcription

1 Heihe River Runoff Prediction Principles & Application Dr. Tobias Siegfried, hydrosolutions Ltd., Zurich, Switzerland September 2017 hydrosolutions

2 Overview Background Methods Catchment Characterization & Data Results Web-Deployment Conclusions & Discussion

3 Prediction Types and Application Short-term prediction [hours] Early warning, e.g. flood prediction Medium-range prediction [days, weeks, month] Operational management Long-term prediction [seasonal, multi-year] Seasonal planning, climate change impacts, etc.

4 Prediction Types and Application Short-term prediction [hours] Early warning, e.g. flood prediction Medium-range prediction [days, weeks, month] Operational management Long-term prediction [seasonal, multi-year] Seasonal planning, climate change impacts, etc.

5 Prediction Types and Application Short-term prediction [hours] Early warning, e.g. flood prediction Medium-range prediction [days, weeks, month] Operational management Long-term prediction [seasonal, multi-year] Seasonal planning, climate change impacts, etc.

6 Heihe River Catchment Characterization

7 Zhamashike Heihe River Catchment Impressions

8 Qilian Heihe River Catchment Impressions

9 Heihe River Catchment Characterization High inter-annual variability in summer discharge with long-term increasing trend. Existing hydropower stations in mid-stream: Longshou 1, Longshou 2, Sandaowan, Baopinghse, Xiaugushan, Dagushan, Erlongshan, Dipanzi. Huangzangsi planned. Flow at Station Yingluoxia is regulated. Flows cannot be predicted unless operating rules are known. Zhamashike Gauging Station, Qilian Gauging Station,

10 Zhamashike Gauging Station, Heihe River Comparison with CA Rivers Average hydrological year in Heihe River Basin: 20 percent runoff from now- & glacier-melt and 80 percent from liquid precipitation. Average hydrological year in large Central Asia Rivers: 80 percent runoff from now- & glacier-melt and 20 percent from liquid precipitation. Qilian Gauging Station, ChatkalRiver, Central Asia, Uzbekistan Q1 Q2 Q3 Q4

11 Data Data Availability Discharge station data (blue dots), all decadal data Qilian (520800): Zhamashike (520400): Meteorological station data (red dots), precipitation and temperature, all decadal data Tuole (52633): Yenigou (52645): Qilian (52657): Tuole Yeniguguo Zhamashike Yingluoxia Qilian

12 Methods Decadal time series as input. Rolling forecasts at different lead times (decades and months). For monthly forecasts,

13 Modeling Approach Regression, as compared to physically-based modeling. Time-delayed coordinate embedding. Target = dependent variable Predictors = indep. Variable, i.e. previous known values of time series. Using auxiliary data, e.g. from meteorological stations (P,T) to improve prediction results

14 Ensemble Models Combine predictions of multiple models that are trained on multiple datasets. Approach used in many forecasting domains.

15 Ensemble Models Combine predictions of multiple models that are trained on multiple datasets. Approach used in many forecasting domains. Example: Climate Change Example: Strom tracks

16 Ensemble Models Combine predictions of multiple models that are trained on multiple datasets. Approach used in many forecasting domains. Example: Climate Change Example: Strom tracks

17 Implementation R (open source modeling environment) Ensemble modeling, i.e. variance reduction through taking many training / test sets of time series, build separate models and average results. Rapid prototyping and easily reproducible results.

18 Qilian Gauging Station, Sample Results Quality assessment using different metrics. Summary: Satisfactory performance, summer discharge remains a challenge due to low predictability of convective storms in the mountains during this season. Metric cubist xgbtree xgblinear parrf enssimple nn enscaret MAE MSE RMSE MAPE LMSE rstd Green: good, red: bad obs (scaled) pred

19 Sample Results Sample Quality assessment of model forecasting skills at Qilian using scaled error metrics for each decade. Metric = absolute error / natural variability, for each decade. Satisfactory performance in summer months during irrigation season. Mediocre performance in winter time during low flows. good forecast excellent forecast

20 Operational Prediction

21 Web-Deployment Welcome Screen Stations Overview

22 Web-Deployment Station Information Discharge Stations

23 Web-Deployment Station Information Discharge Stations

24 Web-Deployment Station Information Meteorological Stations

25 Web-Deployment Station Information Meteorological Stations

26 Web-Deployment Data Enter/Edit Data

27 Web-Deployment Data Enter/Edit Data

28 Web-Deployment Models

29 Web-Deployment Forecast

30 Web-Deployment Assessment of Forecast Quality

31 Applications to other Regions Lets work together on this in Central Asia! Example: Uzbekistan

32 Applications to other Regions Lets work together on this in Central Asia! Example: Uzbekistan Promising first results

33 Conclusions Ensemble statistical methods offer a powerful approach to forecasting discharge of rivers. Good, long-term data record important. This includes data on discharge as well as data from nearby meteorological stations. Web-deployment offers interesting opportunity for operationalization of forecasts. Scalable implementations ensure quick replicability for other catchments and regions, contingent on the availability of data.

34 Discussion