The POLIMI forecasting chain for flood and drought predictions

Transcription

1 The POLIMI forecasting chain for flood and drought predictions Politecnico di Milano A. Ceppi 1, G. Ravazzani 1, C. Corbari 1, I. Bocus 1, G. Di Luccio 1, S. Meucci 2, A. Perotto, R. Salerno 3, M. Mancini 1 1 Department of Civil and Environmental Engineering (D.I.C.A.), Politecnico di Milano, Italy 2 M.M.I. s.r.l., Milano, Italy 3 Epson Meteo Centre, Cinisello Balsamo (Mi), Italy

2 Social and financial impact: extreme events and financial losses Main floods in the last 15 years in Italy and Europe Source: Munich Re report, 214 Italy Definition of great natural catastrophe: "a region s ability to help itself is distinctly overtaxed, making supraregional or international assistance necessary." Liguria (21, 211, 212) and Sardinia (213) Tuscany, Veneto, Campania (21) Messina, Tuscany (29) Piedmont (28) Marche (26) Lombardy (22) Calabria (2, 26, 211) Piedmont/Aosta Valley (2) Europe Serbia (214) Spain (212) Polond, Czech Republic and Germany (21) United Kingdom (27, 29, 214) Austria, Switzerland (27) Germany (26, 21, 213) Romania, Moldava, Switzerland, Austria (25) Polond, Czech Republic, Germany, Romania (22) The POLIMI forecasting chain for flood and drought predictions 2

3 Background & Aims of the study Over the last twenty years severe river floods and droughts have occurred in Europe, causing thousands of deaths and billion Euros in insured economic losses. Experience suggests that appropriate warnings with sufficient lead time can mitigate the consequences of heavy precipitation events and long dry periods. Therefore, meteorological forecasts coupled to hydrological models can be used to decide on an early water-system control action to prevent or reduce problems with floods, droughts or water quality. The two faces of the same coin Floods Droughts Background: The complex flood protection system of Milan and surroundings developed in the last 6 years has not been able to protect its urban area, which frequently flooded in the last 25 years; hence the improvement of the Milan flood control system needs a synergism between structural and non-structural engineering approaches. The POLIMI forecasting chain: WRF (deterministic, 1 km) + FEST-WB model (2 m) Area of study: three catchments located northern than Milan area (the Olona, Seveso and Lambro River basins) Target: how early warning systems are an effective complement to structural measures for flood control in Milan city? Background: In recent years frequent periods of water scarcity have enhanced the need to use water more carefully, even in European areas traditionally rich of water such as the Po Valley in northern Italy. The POLIMI forecasting chain: WRF (probabilistic with 2 ensemble members, 18 km) + FEST-EWB (2 m) Area of study: Muzza Bassa Lodigiana Consortium in the Po Valley, northern Italy. Target: development and implementation of the realtime drought forecasting system in the experimental test-site of a maize field in Livraga (Lodi): can we save irrigation water and use it in wiser way? The POLIMI forecasting chain for flood and drought predictions 3

4 FEST-WB hydrological model Snow Dynamics Meteorological forcings Spatial interpolation: Thiessen, IDW Soil Moisture update FEST-WB: Flash flood Event based Spatially distributed rainfall runoff Transformation including Water Balance Soil Parameters Vegetation Parameters Ravazzani, G., Gianoli, P., Meucci, S., and Mancini, M. (214). Assessing downstream impacts of detention basins in urbanized river basins using a distributed hydrological model, Water Resources Management, 28(4), doi: 1.17/s DEM Percolation Surface Runoff Definition of river network Subsurface routing groundw ater Surface flow routing Lakes and reservoires LEGEND Input Process Hydrograph Output Internal variable The POLIMI forecasting chain for flood and drought predictions 4

5 Area of study for flood forecasts Complex and anthropized channel network due to: many artificial channels for water supply sewerage system structural works for flood protection (Diotti sluice, CSNO, detention basin of Ponte Gurone) Basin Gauging Section Area [km 2 ] Lag Time [h] Olona Castellanza (VA) Seveso Paderno Dugnano (MI) Lambro Milano, via Feltre Change of natural discharge The POLIMI forecasting chain for flood and drought predictions 5

6 FEST-WB: input data Hourly Meteorological Data: Temperature [ C] Precipitation [mm] Air Humidity [%] Short Wave Solar Radiation [W/m 2 ] Basin N Stations Air Temperature Precipitation Air Humidity Solar Radiation Olona 2 3 Seveso Lambro TOTAL The POLIMI forecasting chain for flood and drought predictions 6

7 Observed data Hourly Hydrological Data Water level [cm] Rating curve Discharge [m 3 /s] Basin Gauging Station Rating curve State Lozza Yes Active Olona Castiglione No - Castellanza Yes Active Seveso Cantù Yes Historical Paderno Dugnano No - Caslino d Erba Yes Active Lambro Lambrugo Yes Active Peregallo Yes Active Milano - via Feltre Yes Active The POLIMI forecasting chain for flood and drought predictions 7

8 Calibration of the FEST-WB model (23-21 events) Basin Olona Gauging Station err Q max [%] Befor e After Lozza Castellanza Seveso Cantù Lambro Caslino Peregallo Milano The POLIMI forecasting chain for flood and drought predictions 8

9 Validation of the FEST-WB model ( events) Basin Station err Q max [%] Olona Lozza 26. Castellanza 16.7 Seveso Cantù -8.9 Caslino 14.5 Lambro Peregallo 1.2 Milano Problems due to detention basin effects at Ponte Gurone dam, operative since 21 The POLIMI forecasting chain for flood and drought predictions 9

10 The WRF meteorological model for flood forecasts The meteorological model used in this study is the WRF-ARW v.3.5 developed by the National Center for Atmospheric Research (NCAR) Spatial resolution: 3 nested domains 12 km -> 4 km -> 1 km Temporal output: 1 hour Vertical level: 37 (non-hydrostiatic) Forecast horizon: 36 hours Starting 12: UTC IC and BC provided by the GFS model (@12 km) Cloud microphysics scheme: Eta Longwave radiation scheme: RRTM (Rapid Radiative Transfer Model) Shortwave radiation scheme: Dudhia Land surface model: Noah with 4 soil layers and 24 types of soil PBL scheme: Bougeault-Lacarrère Owner: MOPI Epson Meteo Centre OUTPUT: Deterministic forecasts of hourly temperature at 2m above ground and precipitation provided by MOPI Epson Meteo Centre The WRF model domain Milan The POLIMI forecasting chain for flood and drought predictions 1

11 Re-analysis of flood events The cascade forecasting system applied in this study is currently based on hydrological model initialization from meteorological model output The re-analysis is based on the exceeding of the alert threshold (code 1): event: the observed discharge exceeds the warning threshold no-event: the observed discharge did not exceed the warning threshold Basin Olona Seveso Lambr o Gauging Station Level [m] Discharge [m 3 /s] Lozza - 36* Castellanza Cantù Paderno Dugnano ** Caslino d Erba - 6* Peregallo 1. 3 Milano, via Feltre * ** Courtesy of Civil Protection of the Lombardy Region The POLIMI forecasting chain for flood and drought predictions 11

12 Re-analysis of flood events A total of 15 events (45 analyzed days considering the peak, the rising and recession limb) between 28 and 21 were selected to value the hydro-meteorological chain performance, coupling the WRF meteorological model with the FEST-WB hydrological model. Case Study Day Type of Event 1 17,18 May 28 Convective 2 12,13,14 July 28 Convective 3 12,13,14 September 28 Convective 4 4,5,6 November 28 Stratiform 5 3 November,1,2 December 28 Stratiform 6 6,7,8 February 29 Stratiform 7 27,28,29 April 29 Stratiform 8 7 July 29 Convective 9 17,18 July 29 Convective 1 23,24,25,26 December 29 Stratiform 11 2,3,4,5,6,7 May 21 Stratiform 12 11,12,13 August 21 Convective 13 18,19 September 21 Convective October, 1,2,3, November 21 Stratiform 15 15,16,17 November 21 Stratiform The POLIMI forecasting chain for flood and drought predictions 12

13 Stratiform events key The POLIMI forecasting chain for flood and drought predictions 13

14 Olona basin: July 29 convective event The POLIMI forecasting chain for flood and drought predictions 14

15 Seveso basin: September 21 convective event 8 milion of Euros as total damage! 16 September 21 output run: h before the main peak flow Forecast Reliability: 37.5% 2 Paderno Dugnano CLEPS-FEST Ensemble Q25-75 E1 E E3 E6 E7 Emean E8 E9 Q [m3s-1] Emax Ensemble E1 E Q [m3s-1] Q [m3s-1] E4 E5 Ensemble Emin E12 Emean E13 E14 median E Emax E15 5 E16 P observed Emin : 22/9/21 : 22/9/21 21/9/21 12: 21/9/21 12: 21/9/21 : 21/9/21 : 2/9/21 12: 2/9/21 12: 2/9/21 : : 2/9/21 12: 19/9/21 19/9/21 12: days 19/9/21 : 18/9/21 12: 19/9/21 : 18/9/21 : 12: 17/9/21 12: 18/9/21 17/9/21 : days The POLIMI forecasting chain for flood and drought predictions 5 16/9/21 12: 18/9/21 : alarm code 17/9/21 12: 17/9/21 : 16/9/21 12: 1 Q sim Fest-WB Q sim Fest-WB Q observed E median Q median alarm code Emean P observed Ensembles Q sim Fest-WB 15

16 Lambro basin: August 21 convective event The POLIMI forecasting chain for flood and drought predictions 16

17 FORECASTED EVENT Skill scores of performance YES OBSERVED EVENT YES HIT (a) NO FALSE ALARM (b) The contingency table gives an overview of the predictive capabilities of the hydro-meteorological chain NO MISS (c) CORRECT REJECTION (d) Index Equation Perfect Score POD (Probability Of Detection) 1 F (False alarm rate) CSI (Critical Success Index) 1 CPI (Correct Performance Index) 1 Wilks, 26 The POLIMI forecasting chain for flood and drought predictions 17

18 FORECASTED EVENT FORECASTED EVENT FORECASTED EVENT Performance for each basin Olona OBSERVED EVENT YES NO Seveso OBSERVED EVENT YES NO Lambro OBSERVED EVENT YES NO YES 13 2 YES 7 2 YES 41 4 NO 1 75 NO NO Index Olona Seveso Lambro POD F CSI CPI The POLIMI forecasting chain for flood and drought predictions 18

19 FORECASTED EVENT Global performance The performance of the hydro-meteorological chain is not so high, but it is encouraging with a POD equal to 45%. The presence of false and missed alarms is due to: low performance of the WRF model during convective events uncertainty in the estimated Paderno Dugnano gauging station not accurate calibration of the hydrological Milano gauging station in the Lambro River basin OBSERVED EVENT Index Value YES NO POD.45 YES 61 8 F.4 CSI.42 NO CPI.74 The POLIMI forecasting chain for flood and drought predictions 19

20 Critical problems in the Lambro River basin (1) 1. The sewerage system in the province of Monza does not let to quantify how much water flows into the Lambro River during flood events Urbanized territories, which are not included in natural basin of the Lambro River, contribute to increase the runoff at the gauging section. Gauging stations Sewer network River network Natural basin of the Lambro River Towns The POLIMI forecasting chain for flood and drought predictions 2

21 Critical problems in the Lambro River basin (2) Problems for reproducing the peak flood and runoff due to lake regulations 2. Diotti sluice: impossible to quantify how much water is released into the Lambro River due to regulations of the water level of the Pusiano Lake water level. Diotti sluice The POLIMI forecasting chain for flood and drought predictions 21

22 Conclusions for flood predictions As non-structural method, the POLIMI hydro-meteorological chain can be used to predict floods in Milan urban area in advance, however some suggestions are strictly required to improve the hydro-meteorological chain: 1) Meteorological data (above all precipitation) are not sufficient to cover the entire basin area, and even hydrological information needs to be incresead. 2) The spatial resolution (1 km) of the WRF model is not approriate, in particular for forecasting convective events. A multi-model approcah, coupling high resolution deterministic models with coarser, but probabilistic models, is suggested. 3) A higher forecast horizon (48-72 hours) is necessary for civil protection actions in such hydrological cathcments. The POLIMI forecasting chain for flood and drought predictions 22

23 FEST-EWB hydrological model Snow Dynamics Meteorological forcings Spatial interpolation: Thiessen, IDW Soil Moisture FEST-EWB: Flash flood Event based Spatially distributed rainfall runoff Transformation including Energy and Water Balance Soil Parameters Vegetation Parameters Corbari C, Ravazzani G, and Mancini M, 211: A distributed thermodynamic model for energy and mass balance computation: FEST EWB. Hydrological Processes 25(9), DEM Percolation Surface Runoff Definition of river network Subsurface routing groundw ater Surface flow routing Lakes and reservoires LEGEND Input Process Hydrograph Output Internal variable The POLIMI forecasting chain for flood and drought predictions 23

24 Area of study for drought forecasts Eddy covariance station and TDR probes The territory of the MBL consortium covers an area of 74 km 2 in which there are more than 15 Actual irrigation evapotranspiration basins and thousands of fluxes irrigation sub-basins and soil which include the private lots of landowners moisture measures The POLIMI forecasting chain for flood and drought predictions 24

25 11/5/211 18/5/211 25/5/211 1/6/211 8/6/211 15/6/211 22/6/211 29/6/211 6/7/211 13/7/211 2/7/211 27/7/211 3/8/211 1/8/211 17/8/211 24/8/211 31/8/211 7/9/211 14/9/211 11/5/211 18/5/211 25/5/211 1/6/211 8/6/211 15/6/211 22/6/211 29/6/211 6/7/211 13/7/211 2/7/211 27/7/211 3/8/211 1/8/211 17/8/211 24/8/211 31/8/211 7/9/211 14/9/211 Soil moisture [-] Rainfall + Irrigation [mm] Cumulated Evapotraspiration [mm] 26/5/21 2/6/21 9/6/21 16/6/21 23/6/21 3/6/21 7/7/21 14/7/21 21/7/21 28/7/21 4/8/21 11/8/21 18/8/21 25/8/21 1/9/21 8/9/21 15/9/21 26/5/21 2/6/21 9/6/21 16/6/21 23/6/21 3/6/21 7/7/21 14/7/21 21/7/21 28/7/21 4/8/21 11/8/21 18/8/21 25/8/21 1/9/21 8/9/21 15/9/21 Soil moisture [-] Rainfall + Irrigation [mm] Cumulated Evapotraspiration [mm] Calibration of the FEST-EWB model (21-211) Rainfall 1 Observed.2 Irrigation Simulated by FEST-WB 1 5 Simulated by FEST-WB Observed Days Soil moisture Days Evapotranspiration Rainfall Irrigation Observed Simulated by FEST-WB Observed Simulated by FEST-WB Days The POLIMI forecasting chain for flood and drought predictions 25 Days

26 25/4/212 2/5/212 9/5/212 16/5/212 23/5/212 3/5/212 6/6/212 13/6/212 2/6/212 27/6/212 4/7/212 11/7/212 18/7/212 25/7/212 1/8/212 8/8/212 15/8/212 22/8/212 29/8/212 25/4/212 2/5/212 9/5/212 16/5/212 23/5/212 3/5/212 6/6/212 13/6/212 2/6/212 27/6/212 4/7/212 11/7/212 18/7/212 25/7/212 1/8/212 8/8/212 15/8/212 22/8/212 29/8/212 Soil moisture [-] Rainfall + Irrigation [mm] Cumulated Evapotraspiration [mm] Validation of the FEST-EWB model (212) Observed Simulated by FEST-WB Rainfall Irrigation Observed Simulated by FEST-WB Days Soil moisture Days Evapotranspiration Time table for irrigation water allotments The POLIMI forecasting chain for flood and drought predictions 26

27 The PRE.G.I. system: Hydro-meteorological forecast for irrigation management Our idea was to create a web application where farmers are able to monitor real-time soil moisture conditions and forecasts. The knowledge of Quantitative Precipitation Forecasts (QPFs) for the following weeks combined with the updating of hydrological conditions makes it possible to obtain a tool for water distribution management in cultivated areas in order to improve irrigation scheduling, minimize irrigation costs and save water Soil moisture forecasts Scheduled irrigations Probability to exceed the water surplus and stress threshold The POLIMI forecasting chain for flood and drought predictions 27

28 The WRF meteorological model for drought predictions The probabilistic forecast was provided by the REPS (Regional Ensemble Prediction System), based on the WRF-ARW model, implemented and developed by the Epson Meteo Centre. Spatial Resolution: 18 km Temporal output: 12 h Vertical levels: 36 (non-hydrostatic) Ensemble members: 2, each perturbation of the ensemble is produced by an algorithm developed by the EMC based on a special application of Ensemble Transform Kalman Filter (EnTKF) Forecast horizon: + 3 days Run starting at: : UTC IC and BC are provided by a Global Ensemble Prediction System (GEPS) based on a modified version of the WRF-ARW applied at the global scale, which has a grid mesh size of 2 km Owner: MOPI Epson Meteo Centre The WRF model domain The POLIMI forecasting chain for flood and drought predictions

29 Cumulated precipitation [mm] Cumulated precipitation [mm] WRF model: performance reanalysis 67 dry events where selected during 23 and 28 Mean cumulated precipitation over the Po basin Initialization: 26 May 26 Cumulated precipitation: Comparison between WRF vs TRMM Time [hours of forecast] Mean cumulated precipitation over the Po basin Initialization: 5 July 26 Time [hours of forecast] The POLIMI forecasting chain for flood and drought predictions

30 Skill scores of performance for the 212 growing season O i = observed values F i = median of forecasted values O i = the average of observed values n = numbers of analyzed events MRE between obs vs ensemble median values for soil moisture NS index between obs vs ensemble NS 1 n i 1 n i 1 ( O F ) i ( O O) i i 2 2 MRE 1 n n Fi Oi Oi i 1 NS index between obs vs ensemble median values for cumulated inflows BS index for 3 thresholds of cumulated rainfall 1 BS n n k 1 ( F k O k ) 2 The results show how it was possible by combing meteorological and hydrological models to have reliable soil moisture forecasts for up to 7 and 1 days respectively, with a mean relative error of less than 1%, and cumulated precipitation forecasts with a NS index above.8 and a BS score lower than.15. N = number of forecasting instances F k = the probability that an event was forecasted O k = the actual outcome of the event at instance k ( if it doesn't happen and 1 if it happens) The POLIMI forecasting chain for flood and drought predictions 3

31 N O I R R I G A T I O N To follow or not to T follow the PREGI system The POLIMI forecasting chain for flood and drought predictions 31 W I H I R R I G A T I O N

32 To follow or not to follow the PREGI system N O I R R I G A T I O N The POLIMI forecasting chain for flood and drought predictions 32 W I T H I R R I G A T I O N

33 Summary: can we save irrigation water? The POLIMI forecasting chain for flood and drought predictions 33

34 Conclusions for drought predictions 1. This developed tool for irrigation management has a higher reliability in comparison with flood forecasting systems, because it is characterized by slower and persistent weather dynamics over larger areas. One can consider, for instance, the large difference in hydrological processes between rainfall events with intensities which can reach up to 1 mm h -1 over areas of a few tens of km 2 (flood events) and events with evapotranspiration rates of about 7-8 mm per day over areas of a few thousand of km 2 (drought events). 2. The benefits of this project are both direct and indirect: the direct benefits regard the monitoring and forecasting of soil water content according to the current state of soil moisture values and water crop requirements, while the indirect benefits regard the optimization of water irrigations pursuing the best quantitative distribution, in particular periods of water scarcity, in order to minimize production losses caused by water stress due insufficient watering 3. One of the future developments is to extend these analyses over different sites with other case studies during future growing seasons. However, a limit for replicating this system in other areas will be that of obtaining real-time data (weather and soil moisture information), amounts and scheduled irrigation dates, which are usually not easy to acquire in real time. The POLIMI forecasting chain for flood and drought predictions 34

35 Thank you for your attention The POLIMI forecasting chain for flood and drought predictions

36 The POLIMI forecasting chain for flood and drought predictions 36