limate change impacts on water resources in the Upper Po basin Giovanni Ravazzani, Marco Mancini, hiara orbari, Alessandro eppi, Laura Boscarello, Giulia Ercolani Department of ivil and Environmental Engineering Water Science and Engineering Division
Introduction 2 Objectives Investigate climate change impacts on water resources of Upper Po river basin hallenges Simulate hydrological cycle of multi-scale complex river basins with few projected meteorological variable (precipitation and temperature) Method ompare hydrological simulations of decade 241-25 respect to control period 21-21
www.acqwa.ch 3 The AQWA Project (Assessing limate impacts on the Quantity and quality of WAter) is a large-scale integrating project, coordinated by University of Geneve and with 35 partners. The goal of the project is to use advanced modelling techniques to quantify the influence of climatic change on the major determinants of river discharge at various time and space scales, and analyse their impact on society and economy, also accounting for feedback mechanisms
Emission Scenario: A1B 4 source: IP The A1 storyline and scenario family describes a future world of very rapid economic growth, global population that peaks in mid-century and declines thereafter, and the rapid introduction of new and more efficient technologies. Major underlying themes are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income.
Emission Scenario: A1B 5 25 source: IP
FEST-EWB hydrological model 6 Snow Dynamics Meteorological forcings Spatial interpolation: Thiessen, IDW Soil Moisture update FEST-EWB: Flash flood Event based Spatially distributed rainfall runoff Transformation including Energy and Water Balance Soil Parameters Vegetation Parameters orbari, Ravazzani G, and Mancini M, 211: A distributed thermodynamic model for energy and mass balance computation: FEST EWB. Hydrological Processes 25(9), 1443-1452 DEM Percolation Surface Runoff Definition of river network Subsurface routing ground water Surface flow routing Lakes and reservoires LEGEND Input Process Hydrograph Output Internal variable
ase study: the Upper Po river basin 7 Basin Area = 38 km 2 57 reservoirs and 13 lakes 1 1 9 m 3 capacity artificial reservoirs Irrigated area = 4 5 km 2 156 river intakes 7 7 km artificial channels Development of modules for simulating reservoirs and irrigation
Evapotranspiration: simplified approach (FEST-WB) 8 Need to use a simplified model that requires only temperature ET P K ET rop oefficient Reference evapotranspiration.5 T T T 17.8 ET.23 Ra max min mean Hargreaves (1982) Required variables: Average, Minimum and Maximum daily Temperature
ET (mm) ET (mm) Modified Hargreaves-Samani equation Hargreaves for ET over Alps c c1zet HS ET, HSM, ET, HSM 9 8 T T 2 HE max min.817.22 z H R T T HT 132 m asl a max 9 PO17 - Vercelli 8 PO11 - Gad min 165 m asl 7 7 6 6 5 5 4 4 3 3 2 FAO-56 PM HSM 2 FAO-56 PM HSM 1 1 2 21 22 23 24 25 26 27 28 29 Year 2 21 22 23 24 25 26 27 28 29 Year Ravazzani G, orbari, Morella S, Gianoli P, and Mancini M, 212: Modified Hargreaves-Samani Equation for the Assessment of Reference Evapotranspiration in Alpine River Basins. Journal of Irrigation and Drainage Engineering 138(7), 592 599
Simplified groundwater model 2D Groundwater model Scheme for the calculation of water fluxes W Q Q W N Q E Q S Groundwater-surfacewater interaction L Q Q Q N S Hydraulic head updating h t1 t 1 h S y h = hydraulic head T = transmissivity [L 2 /T] S y = specific yield [-] W = sources (+) or sinks (-) Local flux calculation Q Q Q Q N TST 2 T T S N TNT 2 T T h h t S t N h h t Q s E t 2 t Q Q S E W TET 2 T T E W W TW T 2 T T h h t E t W W h t h t h M W Q Q Q Q h w h Q K sblw M h w h Ravazzani G, Rametta D, and Mancini M, 211: Macroscopic cellular automata for groundwater modelling: A first approach. Environmental Modelling & Software 26(5), 634 643.
River-groundwater interaction October February 2 26 flood low flow Losing cell Gaining cell
Groundwater model calibration 5 m Isopiezometric lines of unconfined aquifer June 22 Observed Simulated River
Local scale validation 13 Eddy covariance stations: measurement of terms of energy and water balance at local scale Livraga (LO) Landriano (PV)
Local scale validation (FEST-EWB) 14 Energy balance model validation against eddy measurements Surface temperature Net radiation Latent Heat Sensible heat Ground heat flux Soil moisture
Soil moisture [-] Precipitation + Irrigation [mm] umulative Evapotranspiration (mm) Local scale validation (FEST-WB) 15 3 25 2 15 1 Observed Simulated Evapotranspiration 5 4/3/212 5/28/212 6/25/212 7/23/212 8/2/212 Date.5.45 2 Soil moisture.4.35 4 6.3.25 Precipitation + Irrigation observed soil moisture simulated soil moisture 8 1.2 4/25/212 5/23/212 6/2/212 7/18/212 8/15/212 Date 12
Toce small case study - LIMATE SENARIOS REMO and RegM3 Precipitation, Temperature, Wind Speed, Solar Radiation, Relative Humidity Bias corrected Spatial resolution = at site Time resolution = 3 hours HYDROLOGIAL MODEL FEST-WB and FEST-EWB Spatial resolution = 2 m
monthly (mm) cumulated (mm) Discharge (m 3 /s) Least cost algorithm Toce small case study - 1 Toce small case study = FEST-WB FEST-EWB Meteo Data Spatial Interpolation Snow Pack Soil Vegetation Parameters Soil Moisture Update Hillslope hannel Deep Surface Runon Drainage runoff Route: Route: linear Muskingumreservoir unge Outflow Hydrograph DEM Flow Paths Hillslope and hannel Network Definition + At site bias corrected climate scenario forcings: REMO and RegM3 7 Observed forcings 4 1 6 5 FEST-EWB FEST-WB 3 1 Simulated FEST-WB Simulated FEST-EWB 4 2 3 1 2 1 1 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Mean monthly and cumulated actual evapotranspiration as computed by FEST- WB and FEST-EWB model driven by meteorological observations 1 3 61 91 122 152 182 213 243 274 34 334 365 Days Mean 21-21 flow duration curves from observed discharges and simulated by FEST-WB and FEST-EWB models driven by meteorological observations
Discharge (m 3 /s) Discharge (m 3 /s) Toce small case study - 2 limate model control period (21-21) Mean flow duration curves simulated by FEST-EWB (left) and FEST-WB (right) hydrological models driven by meteorological observations, REMO and RegM3 simulated climatic forcings. 1 1 weather stations weather stations RegM3 RegM3 1 REMO 1 REMO 1 1 1 3 61 91 122 152 182 213 243 274 34 334 365 Days 1 3 61 91 122 152 182 213 243 274 34 334 365 Days
Discharge (m 3 /s) Discharge (m 3 /s) Toce small case study - 3 limate model period 241-25 Mean flow duration curve of period 241-25 respect to control period (21-21) simulated by FEST-EWB driven by REMO (left) and FEST-WB driven by REMO (rigth). 1 1 21-28 21-28 241-25 241-25 1 1 1 1 1 3 61 91 122 152 182 213 243 274 34 334 365 Days 1 3 61 91 122 152 182 213 243 274 34 334 365 Days
Snow Water Equivalent (mm) % Evapotranspiration (mm) Discharge % (mm) Precipitation (mm) % Temperature ( ).6 Toce small case study - 4 FEST-WB 2m 4 2.5 1.9 1.5 1.3 1.5 1.4.9 2.8 + REMO at site Area = 1534 km 2 Difference REMO-415 REMO-11 3 2 1 138 133 72 5-16 -14-5 2 2 + 13 % P 15 T 11-14 -24-9 77-7 69 1-5 -1 1 2 3 4 5 6 7 8 9 1 11 12 13 5 1 1.6.5-1.1.6 2.5 1.9 1.5 1.3 1.5 1.4.9 2.8 + 1.3-1 -1 1 2 3 4 5 6 7 8 9 1 11 12 13 1 5-5 3 5 6 4 5 6 7 8 9 1 11 12 135 2 2 19 25 1-18 1 SWE -4-18 -13-11 -19-12 -16-25 -42-5 -75 1 8 75 4 2 1 3 185 + 8 % 75 + 13 % -1 ET 32 41 5 23 21 8 12 25 3 4 3-14 -25-5 -75 2 1 95 46 9-9 -2-14 -29-29 Q 62 65 9 2 15 1 5-5 -1 1 2 3 4 5 6 7 8 9 1 11 12 13-1 1 2 3 4 5 6 7 8 9 1 11 12 13-1 1 2 3 4 5 6 7 8 9 1 11 12
Snow Water Equivalent (mm) % Evapotranspiration (mm) % Discharge (mm) Precipitation (mm) % Temperature ( ) Toce small case study - 5 FEST-WB 2m + RegM at site Area = 1534 km 2 Difference RegM-415 RegM-11 155 4 3 2 1 145 81 5 2 155 + 25 % P T 15 39 45 23 2 15 2 1 5 15 2-27 -27-41 -5-1 1 2 3 4 5 6 7 8 9 1 11 12 13 1 5 2.4 2.1 1.6 2.1 1.1 1.4.6.7 1.1 1.3.3-1.3 + 1.1-5 -1-1 1 2 3 4 5 6 7 8 9 1 11 12 13 1 3 2 23 1 39 3 11 23 23-27 45 1 5 6-5 SWE -41 5 32 25 2 1 8 75-18 -25-5 2-27 6 15 1-75 -1 1 2 3 4 5 6 7 8 9 1 11 12 13 4 15 1 1 3 + 13 % 75 + 23 % 5-14 5-5 5 5 16 18 17 12 28 16 13 25-25 -5-75 -1 1 2 3 4 5 6 7 8 9 1 11 12 13 ET 146 Q 2 15 113 2 1 48 33 44 5 26 1 9 1-9 -1-23 -38-5 -1 1 2 3 4 5 6 7 8 9 1 11 12
limate change impacts over Upper Po 22 LIMATE SENARIOS REMO and RegM3 Precipitation and Temperature REMO T (K) - 1 ST January 21 Bias corrected Spatial resolution = 25 km Time resolution = 3 hours HYDROLOGIAL MODEL FEST-WB Spatial resolution = 1 km
Discharge (mm) Discharge (mm) Discharge (mm) Discharge (mm) alibration of hydrological model 23 2 15 Pellice@Villafranca uncertainty band simulated-ground observed 15 1 Varaita@Polonghera uncertainty band simulated-ground Observed 1 5 5 gen feb mar apr mag giu lug ago set ott nov dic gen feb mar apr mag giu lug ago set ott nov dic 3 25 2 Toce@andoglia uncertainty band simulated-ground observed 15 1 Po@Isola uncertainty band simulated-ground observed 15 1 5 5 gen feb mar apr mag giu lug ago set ott nov dic gen feb mar apr mag giu lug ago set ott nov dic
Snow Water Equivalent (mm) Evapotranspiration (mm) Discharge (mm) Precipitation (mm) % Temperature ( ) limate change impacts over Po at Isola 24.6 FEST-WB 1 km 2.5 1.9 1.5 1.3 1.5 1.4.9 2.8 + REMO 25 km Area = 26 km 2 Difference REMO-415 REMO-11 2 15 1 5 5 97 36 5-5 3 75 + 9 % P T -1-23 -6 12-3 -14 33-9 38 1-25 -5-75 -1 1 2 3 4 5 6 7 8 9 1 11 12 13 5 25 2 1 1.7.8 -.9.7 2.5 1.8 1.6 1.7 1.5 1.6 1.1 2.7 + 1.4-1 -1 1 2 3 4 5 6 7 8 9 1 11 12 13 1 5-5 2 SWE 1 5 75 15 5 4 5 6 7 8 9 1 11 12 13 7 1-7 24 19 1 25-14 -13-12 -22-17 -25-27 -3 4 3 2 32 1 1 77 75-1 + 4 % ET + 13 % 45 5 48 43 2 25 2 11 1 11 5 4 2 7 2 5 5-2 -3-4 -7-25 -2 Q 15 8 31 1 75 5 25-25 5-5 -75 1-5 -75-5 -75-1 1 2 3 4 5 6 7 8 9 1 11 12 13-1 1 2 3 4 5 6 7 8 9 1 11 12 13-1 1 2 3 4 5 6 7 8 9 1 11 12
Stage (m asl) Stage (m asl) Stage (m asl) limate change impacts over reservoirs 25 Sabbione reservoir, storage capacity 25 1 6 m 3, catchment area 19 km 2 246 245 244 245 244 243 242 21 22 23 25 26 27 29 21 246 243 Max October 242 Min May 241 242 243 245 245 target 244 243 target stage simulated stage simulated 242 241 242 243 245 246 247 249 25
Stage (m asl) Stage (m asl) Stage (m asl) limate change impacts over reservoirs 26 245 ampliccioli reservoir, storage capacity 8 1 6 m 3, catchment area 34 km 2 137 244 136 135 134 133 243 Max Jul-Nov 242 Min April 241 242 243 245 132 21 22 23 25 26 27 29 21 137 136 135 134 target stage simulated stage target simulated 133 132 241 242 243 245 246 247 249 25
GRI (-) limate change impacts on groundwater 27 Impact on Groundwater 2.5 Groundwater Resource Index (*) 2. 1.5 1..5 where GRI y,m and D y,m are respectively the values of the index and of the groundwater detention for the year y and the month m, while μ D,m and σ D,m are respectively the mean and the standard deviation of groundwater detention values D simulated for the month m in a defined number of years.. 21 211 221 231 241 251 -.5-1. -1.5 ly value Decadal average -2. -2.5 (*) Mendicino, G., Senatore, A., Versace, P., 28: A Groundwater Resource Index (GRI) for drought monitoring and forecasting in a mediterranean climate. Journal of Hydrology, 357(3 4), 282 32
limate change impacts on groundwater 28 Impact on Groundwater July 241 GRI = +.53 Surplus = 5 1 6 m 3 half the capacity of all artificial reservoirs
onclusions 29 limate projections on Upper Po basin show an increase of annual precipitation and temperature but a decrease of precipitation during summer period Shift in temperature and precipitation patterns and amount reflect an increase of snow accumulation during winter but a rapid melt during spring. An increase of mean annual discharge but a decrease during summer are expected. Artificial reservoirs regulation policy has to be revised due to the shift of snow melting that can affect larger reservoirs. Groundwater detention is expected to increase with positive value of Groundwater Resource Index during summer that can compensate the decrease of discharge.