Rainfall daily gridded datasets for the periods 1980-2010 and 2020-2050

Rainfall daily gridded datasets for the
periods 1980-2010 and 2020-2050
Corrado Camera
Nicosia, 6-9 December 2012
EWACC – Young Scholars Forum

Outlines
• Introduction
• Contest and aim
• Gridded datasets for past times
• Available data and their reliability (Observational data)
• Selection of the best interpolation technique
• Gridded datasets for future times
• Available data (RCMs)
• Downscaling
• Interpolation

Introduction
Eastern Mediterranean heavily affected by future climate change
Enhanced water scarcity
Need for reliable adaptation measures

Introduction
Very dry < 400 mm/y
Very wet > 600 mm/y

Introduction
DIMINISHING RAINFALL
GRIDDED DAILY DATASETS
RESOLUTION 1X1 KM
Effects on hydrological regime
Effects on groundwater recharge
and surface water flow
Effects on GW amount and quality
UNDERSTAND
?
QUANTIFY
MODELS
Effects on man’s activities
ADAPT

Datasets for past times
INPUT OBSERVATIONAL DATA
Period 1980 - 2010
Now 74 stations (only Greek Cyprus)
Target 130 stations (whole island)
Daily data
Gaps filled
Data tested for homogeneity

Introduction to Interpolation Techniques
• Few works with daily data, mainly monthly
• General scheme simple (single) methods – complex (combined) methods
SIMPLE METHODS
Regression geographical variables
• Linear multiple regression (LMR)
• Geographically weighted regression
(GWR)
• Step Wise Regression (SW)
Neighbouring interpolation
• Inverse distance weighting (IDW)
• Angular Distance weighting (ADW)
• Kriging (KR)
• Thin Plate Splines (TPS)
COMBINED METHODS
Regression + residuals interpolation
Geographical variables:
• Regression (LMR, GWR, SWLMR,
SWGWR)
Residuals after regression:
• interpolation (IDW, ADW, KR, TPS)
Sum two contributions

Variables for regression
Variables representative of the processes
Differences winter-summer
AUTUMN-WINTER:
Land-sea effects
Orographic effects
Mountain shadow effects
SPRING-SUMMER:
Convective processes
Orographic effects
Mountain shadow effects

Variables for regression
SEASONAL MEANS FOR THE PERIOD 1980-2010 (IDW)

Variables for regression
Six variables: 1) altitude, 2) distance from coast, 3) distance from the
mountains main ridge to the east and 4) to the west, 5) easting; 6) northing

Regressions
REGRESSION METHODS
• Multiple linear regression (lmr)
• Geographically weighted regression (gwr)
• Step wise regression based on AIC (swr) AIC accuracy-complexity measure
INTERPOLATION METHODS
• Inverse Distance Weighting (IDW)
• Angular Distance Weighting (ADW)
• 3D Thin Plate Splines (3DTPS)
• Kriging
EVALUATION METHODS
• CRE compound relative error
• NRMSE normalized RMSE
• MAE mean absolute error
• R Pearson coefficient
• RMSE root mean squared error
• CSI critical success index
• Test subset 36 days (rain 0, min, max, 3 rd , 6 th … 99 th percentile area average)
• Evaluation - Ranking station by station, day by day

Ranking methods
days
stations
Models Ave rank CRE # MAE # RMSE # NRMSE # R # CSI # CSI.extr # CRE # MAE # RMSE # NRMSE # R # CSI # CSI.extr #
swlmridw
1.93 0.70 1 1.80 2 2.75 1 0.14 1 0.49 1 0.50 3 0.29 2 0.74 2 1.80 2 4.73 2 0.07 3 0.96 2 0.82 3 0.86 2
idw 2.64 0.71 2 1.85 5 2.79 3 0.14 2 0.49 2 0.47 4 0.23 4 0.62 1 1.85 5 4.71 1 0.07 5 0.97 1 0.83 1 0.91 1
gwr-idw 2.86 0.75 4 1.77 1 2.83 4 0.14 4 0.49 3 0.51 1 0.31 1 0.86 7 1.77 1 4.74 3 0.07 1 0.96 4 0.82 2 0.85 4
lmr-idw 3.36 0.72 3 1.83 4 2.79 2 0.14 3 0.48 4 0.51 2 0.29 3 0.75 3 1.83 3 4.79 5 0.07 4 0.96 3 0.81 5 0.86 3
gwr 4.50 0.78 5 1.81 3 2.88 5 0.14 5 0.43 5 0.45 5 0.22 5 0.83 5 1.81 4 4.77 4 0.07 2 0.95 5 0.81 4 0.85 6
swlmr 6.07 0.78 6 2.02 6 3.01 6 0.15 6 0.36 7 0.42 6 0.17 7 0.83 4 2.02 6 5.28 6 0.08 6 0.94 6 0.79 6 0.84 7
lmr 6.64 0.81 7 2.07 7 3.08 7 0.15 7 0.36 6 0.41 7 0.18 6 0.86 6 2.07 7 5.37 7 0.08 7 0.93 7 0.78 7 0.85 5

Gridded datasets for future times
DATA RETRIEVAL PERIOD 2020-2050
Suitable source Climate Models (past calibration – future prediction)
• GCMs Global - CO 2 emission scenarios - resolution 300 km
• RCMs smaller areas (e.g. Europe) - dynamical downscaling GCM - resolution 25 km
IPCC, 2007

Gridded datasets for future times
6 RCMs from ENSEMBLES 2 models for 3 different forcing GCMs
Institution Driving GCM Model Acronym
CNRM ARPEGE_RM5.1 Aladin CNRM-RM5.1
ETHZ HadCM3Q0 CLM ETHZ-CLM
KNMI ECHAM5-r3 RACMO KNMI-RACMO2
METNO BCM HIRHAM METNOHIRHAM
METO-HC HadCM3Q0 HadRM3Q0 METO-HC_HadRM3Q0
MPI ECHAM5-r3 REMO MPI-M-REMO
Select one
scores to evaluate models performance VS observed values:
• point values
• spatial trend
Mean yearly
value

Gridded datasets for future times
MEAN ANNUAL RAINFALL (1980-2010) - OBSERVATIONS

Gridded datasets for future times
MEAN ANNUAL RAINFALL (1980-2010) - MODELS

Gridded datasets for future times
FROM RCM TO OBSERVATIONAL POINTS
ASSUMING
Stationary climate for a 30 years period
DOWNSCALING
• Monthly base • Skewness
• Mean
• Pdry
• Variance • Auto-correlation
Change factors (α)
USING
P
P
P
Fut
Obs
Fut
RCMFut
RCMFut
P
= where P
= α
RCMCon
RCMCon
P
P
Obs
= α ⋅ P
P = any variable

Gridded datasets for future times
HOW TO PASS FROM STATISTICS TO COMPLETE DAILY RAINFALL TIMESERIES?
Stochastic rainfall model - Neyman-Scott Rectangular Pulses (NSRP) model
RAINFALL GENERATOR
MODEL PARAMETERS
Storm origin time (λ) [h -1 ]
Number of raincells (ν) [-]
Raincell origin delay (β) [h -1 ]
Raincell intensity (ξ) [h/mm]
Raincell duration (η) [h -1 ]
Fitted from rainfall monthly statistics
Arriving as a Poisson process
Simulation as long as desired

Conclusions
• General setting
• Testing and implementing more interpolation methods
• Implementing quantitative RCM skill score
• Rainfall generator outputs
• Extension of the work to other variables (e.g. temperature)
ANY QUESTION OR SUGGESTION IS WELCOME
• Adriana Bruggeman
• Panos Hadjinicolau
• Evangelos Tyrlis
• Andries De Vries
ACKNOWLEDGEMENTS
• Nick Polydorides
• Hakan Djuma
• Stelios Pashiardis

References
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