[American Society of Civil Engineers World Environmental and Water Resources Congress 2008 -...

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Evaluation of uncertainties in Downscaling precipitation due toClimate change scenarios

Mohammad Karamouz1, Fellow ASCE, Sara Nazif2, Sanaz Imen3, Mahdis Fallahi41Professor, School of Civil Engineering, University of Tehran, Email: [email protected]

2 Ph.D. candidate, School of Civil Engineering, University of Tehran, Email: [email protected] M.Sc., School of Civil Engineering, University of Tehran, Email: [email protected]

4 M.Sc., School of Civil Eng., Amirkabir Univ. of Tech., Tehran, Iran, [email protected]

Abstract:There are considerable uncertainties in precipitation downscaling especially inconsidering climate change scenario effects. Evaluation of these uncertainties playsan important role in Integrated Water Resources Management (IWRM). GlobalClimate Models (GCMs) are the primary tools for climate change. There isconsiderable uncertainty in GCM simulations of climate change associated with: (i)uncertainty in future green house gas emissions and cycles that are usually simulated‘off-line’ (ii) uncertainty in the GCM response to model structure, parameterization,and spatial resolution. In this paper, the effects of using different climate changescenarios for precipitation downscaling are evaluated. Uncertainties in thedownscaling model are also dependent on the input data and available observationsfor model calibration. For assessment of this aspect of uncertainties in precipitationdownscaling, different periods of available data are used for model calibration. Afterdeveloping 100 sets of ensemble data of downscaled precipitation for one hundredyears, the probability distributions of downscaled values are determined andcompared with the observed values. The results show that there are considerableuncertainties associated with the climate change scenarios and the input data of themodel.

Keywords: Downscaling, Uncertainty, Precipitation, Probability, Climate Change.

IntroductionPopulation growth and industrial and agricultural development cause societies to faceserious challenges in allocating scarce water resources to increasing water demandsin arid and semi arid regions. Furthermore, massive industrialization and theextended use of fossil fuels have lead to a great increase in the atmosphericconcentrations of greenhouse gases. This affects global climate dynamics andchanges land atmosphere interactions at unprecedented scales (IPCC, 2001).Assessment of the long-term impacts of climate change on hydro-climatologicvariables is essential in planning water resources. Current water resources

World Environmental and Water Resources Congress 2008 Ahupua'a © 2008 ASCE

Copyright ASCE 2008 World Environmental and Water Resources Congress 2008: Ahupua'a World Environmental and Water Resources Congress 2008

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management policies are relatively effective in handling inter-annual variability.However, enough considerations have not been given to long term trends untilclimate change impacts began to be felt as an immediate crisis.While GCMs account for the dynamics of global circulation patterns and the earthsurface-atmosphere system, their results are too coarse to be used for hydrologicprocesses. They need to be downscaled for local spatial and temporal meteorologicalstudies. The main downscaling methodologies can be classified as dynamicdownscaling with limited area regional climate models and statistical downscaling.Wilby et al. (1999) compared the capabilities of two types of precipitation andtemperature data that can be used in hydrological models. The first data are theresults of a statistical downscaling model and the second data are raw and elevationcorrected GCM outputs. They concluded that statistical downscaling may result inbetter hydrologic simulations, and it is an adequate way to recover the missing data.The statistical downscaling has been done in this paper by SDSM (StatisticalDownScaling Model) which can develop ensemble data for the prediction purpose.There are considerable uncertainties associated with the downscaling procedure.Uncertainties come from a range of sources such as 1) lack of adequate knowledgeabout the characteristics of system interactions 2) sampling and data collection errorsand 3) influences of climate and human responses. The engineers and decisionmakers benefit from the knowledge of expected accuracy of forecasts. Forecastaccuracy varies both spatially and temporally as a result of initial state and modelingerrors.Schmidli et al. (2005) examined the uncertainty in the procedure of downscaling, bycomparing several different downscaling models. Sajjad Khan et al. (2005) comparedthree downscaling models, namely the Statistical Down-Scaling Model (SDSM), theLong Ashton Research Station Weather Generator (LARS-WG) model and anartificial neural network (ANN) model, in terms of various uncertainties that affecttheir downscaled results. According to the evaluation of variability and uncertainty,the performances of the LARS-WG model and the SDSM are almost similar,whereas the ANN model performance is found to be poor in that case. Hardenberg etal. (2007) explored the sources of forecast uncertainty in a stochastic ensembleprediction series for small-scale precipitation. They showed that, in currentoperational configurations, small and large-scale uncertainties are the same. Dibikeet al. (2007) used uncertainty analysis to make a quantitative evaluation of thereliability of statistically downscaled climate data representing local climateconditions in northern Canada. They showed that downscaling results using theregression-based statistical downscaling method driven by accurate GCM predictorsare able to reproduce the climate regime over the study area. It has been stated thatensemble spread should provide a measure of forecast uncertainty (Kalnay andDalcher 1987; Murphy 1988; Houtekamer 1996), such that high/low spread eventscorrespond to high/low forecast errors. This relationship is quantified by finding thecorrelation between a measure of ensemble spread and the accuracy of a particulardeterministic forecast. It has been demonstrated that ensemble forecast systems candescribe forecast uncertainty associated with errors in the initial conditions.This paper deals with uncertainties in precipitation downscaling in both the inputdata and modeling procedure. For this purpose, different input data is considered for



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model calibration and 100 ensembles of precipitation data for 100 years aredeveloped. The uncertainty of outputs is evaluated using a probabilistic approach.The paper gives a description of the methodology and then, a short explanation aboutthe study area is given. Finally the results of study are discussed and a summary andconclusion is given.

MethodologyGCMs are run at low resolution and their output is needed to be downscaled forindividual sites. Downscaling enables the construction of climate change scenariosfor individual sites at daily time scales, using grid resolution GCM outputs. Methodsthat are used for uncertainty quantification in the precipitation downscalingprocedure need to cope with the sensitive dependence on initial conditions and theinteraction of many spatial and temporal scales. Moreover, they should account forthe fact that the sources of uncertainty could also be uncertain. There are two maintypes of uncertainty in precipitation downscaling. The first group arises fromuncertainties in input data and the second group is related to uncertainties in themodeling procedure. In this paper, the uncertainties in the input data are evaluated byconsidering two different climate change scenarios namely Had A2 and Had B2, andtwo different calibration periods, 1971-1991 and 1971-2000. The Had A2 scenarioassesses reinforcing forces that support the increasing population based on familyvalues and traditions without relying on a lifestyle that depends on economicaladvancement. However in the Had B2 scenario, more emphasis is given toreinforcing economical, social and environmental issues. Both of them are regionalscenarios of climate change.The ensemble data has been generated to consider uncertainties in the modelingprocedure. Statistical consistency implies a relationship between the expectedvariance of the ensemble mean error with the dispersion of the ensemble. Thedispersion of the ensemble is quantified by:

jxN

xN

sN

i

N

kjkjij ∀−= ∑ ∑

= =1 1

2,,

2 )1

(1

(1)

and the prediction error of each ensemble data is formulated as:

jxN

xN

kjkjTj ∀−= ∑

=1

2,,

2 )1

(ε (2)

s and ε are named RMS (Root Mean Square) spread and RMS error, respectively. xT,j

is the true state in the jth case and N is the number of ensemble members. Ensemblemembers, x1,j, . . . ,xN,j, are independent draws from the pdf pj, which has mean µj

and standard deviation σj. The RMS spread and RMS error indices are used forevaluation of uncertainties in precipitation downscaling using the ensemble approach(Leutbecher and Palmer, 2007).

Study areaThe study area of this paper is the Ahar-chay basin, located between 47° 20' and 47°30' north longitude and 38° 20' and 38°45' east latitude, in northwestern Iran. Thelocation of the study area on the Iran map has been shown in Figure 1. This basinrelies on the Ahar River for its water supply. The Ahar River flow is highly



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dependent on seasonal rainfalls; therefore, the prediction of precipitation changes inthe future under climate change effects is very important for future water resourcesmanagement. There are three climatologic stations in the study area includingKasanagh, Kasin and Varzaghan whose characteristics are shown in Table 1.Kasanagh station has been considered in this study for precipitation downscalingbecause of its longest time series. The observed climatic signals for years 1971 to2000 and also GCM simulations under two climate change scenarios namely Had A2and Had B2 are downloaded from the website of the Canadian Climate ImpactsScenarios Group (CCISG).

Figure 1. The location of the study area on the Iran map and climatologic stations of the basin

Table 1. The characteristics of climatologic stations in Ahar-chay basin

VarzaghanKasinKasanaghStation

39'-°4635'-°4651'-°46Longitude-31'°38-31'°38-31'°38Latitude

282269288Average of yearly precipitation (mm)

1971-20001997-19881971-2000Operation time period (year)

ResultsThe SDSM model has been run for two climate change scenarios with twocalibration periods of 1971-1991 and 1971-2000. In each run, SDSM model develops100 sets of ensemble data. SDSM gives results in a daily time scale therefore theresults are aggregated in monthly and seasonal (calendar season) scales. The rootmean square error (RMSE) for downscaled precipitations in each run, in monthly andseasonal time scales, has been calculated and presented in Table 2. It shows that themodels calibrated for 1971-2000, have higher RMSE in both seasonal and monthlytime scales. This can be due to significant missing data between years 1991 and2000.In each monthly or seasonal time step, a normal cumulative density function (CDF)

Varzaghan KasanaghVarzaghan

Kasin



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has been fitted to the downscaled ensemble data. The observed values in historicaldata are compared with the normal CDF of downscaled precipitation values and theresults are shown in Table 3. According to this table, the seasonal predicted values ofthe Had B2 scenario calibrated in 1971-2000 are the most consistent predictionsbecause of having approximately similar percentages for different probability ranges.The other interesting result of this table is that most of the models with lowerpercentages fill in the range of 0.5-0.75 of probability of occurrence. For example, inFigure 2 the observed and downscaled precipitation CDFs for four downscalingmodels, in monthly and seasonal time scales are compared. The seasonal CDFs arefar from each other so each amount of predictions has a wide range of probability ofoccurrence but the probability ranges in a monthly scale are narrower. These resultsindicate that seasonal downscaled precipitations are more uncertain.

Table 2. The RMSE for different SDSM simulationsTime scale Scenario Calibration period RMSE (mm)Monthly Had A2 1971-2000 31.60Monthly Had A2 1971-1991 21.72Monthly Had B2 1971-2000 33.50Monthly Had B2 1971-1991 22.74Seasonal Had A2 1971-2000 67.09Seasonal Had A2 1971-1991 43.88Seasonal Had B2 1971-2000 68.23Seasonal Had B2 1971-1991 38.64

Table 3. The percentages of ensemble members with different probabilities of occurrenceTime scale Scenario Calibration

period X<0.25 0.25<X<0.5 0.5<X<0.75 0.75<X<1Monthly Had A2 1971-2000 0.33 0.28 0.21 0.18Monthly Had A2 1971-1991 0.34 0.26 0.18 0.23Monthly Had B2 1971-2000 0.32 0.32 0.16 0.20Monthly Had B2 1971-1991 0.33 0.27 0.17 0.24Seasonal Had A2 1971-2000 0.35 0.24 0.20 0.21Seasonal Had A2 1971-1991 0.30 0.24 0.24 0.23Seasonal Had B2 1971-2000 0.25 0.25 0.24 0.26Seasonal Had B2 1971-1991 0.45 0.18 0.13 0.24

In the next step, mean of RMS spread and error in the study period are calculated andpresented in Table 4. The models calibrated for years 1971-1991 have less RMSspread and error in comparison with models calibrated for years 1971-2000.Therefore, the models calibrated for years 1971-1991 are more certain and accurate.It can be concluded that the downscaled data using the Had B2 scenario (1971-2000)are the most uncertain and inaccurate data in both monthly and seasonal scalebecause of higher values of RMS spread and error. The relation between RMS spreadand error for scenario Had A2 calibrated in 1971-2000 in monthly and seasonal time



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(a)

0

20

40

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0 10 20 30 40 50 60RMS spread

RM

Ser

ror

(b)

0

20

40

60

80

100

120

0 10 20 30 40 50 60RMS spread

RM

Ser

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scales are shown in Figure 3. There is almost a linear relation with RMS spread anderror as it is expected.

Figure 2. The comparison of observed and downscaled precipitations in (a) seasonaland (b) monthly time scales

Table 4. RMS spread and error for downscaled ensemble dataTime scale Scenario Calibration period RMS error RMS spreadMonthly Had A2 1971-2000 19.20 17.86Monthly Had A2 1971-1991 14.94 15.06Monthly Had B2 1971-2000 20.04 18.21Monthly Had B2 1971-1991 15.76 15.04Seasonal Had A2 1971-2000 42.80 32.14Seasonal Had A2 1971-1991 29.97 26.41Seasonal Had B2 1971-2000 43.28 32.86Seasonal Had B2 1971-1991 31.12 27.58

Figure 3. The relation between RMS spread and error for scenario Had A2 calibratedin 1971-2000 in (a) monthly (b) seasonal time scale

Summary and conclusionThe GCMs outputs are used for evaluation of future changes in precipitation andother hydro-climatic variables. In this study, the GCM outputs are downscaled usingthe SDSM model for the Ahar-chay basin in Iran. The effects of uncertainties in the

(b)

0

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0 20 40 60 80 100 120

Monthly rainfall (mm) (Nov1989)

Pro

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had A2 (1971-1991)

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input and modeling procedure of precipitation downscaling are evaluated bydeveloping ensemble data and fitting CDFs to them. The climate change scenarioeffects on precipitation downscaling are also considered using Had A2 and Had B2scenarios. The uncertainties in ensemble forecast are evaluated using RMS spreadand error. It seems that the results of scenario Had B2 are more uncertain than HadA2. Also, using the monthly time series could result in obtaining more certain resultsthan using seasonal time series. The results of this study show the importance ofconsidering uncertainties in precipitation downscaling. This information could helpthe decision making in climate and water resources studies.

ReferencesDibike, Y.B., Gachon, P., St-Hilaire, A., Ouarada, T.B.M.J., Nguyen, van T.V.,(2007), "Uncertainty Analysis of Statistically Downscaled Temperature andPrecipitation Regimes in North Canada", Journal of Theoretical and AppliedClimatology.

Hardenberg, J., Ferraris, L., Rebora, N., Provenzale, A., (2007), " MeteorologicalUncertainty and Rainfall Downscaling", Journal of the European Sciences Union,Vol.14, P.P. 193-199

Houtekamer, P. L., L. Lefaivre, J. Derome, H. Ritchie, and H. L. Mitchell, (1996), "A system simulation approach to ensemble prediction", Mon. Wea. Rev., 124, 1225–1242.

IPCC. (2001a). Climate change 2001. The science of climate change. Contribution ofworking group I to the second assessment report of the intergovernmental panel onclimate change. eds. Houghton, J.T., Filho, L.G.M., Callander, B.A., Harris, N.,Attenberg, A. and Maskell K., 572 pp. Cambridge University Press, Cambridge.

Kalnay, E. and A. Dalcher, (1987)," Forecasting forecast skill", Mon. Wea. Rev.,115, 349–356.

Leutbecher, M. and T.N. Palmer, (2007), "Ensemble forecasting", Elsevier, Journalof Computational Physics.

Murphy, J., (1988)," Impact of ensemble forecasts on predictability". Quart. J. Roy.Meteor. Soc., 114, 463–493

Sajjad Khan, M., Coulibaly, P., Dibike, Y., (2005), "Uncertainty Analysis ofStatistical Downscaling Methods", Journal of Hydrology, Vol. 319, pp. 357-382

Schimidi, J., Goodess, C.M., Frei, C., Haylock, M.R., Hundecha, Y., Ribalaygua, J.,Schmith, T., (2005), "Statistical and Dynamical Downscaling of precipitation: AnEvaluation and Comparison of Scenarios for the European Alps", Journal ofGeophysical Reserch



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Wilby, R.L., Hay, L.E., Leavesley, G.H.,(1999) "A Comparison of Downscaled andGCM Output: Implications for Climate Change Scenarios in the San Juan RiverBasin,Colorado", Journal of Hydrology , pp. 67-91



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