Extreme Precipitation in a Changing Climate: Ankara Case Study Middle - - PowerPoint PPT Presentation

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Extreme Precipitation in a Changing Climate: Ankara Case Study

Middle East Technical University Graduate School of Natural and Applied Sciences Earth System Science (ESS) Interdisciplinary Program

Sertaç Oruç1, İsmail Yücel 1, 2 and Ayşen Yılmaz1, 3

1 Earth System Science (ESS) Interdisciplinary Program, Middle East Technical University 2 Dept. of Civil Eng., Water Resources Division, Middle East Technical University 3 Institute of Marine Sciences, Middle East Technical University

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Outline

 Motivation  Objectives  Methodology  Observed Precipitation Data Analyses  Projected Precipitation Data Analyses  Summary and Conclusions

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Motivation: Climate Change

Global Precipitation Change (For the last 2 decades and Projected Periods) Low (RCP 2.6) ensemble average (dark line) and spread of ensemble members (shaded area). Values are for the model grid cell containing: 39.912°N 32.84°E High (RCP 8.5) ensemble average (dark line) and spread of ensemble members (shaded area). Values are for the model grid cell containing: 39.912°N 32.84°E.

https://gisclimatechange.ucar.edu/inspector

RCP : Representative Concentration Pathways

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Figure 1.1. Annual count of extreme events in Turkey in the period of 1940-2017 Figure 1.2. Distribution of extreme events and their types in Turkey in 2017

Motivation: Climate Change and Extreme Events

Annual count of extreme events in Turkey shows an increasing trend in 1940-2017 period (Climate Assessment 2017 Report, February 2018 – State Meteorological Service). During 2017 most hazardous extreme events were; heavy rain/floods (31%), wind storm (36%), hail (16%), heavy snow (7%), and lightning (4%)

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Problem Statement

• To analyze the rainfall extreme value frequencies for stationary and nonstationary conditions in Ankara region,
• To produce Return Levels in stationary and non-stationary conditions with observed data and future projections,.
• To figure out the superiority of nonstationary and stationary models to each other,
• Climate change in Turkey has been evaluated in many different studies with its different aspects. Majority of analysis

performed and the future estimation works were focused on temperature and precipitation changes which are the most important climate parameters causing the extreme events.

• In the last decades, heavy rainfall and flash flooding caused various damages in Turkey; for example settlements were

damaged, road transportation and vehicles are disrupted, and life was negatively affected in Ankara

Objectives

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The methodology of precipitation analysis in this study consists of; (1) Trend analysis is carried out for observed (1950-2015) and projected data (2015-2098) (2) Projected data is disaggregated into finer scales (5 min) and then it is aggregated to next analysis time scales (10, 15, and 30 min, …) (3) Stationary GEV (St) models are developed, return levels are derived for desired return periods considering single and multi-time periods for observed and single period for projected data (4) Non-stationary GEV (NSt) models are developed, return levels are derived for desired return periods for observed and projected data (5) Stationary and Non-stationary model results were compared

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Methodology and Data

• Observed Data for Ankara - 1950-2015 (State Meteorological Services)
• Projected Data; Three global climate models (GCM) are used; namely HadGEM2-ES, MPI-ESM-MR and GFDL-
• ESM2M. These models are operated with the RCP 4.5 and RCP 8.5 emission scenarios - 2015-2098 (State

Meteorological Services)

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Rainfall Data Observed Trend Analysis GEV Models Stationary Return Levels for Desired Storm Durations and Periods Nonstationary Mean/Median of Return Levels for Desired Storm Duration Define Covariates Obatining Blocks Projected Disaggregation Aggregation Obatining Blocks GEV Models Stationary Return Levels for Desired Storm Durations and Periods Nonstationary Mean/Median of Return Levels for Desired Storm Duration Define Covariates

Figure 1.3. Rainfall Data Analyses Framework

Methodology

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Trends & Change Point

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Observed Data

Figure 1.4. Sub-Hourly Time Series Trend Figure 1.5. Hourly Time Series Trend Figure 1.6. Average annual maximum rainfall intensities (mm) for sub-hourly and hourly storm durations

5 10 15 20 25 30 35 40 45 50 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Precipitation Height (mm) Years 5 min 10 min 15 min 30 min 10 20 30 40 50 60 70 80 90 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Precipitation Height (mm) Years 1 hour 2 hours 3 hours 6 hours 10 20 30 40 50 60 70 80 90 5 min 10 min 15 min 30 min 1 hour 2 hours 3 hours 6 hours Precipitation Height mm Storm Duration Average 1950-1975 Maximum 1950-1975 Average 1976-2015 Maximum 1976-2015 Average 1950-2015 Maximum 1950-2015

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9 Model Location Scale Shape NStGEV1 𝜈t =𝛾0 +𝛾1t 𝜏 (constant) 𝜊 (constant) NStGEV2 𝜈t =𝛾0 +𝛾1t 𝜏t =𝛾0 +𝛾1t 𝜊 (constant) NStGEV3 𝜈 (constant) 𝜏t =𝛾0 +𝛾1t 𝜊 (constant) NStGEV4 𝜈t =𝛾0 +𝛾1temperature 𝜏 (constant) 𝜊 (constant) NStGEV5 𝜈t =𝛾0 +𝛾1t 𝜏t =𝛾0 +𝛾1exp(temperature) 𝜊 (constant) NStGEV6 𝜈t =𝛾0 +𝛾1exp(temperature) 𝜏t =𝛾0 +𝛾1exp(temperature) 𝜊 (constant) NStGEV7 𝜈t =𝛾0 +𝛾1exp(temperature) 𝜏t = (constant) 𝜊 (constant) NStGEV8 𝜈 (constant) 𝜏t =𝛾0 +𝛾1temperature 𝜊 (constant)

Table 1.1. Non-stationary models with time and covariate (temperature) dependent location and scale parameters

Stationary Models (St) 5 Minutes 10 Minutes 15 Minutes 30 Minutes 1 Hour 2 Hours 3 Hours 6 Hours NonStationary Models (NSt) 5 Minutes 10 Minutes 15 Minutes 30 Minutes 1 Hour 2 Hours 3 Hours 6 Hours

Figure 1.7. Storm Durations Used for Stationary Models

2-year 5-year 10-year 25-year 50-year 100-year 200-year

Mean Value Change

FiveMin

• 4%
• 4%
• 5%
• 9%
• 11%
• 15%
• 18%

TenMin

• 14%
• 13%
• 12%
• 9%
• 7%
• 5%
• 3%

FifteenMin

• 1%
• 4%
• 6%
• 9%
• 12%
• 14%
• 17%

ThirtyMin 0%

• 3%
• 6%
• 10%
• 13%
• 16%
• 19%

OneHour

• 7%
• 5%
• 3%

0% 3% 6% 10% TwoHours 0%

• 3%
• 4%
• 5%
• 6%
• 7%
• 7%

ThreeHours 0%

• 3%
• 5%
• 8%
• 11%
• 13%
• 16%

SixHours 1%

• 1%
• 2%
• 3%
• 4%
• 5%
• 5%

Median Value Change

FiveMin

• 3%
• 2%
• 4%
• 7%
• 9%
• 12%
• 15%

TenMin

• 13%
• 12%
• 10%
• 7%
• 4%
• 2%

0% FifteenMin

• 1%
• 2%
• 4%
• 7%
• 9%
• 12%
• 14%

ThirtyMin 1%

• 2%
• 5%
• 8%
• 10%
• 13%
• 16%

OneHour

• 8%
• 5%
• 2%

2% 5% 8% 12% TwoHours

• 1%
• 2%
• 3%
• 4%
• 4%
• 5%
• 5%

ThreeHours

• 1%
• 2%
• 4%
• 7%
• 9%
• 11%
• 14%

SixHours 0%

• 1%
• 2%
• 2%
• 3%
• 3%
• 4%

Table 1.2. Nonstationary GEV Best Fit Model Return Levels (mm) - Mean and Median Value Change with Respect to Stationary GEV Model

Observed Data

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Observed Data

• The shorter the storm duration the larger the differences between the non-stationary and stationary extremes.
• Among the storm durations, only one hour time series exhibit larger values for its nonstationary model return level

values, however this is not valid for shorter return periods such as 5 years or 20 years

• Sub-hourly storm durations indicate larger difference than hourly storm durations and non-stationary estimates are

smaller than their corresponding stationary values

Figure 1.8. Stationary and Best Fit Nonstationary Model Return Level (mm) Comparison - Return Period vs. Return Level

5 10 15 20 2 5 10 20 25 50 100 200 Return Level mm Return Period (Years) StFiveMin NStFiveMin 5 10 15 20 25 30 2 5 10 20 25 50 100 200 Retun Level mm Return Period (Years) StTenMin NStTenMin 10 20 30 40 2 5 10 20 25 50 100 200 Return Level mm Return Period (Years) StFifteenMin NStFifteenMin 10 20 30 40 50 60 70 2 5 10 20 25 50 100 200 Return Level mm Return Period (Years) StThirtyMin NStThirtyMin 20 40 60 80 100 2 5 10 20 25 50 100 200 Return Level mm Return Period (Years) StOneHour NStOneHour 20 40 60 80 100 2 5 10 20 25 50 100 200 Return Level mm Return Period (Years) StTwoHours NStTwoHours 20 40 60 80 100 2 5 10 20 25 50 100 200 Return Level mm Return Period (Years) StThreeHours NStThreeHours 20 40 60 80 100 2 5 10 20 25 50 100 200 Return Level mm Return Period (Years) StSixHours NStSixHours

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(Non) Stationary ((N)St) 10 Minutes MPI-ESM-MR (MPI) RCP 4.5 RCP 8.5 HADGEM2-ES (HG) RCP 4.5 RCP 8.5 GFDL-ESM2M (GFDL) RCP 4.5 RCP 8.5 15 Minutes MPI RCP 4.5 RCP 8.5 HG RCP 4.5 RCP 8.5 GFDL RCP 4.5 RCP 8.5 1 Hour MPI RCP 4.5 RCP 8.5 HG RCP 4.5 RCP 8.5 GFDL RCP 4.5 RCP 8.5 6 Hours MPI RCP 4.5 RCP 8.5 HG RCP 4.5 RCP 8.5 GFDL RCP 4.5 RCP 8.5 Figure 1.9. Projected Storm Durations Used for Stationary Models for 2015-2098 period

Projected Data

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Projected Data: Trends

Figure 1.10. Projected 10-15 Minutes (a,b) and 1-6 Hours (c,d) Annual Maximum Time Series for 2015-2098

(a) (b) (c) (d)

2 4 6 8 10 12 14 16 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078 2081 2084 2087 2090 2093 2096 Height mm MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG 4.5 HG 8.5 5 10 15 20 25 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078 2081 2084 2087 2090 2093 2096 Heihgt mm MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG10 4.5 HG10 8.5 5 10 15 20 25 30 35 40 45 50 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078 2081 2084 2087 2090 2093 2096 Height mm MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG10 4.5 HG10 8.5 10 20 30 40 50 60 70 80 2015 2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078 2081 2084 2087 2090 2093 2096 Height mm MPI 4.5 MPI 8.5 GFDL 4.5 GFDL 8.5 HG10 4.5 HG10 8.5

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Return Period

• Years

2 5 10 25 50 100 200 2 5 10 25 50 100 200 Model

Mean Value Change Median Value Change MPI45 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% MPI85 0%

• 1%
• 1%
• 2%
• 2%
• 2%
• 2%

1% 0%

• 1%
• 1%
• 2%
• 2%
• 2%

GFDL45

• 1%
• 1%

0% 0% 1% 1% 2% 1% 1% 1% 1% 2% 2% 3% GFDL85 1% 0% 0%

• 1%
• 1%
• 2%
• 2%

2% 2% 2% 2% 2% 2% 1% HG45 0%

• 1%

0% 1% 2% 2% 4% 0%

• 1%

0% 1% 2% 2% 4% HG85 0% 0% 0% 0% 0% 0% 1%

• 1%
• 1%
• 1%
• 1%

0% 0% 0% Mean Value Change Median Value Change MPI45 0% 0% 0% 0% 1% 2% 3% 0% 0% 0% 1% 1% 2% 3% MPI85 0%

• 1%
• 1%
• 2%
• 2%
• 2%
• 3%

1% 0%

• 1%
• 1%
• 2%
• 2%
• 2%

GFDL45

• 1%
• 1%
• 1%

0% 0% 0% 1%

• 1%
• 1%
• 1%

0% 0% 0% 1% GFDL85 0% 0%

• 1%
• 1%
• 1%
• 1%
• 1%

0% 0% 0% 0%

• 1%
• 1%
• 1%

HG45

• 1%
• 1%

0% 2% 3% 5% 6%

• 1%
• 1%

0% 2% 3% 5% 6% HG85 0% 0% 0% 0% 0% 0% 1%

• 2%
• 1%
• 1%
• 1%
• 1%

0% 0% Mean Value Change Median Value Change MPI45

• 1%
• 1%
• 1%
• 1%
• 2%
• 2%
• 2%
• 1%
• 1%
• 1%
• 1%
• 2%
• 2%
• 2%

MPI85 0%

• 2%
• 2%
• 1%
• 1%

0% 1% 1%

• 1%
• 2%
• 1%

0% 1% 2% GFDL45

• 1%

0% 0% 1% 2% 3% 4%

• 1%

0% 0% 1% 2% 3% 4% GFDL85 0% 0%

• 1%
• 2%
• 3%
• 3%
• 4%

2% 2% 1% 1% 0% 0%

• 1%

HG45 0%

• 1%
• 1%

0% 0% 0% 1% 0%

• 1%
• 1%

0% 0% 0% 1% HG85 0%

• 1%
• 1%
• 1%
• 1%
• 1%
• 1%
• 3%
• 3%
• 2%
• 2%
• 2%
• 2%
• 2%

Mean Value Change Median Value Change MPI45 0%

• 1%
• 1%
• 2%
• 2%
• 3%
• 4%

0%

• 1%
• 1%
• 2%
• 2%
• 3%
• 4%

MPI85 2% 2% 1% 0% 0%

• 1%
• 2%

2% 3% 3% 3% 3% 2% 2% GFDL45 1% 0%

• 2%
• 5%
• 7%
• 10%
• 13%

1%

• 1%
• 3%
• 6%
• 9%
• 12%
• 15%

GFDL85 0%

• 2%
• 3%
• 5%
• 7%
• 9%
• 12%

1% 0%

• 1%
• 3%
• 4%
• 5%
• 7%

HG45 0%

• 1%
• 3%
• 5%
• 7%
• 9%
• 11%

2% 2% 1%

• 1%
• 2%
• 4%
• 6%

HG85 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1%

Table 1.3. Nonstationary Mean and Median Value Change with Respect to Stationary Model - Projected Data

Ten Minutes Fifteen Minutes One Hour Six Hours 2-year 1%

• 5%

0% 1% 5-year 0%

• 7%
• 1%

0% 10-year -1%

• 9%
• 1%
• 1%

25-year -2%

• 10%
• 1%
• 2%

50-year -3%

• 11%

0%

• 4%

100-year -3%

• 12%

0%

• 5%

200-year -4%

• 12%

1%

• 6%

Table 1.4. Nonstationary Model-Stationary Comparison for Projected Data - Average Values

Projected Data

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Figure 1.11. Stationary Model Results for Projected Time Series

Projected Data

5 10 15 20 25 30 Return Level mm Return Period StTenMinMPI45 StTenMinMPI85 StTenMinGFDL45 StTenMinGFDL85 StTenMinHG45 StTenMinHG85 Average Max 5 10 15 20 25 30 35 Return Level mm Return Period StFifteenMinMPI45 StFifteenMinMPI85 StFifteenMinGFDL45 StFifteenMinGFDL85 StFifteenMinHG45 StFifteenMinHG85 Average Max 5 10 15 20 25 30 35 40 45 50 Return Level mm Return Period StOneHourMPI45 StOneHourMPI85 StOneHourGFDL45 StOneHourGFDL85 StOneHourHG45 StOneHourHG85 Average Max 10 20 30 40 50 60 70 80 90 100 Return Level mm Return Period StSixHoursMPI45 StSixHoursMPI85 StSixHoursGFDL45 StSixHoursGFDL85 StSixHoursHG45 StSixHoursHG85 Average Max

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On average nonstationary models produce mostly lower return levels for mid and longer return periods for all durations and similar results for short (2 and 5 years) return periods except one hour storm duration.

Projected Data

Figure 1.12. 10-15 Minutes and 1-6 Hours Ensemble Model Comparison for Projected Data

5 10 15 20 25 30 2-year 5-year 10-year 20-year 25-year 50-year 100-year200-year Return Level mm NStTenMinAvrg NStTenMinMax StTenMinAvrg StTenMinMax 5 10 15 20 25 30 35 40 Return Level mm NStFifteenMinAvrg NStFifteenMinMax StFifteenMinAvrg StFifteenMinMax 5 10 15 20 25 30 35 40 45 50 Return Level mm NStOneHourAvrg NStOneHourMax StOneHourAvrg StOneHourMax 20 40 60 80 100 120 Return Level mm NStSixHoursAvrg NStSixHoursMax StSixHoursAvrg StSixHoursMax

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St St St St St St St SMS SMS SMS SMS SMS SMS SMS NStMean NStMean NStMean NStMean NStMean NStMean NStMean NstMedian NstMedian NstMedian NstMedian NstMedian NstMedian NstMedian

• 5,00

10,00 15,00 20,00 25,00 30,00 2-year 5-year 10-year 25-year 50-year 100-year 200-year St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85 StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45Mean NStGFDL45Median NStGFDL85Mean NStGFDL85Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg

Projected Data

Figure 1.15. Ten Minutes Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data

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Projected Data

Figure 1.16. Fifteen Minutes Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data

St St St St St St St SMS SMS SMS SMS SMS SMS SMS NStMean NStMean NStMean NStMean NStMean NStMean NStMean NstMedian NstMedian NstMedian NstMedian NstMedian NstMedian NstMedian

• 5,00

10,00 15,00 20,00 25,00 30,00 35,00 40,00 2-year 5-year 10-year 25-year 50-year 100-year 200-year St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85 StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45445Mean NStGFDL454Median NStGFDL854Mean NStGFDL854Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg

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St St St St St St St SMS SMS SMS SMS SMS SMS SMS NStMean NStMean NStMean NStMean NStMean NStMean NStMean NstMedian NstMedian NstMedian NstMedian NstMedian NstMedian NstMedian

• 10,00

20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00 2-year 5-year 10-year 25-year 50-year 100-year 200-year St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85 StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45Mean NStGFDL45Median NStGFDL85Mean NStGFDL85Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg

Projected Data

Figure 1.17. One Hour Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data

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St St St St St St St SMS SMS SMS SMS SMS SMS SMS StMPI45 StMPI45 StMPI45 StMPI45 StMPI45 StMPI45 StMPI45 NStMPI45Mean NStMPI45Mean NStMPI45Mean NStMPI45Mean NStMPI45Mean NStMPI45Mean NStMPI45Mean NStMPI45Median NStMPI45Median NStMPI45Median NStMPI45Median NStMPI45Median NStMPI45Median NStMPI45Median

• 10,00

20,00 30,00 40,00 50,00 60,00 70,00 80,00 90,00 100,00 2-year 5-year 10-year 25-year 50-year 100-year 200-year St SMS NStMean NstMedian StMPI45 StMPI85 StGFDL45 StGFDL85 StHG45 StHG85 NStMPI45Mean NStMPI45Median NStMPI85Mean NStMPI85Median NStGFDL45Mean NStGFDL45Median NStGFDL85Mean NStGFDL85Median NStHG45Mean NStHG45Median NStHG85Mean NStHG85Median StProjectedAvg NStProjectedAvg

Projected Data

Figure 1.18. Six Hours Data Model Comparison - Best Fit Nst and St for Observed and Projected Data and SMS (State Meteorological Service) Data

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Summary and Conclusions:

• Stationary GEV models were capable of fitting extreme rainfall data for all durations but the developed non-stationary

GEV models showed advantage over the stationary models

• The differences in design rainfall estimates between two time slice, entire period and nonstationary assumption models

support the need to update the current information, with the most recent data and approaches.

• The differences also reveal the need to conduct analysis using future climate data.
• Nonstationary model results are in general exhibited smaller return level values with respect to stationary model results of

each storm duration for the observed data driven model results.

• On average nonstationary models produce mostly lower return levels for mid and longer return periods for all durations

and similar results for short (2 and 5 years) return periods except one hour storm duration for the projected data.

• Almost all the nonstationary model maximum return level results are significantly higher than stationary model maximum

return level results for all storm durations and return periods for the projected data driven model results.

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References

1. Cheng, L. (2014). Frameworks for univariate and multivariate non-stationary analysis of climatic extremes, PhD. Dissertation, UC Irvine. 2. Cheng, L., & AghaKouchak, A. (2014). Nonstationary precipitation intensity-duration-frequency curves for infrastructure design in a changing climate. Sci.

• Rep. 4, 7093. doi:10.1038/srep07093

3. Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values, Springer, London. 4. Coles, S. G., & Sparks, R. S. J. (2006). Extreme value methods for modelling historical series of large volcanic magnitudes. Chapter 5, Statistics in Volcanology. 5. Efstratiadis, A., and D. Koutsoyiannis, An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, (2002). Proceedings

• f the Fifth International Conference on Hydroinformatics, Cardiff, UK, 1423-1428, International Water Association, (http: //itia.ntua.gr/el/docinfo/524/)

6. Gilleland, E., & Katz, R. (2016). extRemes 2.0: An Extreme Value Analysis Package in R. Journal of Statistical Software, 72(8), 1-39. doi: 10.18637/jss.v072.i08 7. Gilleland, E. (2016). Extreme Value Analysis, Package ‘extRemes’ 8. Kossieris, P., Makropoulos, C., Onof, C., & Koutsoyiannis, D. (2016a). A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett- Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980-992. 9. Kossieris, P., Makropoulos, C., Onof, C., & Koutsoyiannis, D. (2016b). HyetosMinute, A package for temporal stochastic simulation of rainfall at fine time scales, Version 2.0.

• 10. SMS, (2018). Republic of Turkey, the ministry of forestry and water affairs, state meteorological service, 2017 Annual Climate Assessment Report, February

2018