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Trends in daily rainfall erosivity in relation with NAO,
 MO and WeMO for NE Spain, during the period
 1955-2006.

Marta Angulo-Martinez
PhD student
Estación Experimental Aula Dei-CSIC,
Zaragoza, España
Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012
Outline

  Introduction:
       - What’s rainfall erosivity?
       - How to measure it
       - Database creation
       - Study area

  Trends in rainfall erosivity at NE Spain, 1955-2006

  Analysis of the relationship between some teleconnection
  patterns and rainfall erosivity

  Influence of the atmospheric teleconnection patterns
  evolution in explaining rainfall erosivity trends

  Conclusions


       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
The rainfall erosivity factor in soil erosion (EI)

Measures the rainfall energy or the ability of rainfall to erode soil.
Climate dynamics and
rainfall genetic mechanisms

                                                            Splash erosion & runoff erosion




          Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Measuring and modelling rainfall erosivity

                                                                                                   Where:
Empirical models:                                                                                  vr = Rainfall volume;
Universal Equation for Soil Erosion (R)USLE                                                        I30 = Maximum intensity in 30 min;
                                                                                                   er = Unit rainfall energy.
           m
   1 n j                                                   o       
R = ∑∑ ( EI 30 ) k                           EI = EI 30 =  ∑ er vr  I 30                    Empirically estimated with
   n j =1 k =1                                             r =1                             data from US

                                                                                   er = 0.29 [1 − 0.72 exp( −0.05ir )]
Limitations:
- High resolution temporal data (15’)                                     Other empirical equations
- Empiricism                                                             Cerro et al. (1998); Barcelona, Spain
 Depends on the physics of rainfall and                                     er ( Cr ) = 0.384 [1 − 0.538 exp( −0.029ir )]
 climate dynamics.
 Depends on the physico-chemical
 characteristics of the soil
                                                                         Van Dijk et al. (2002); Universal

                                                                             er (VD ) = 0.283 [1 − 0.52 exp( −0.042ir )]


           Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Measuring rainfall kinetic energy er and splash




      Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Relationships between kinetic energy (er) and intensity


 Logarithmic functions
      Ek = a + b log I

 Power-law functions
       Ek = a I b

 Exponential functions

EK = emax [1 − a exp (−b I )]

USLE proposal
er = 0.29 [1 − 0.72 exp( −0.05ir )]


              Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Daily Rainfall Erosivity Model

   1 n j
             m                                                                                                                    Databases
R = ∑∑( EI 30 ) k
   n j =1 k =1                                                                                                                    110 rainfall series
                                                                                                                                   15’ time resolution
              o       
EI = EI 30 =  ∑ er vr  I 30                              EI m = am P bm + ε                                                      Period 1997-2006
              r =1    
                                                                                                                                  156 rainfall series
er = 0.29 [1 − 0.72 exp( −0.05ir )]                                                                                                Daily time resolution
                                                                                                                                    Period 1955-2006




                                                                            R factor estimated MJ mm ha-1h-1 y-1




                                                                                                                   R factor observed MJ mm ha-1h-1 y-1



             Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Daily Rainfall Erosivity databases spatial distribution




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Daily Rainfall Erosivity vs. Rainfall (month by month)




      Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Trends in rainfall erosivity in relation with NAO, MO &
WeMO


Study case:


Trends in rainfall erosivity at NE Spain
during the period 1955-2006 in relation
with NAO, MO & WeMO



        Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Rainfall erosivity trends at annual scale




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Rainfall erosivity trends at seasonal scale




      Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Rainfall erosivity trends at seasonal scale




      Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Temporal evolution of rainfall erosivity quintile
occurrence




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Rainfall erosivity trends at daily scale; evolution of the
occurrence in low (Q1) and high (Q5) events.




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Could be these trends related with atmospheric
 mechanisms and teleconnection patterns evolution?
The characteristics of the rainfall erosivity events—Drop Size Distribution, Kinetic
Energy, Intensity and Duration—depend on the type of rainfall event (Van Dijk
et al. 2002), and in the meteorological and climatic characteristics involved in the
generation of the type of rainfall event:

     The leading atmospheric circulation patterns

     Sea level pressure fields

     Temperature contrasts between high and low atmospheric
     layers

     Humidity percentage in the air masses

     Wind flows

     Geographical factors


           Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Main atmospheric teleconnection patterns affecting the
climate of the study area




      Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Teleconnection indices circulation patterns in positive
       (above) and negative (beneath) phase
NAO+                                 MO+                                                     WeMO+




NAO-                                 MO-                                                     WeMO-




             Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Are teleconnection indices phases influencing daily
  rainfall erosivity?
 Differences in daily rainfall erosivity, negative and positive
  atmospheric teleconnection indices phases
                  Positive events = daily indices > 0.5
                    sd
                 Negative events = daily indices < -0.5 sd
 Relative difference of average daily rainfall erosivity of negative NAO
 days with respect to positive NAO days.

                                  EIdif = (EINAO- - EINAO+) / EINAO+

 where EINAO- (EINAO+) is the average daily rainfall erosivity of negative (positive) NAO days


Statistical significance checked by the Wilcoxon-Mann-Whitney test
month by month. Significance level α=0.05

              Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
NAO influencing daily rainfall erosivity

Relative difference of average daily rainfall erosivity of negative NAO days with respect to positive NAO days
  (MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between
  positive and negative NAO phases.




              Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
MO influencing rainfall erosivity

Relative difference of average daily rainfall erosivity of negative MO days with respect to positive MO days
  (MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between
  positive and negative MO phases.




              Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
WeMO influencing rainfall erosivity

Relative difference of average daily rainfall erosivity of negative WeMO days with respect to positive WeMO
days (MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between
positive and negative WeMO phases.




             Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Analysis of the probability of occurrence of extreme daily
rainfall erosivity records associated to negative and positive
teleconnection indices phase




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Temporal evolution of atmospheric teleconnection
indices and of rainfall erosivity quintile occurrence




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Conclusions


  Rainfall erosivity has decreased in the NE of the Iberian
  peninsula during the last 55 years

  Decrease occurs at annual and seasonal scale, though little
  increase occurs at summer and autumn


  Decrease is explained by a reduction in the high and extreme
  rainfall erosivity events occurrence , whilst low events have
  increased.

  This evolution seems related with positive trends in the
  atmospheric teleconnection indices: NAO, MO and WeMO.




       Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
Contact details:
Estación Experimental Aula Dei – CSIC, Zaragoza (ESPAÑA)/Departamento de Suelo y
Agua
Marta Angulo-Martinez
PhD Student

Email: mangulo@eead.csic.es




 Thanks!

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Trends in daily rainfall erosivity in relation with NAO, MO and WeMO for Spain, 1955-2006

  • 1. Trends in daily rainfall erosivity in relation with NAO, MO and WeMO for NE Spain, during the period 1955-2006. Marta Angulo-Martinez PhD student Estación Experimental Aula Dei-CSIC, Zaragoza, España Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012
  • 2. Outline Introduction: - What’s rainfall erosivity? - How to measure it - Database creation - Study area Trends in rainfall erosivity at NE Spain, 1955-2006 Analysis of the relationship between some teleconnection patterns and rainfall erosivity Influence of the atmospheric teleconnection patterns evolution in explaining rainfall erosivity trends Conclusions Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 3. The rainfall erosivity factor in soil erosion (EI) Measures the rainfall energy or the ability of rainfall to erode soil. Climate dynamics and rainfall genetic mechanisms Splash erosion & runoff erosion Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 4. Measuring and modelling rainfall erosivity Where: Empirical models: vr = Rainfall volume; Universal Equation for Soil Erosion (R)USLE I30 = Maximum intensity in 30 min; er = Unit rainfall energy. m 1 n j  o  R = ∑∑ ( EI 30 ) k EI = EI 30 =  ∑ er vr  I 30 Empirically estimated with n j =1 k =1  r =1  data from US er = 0.29 [1 − 0.72 exp( −0.05ir )] Limitations: - High resolution temporal data (15’) Other empirical equations - Empiricism Cerro et al. (1998); Barcelona, Spain Depends on the physics of rainfall and er ( Cr ) = 0.384 [1 − 0.538 exp( −0.029ir )] climate dynamics. Depends on the physico-chemical characteristics of the soil Van Dijk et al. (2002); Universal er (VD ) = 0.283 [1 − 0.52 exp( −0.042ir )] Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 5. Measuring rainfall kinetic energy er and splash Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 6. Relationships between kinetic energy (er) and intensity Logarithmic functions Ek = a + b log I Power-law functions Ek = a I b Exponential functions EK = emax [1 − a exp (−b I )] USLE proposal er = 0.29 [1 − 0.72 exp( −0.05ir )] Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 7. Daily Rainfall Erosivity Model 1 n j m Databases R = ∑∑( EI 30 ) k n j =1 k =1 110 rainfall series 15’ time resolution  o  EI = EI 30 =  ∑ er vr  I 30 EI m = am P bm + ε Period 1997-2006  r =1  156 rainfall series er = 0.29 [1 − 0.72 exp( −0.05ir )] Daily time resolution Period 1955-2006 R factor estimated MJ mm ha-1h-1 y-1 R factor observed MJ mm ha-1h-1 y-1 Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 8. Daily Rainfall Erosivity databases spatial distribution Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 9. Daily Rainfall Erosivity vs. Rainfall (month by month) Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 10. Trends in rainfall erosivity in relation with NAO, MO & WeMO Study case: Trends in rainfall erosivity at NE Spain during the period 1955-2006 in relation with NAO, MO & WeMO Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 11. Rainfall erosivity trends at annual scale Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 12. Rainfall erosivity trends at seasonal scale Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 13. Rainfall erosivity trends at seasonal scale Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 14. Temporal evolution of rainfall erosivity quintile occurrence Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 15. Rainfall erosivity trends at daily scale; evolution of the occurrence in low (Q1) and high (Q5) events. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 16. Rainfall erosivity trends at daily scale; evolution of the occurrence in low (Q1) and high (Q5) events. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 17. Rainfall erosivity trends at daily scale; evolution of the occurrence in low (Q1) and high (Q5) events. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 18. Rainfall erosivity trends at daily scale; evolution of the occurrence in low (Q1) and high (Q5) events. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 19. Could be these trends related with atmospheric mechanisms and teleconnection patterns evolution? The characteristics of the rainfall erosivity events—Drop Size Distribution, Kinetic Energy, Intensity and Duration—depend on the type of rainfall event (Van Dijk et al. 2002), and in the meteorological and climatic characteristics involved in the generation of the type of rainfall event: The leading atmospheric circulation patterns Sea level pressure fields Temperature contrasts between high and low atmospheric layers Humidity percentage in the air masses Wind flows Geographical factors Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 20. Main atmospheric teleconnection patterns affecting the climate of the study area Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 21. Teleconnection indices circulation patterns in positive (above) and negative (beneath) phase NAO+ MO+ WeMO+ NAO- MO- WeMO- Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 22. Are teleconnection indices phases influencing daily rainfall erosivity? Differences in daily rainfall erosivity, negative and positive atmospheric teleconnection indices phases Positive events = daily indices > 0.5 sd Negative events = daily indices < -0.5 sd Relative difference of average daily rainfall erosivity of negative NAO days with respect to positive NAO days. EIdif = (EINAO- - EINAO+) / EINAO+ where EINAO- (EINAO+) is the average daily rainfall erosivity of negative (positive) NAO days Statistical significance checked by the Wilcoxon-Mann-Whitney test month by month. Significance level α=0.05 Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 23. NAO influencing daily rainfall erosivity Relative difference of average daily rainfall erosivity of negative NAO days with respect to positive NAO days (MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between positive and negative NAO phases. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 24. MO influencing rainfall erosivity Relative difference of average daily rainfall erosivity of negative MO days with respect to positive MO days (MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between positive and negative MO phases. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 25. WeMO influencing rainfall erosivity Relative difference of average daily rainfall erosivity of negative WeMO days with respect to positive WeMO days (MJ mm ha-1 h-1 y-1 (MJ mm ha-1 h-1 y-1)-1). Shaded areas indicate not significant difference between positive and negative WeMO phases. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 26. Analysis of the probability of occurrence of extreme daily rainfall erosivity records associated to negative and positive teleconnection indices phase Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 27. Temporal evolution of atmospheric teleconnection indices and of rainfall erosivity quintile occurrence Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 28. Conclusions Rainfall erosivity has decreased in the NE of the Iberian peninsula during the last 55 years Decrease occurs at annual and seasonal scale, though little increase occurs at summer and autumn Decrease is explained by a reduction in the high and extreme rainfall erosivity events occurrence , whilst low events have increased. This evolution seems related with positive trends in the atmospheric teleconnection indices: NAO, MO and WeMO. Workshop “Non-stationary extreme value modelling in climatology” February 15-17, 2012; Technical University of Liberec
  • 29. Contact details: Estación Experimental Aula Dei – CSIC, Zaragoza (ESPAÑA)/Departamento de Suelo y Agua Marta Angulo-Martinez PhD Student Email: mangulo@eead.csic.es Thanks!